JPH1166297A - Image processing apparatus and method - Google Patents

Abstract

(57) [Problem] To reproduce an image based on image data obtained by reading a document in which a halftone area and a character area are mixed, and to reproduce a halftone area faithfully with a simple configuration For the region, a sharpened image is reproduced. SOLUTION: A first parameter extracting unit 104 acquires a parameter based on luminance of input image data.
Then, the enhancement processing determination unit 105 determines whether to perform edge enhancement on the parameter. Further, the second parameter extracting unit 106 acquires a parameter based on the luminance of the image data. Then, the edge amount calculation unit 108 determines whether or not the edge amount obtained by executing the edge enhancement on the obtained parameter is equal to or larger than a predetermined threshold.
As a result of this determination, if the edge amount is equal to or greater than a predetermined threshold,
The edge amount adding unit 109 performs edge enhancement on the input image data.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

[0001] 1. Field of the Invention [0002] The present invention relates to an image processing apparatus and method for processing input image data.

[0002]

2. Description of the Related Art Conventionally, in an image processing apparatus such as a digital copying machine or a digital facsimile, in order to reproduce an original satisfactorily, depending on the type of the original, for example, in the case of a character original, a binarization method using a fixed threshold, a natural image, etc. For halftone originals such as photographs and the like, a binarization method such as a dither method or an error diffusion method (ED method) has been adopted. This switching is generally performed by operating an external key.

However, there is a problem that the operation for determining the type of the original by key operation is troublesome. In the case of a document in which characters and photographs are mixed in a one-page document, a good image cannot be obtained by either one of the processes.

Therefore, several methods (image area separation method) have been proposed in which a character area and a halftone area in a document are automatically discriminated using an image area separation technique, and a processing method is switched according to the type of image. Was.

Among them, as a simple one, a luminance difference of pixel data which is a difference between a maximum value and a minimum value in a reference matrix including a target pixel is extracted as a parameter, and the extracted parameter is extracted as a fixed threshold value. It is to compare with. In some cases, a character area and a halftone area are determined based on the value of a K (black) component obtained after changing luminance data into density data.

[0006]

However, in the case where a character area and a halftone area in a document are discriminated using the luminance difference as a parameter, the input image data is an image composed of three color components of R, G and B. Since the data is data, it is necessary to configure an arithmetic circuit for processing the image data for each color component, and there is a problem that a circuit scale and a processing time increase.

In general, if the luminance difference is small, it is determined as a halftone area, and if it is large, it is determined as a character area. Therefore, a high-density part other than a background (white background), a natural image, or a photograph is also determined as a halftone area. When a density-preserving quantization process is performed on a region including a background portion (white background), a natural image, a photograph, or the like, which is determined as a halftone region, and a high-density portion, granular noise is generated in this region. It can happen.

Therefore, as one of the methods for preventing the generation of noise in this area, it is conceivable to identify a region consisting of a background portion (white background) or a high-density portion and perform threshold processing so that no noise is generated. Can be However, such threshold processing prevents an actual halftone area from being a background area (white background), a natural image, a photograph, or the like, and is determined to be an area composed of high-density parts. However, there is a problem that a halftone area (an area composed of a natural image, a photograph, and the like) cannot be faithfully limited.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and has a simple configuration for reproducing an image based on image data obtained by reading a document in which a halftone area and a character area are mixed. It is an object of the present invention to provide an image processing apparatus and method capable of reproducing a sharpened image of a character area while faithfully reproducing a halftone area.

[0010]

To solve this problem, for example, an image processing apparatus according to the present invention has the following arrangement. That is, an image processing apparatus that processes input image data, wherein a first obtaining unit that obtains a parameter based on luminance of the input image data, and a parameter that is configured by the first obtaining unit. On the other hand, a first determination unit that determines whether or not to perform edge enhancement, a second acquisition unit that acquires a parameter based on the luminance of the input image data, and an image acquired by the second acquisition unit. A second determining means for determining whether or not an edge amount obtained by performing edge enhancement on a parameter is equal to or greater than a predetermined threshold value; and a result of the determination by the second determining means, the edge amount is determined to be a predetermined value. Execution means for executing edge enhancement on the input image data when the threshold value is equal to or more than the threshold value.

[0011]

Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.

<First Embodiment> FIG. 1 is a block diagram showing a configuration of an image processing apparatus according to a first embodiment.

In FIG. 1, reference numeral 101 denotes a scanner for reading a document. The scanner 101 includes a CCD (fixed image sensor) image sensor or a CS (contact sensor). Reference numeral 101a denotes an analog video signal output from the scanner 101.

Reference numeral 102 denotes a pre-processing unit that performs pre-processing on the analog signal 101a. The processing in the pre-processing unit 102 will be described later in detail. 102a is the preprocessing unit 1
02, R quantized to 8 bits per pixel,
G and B multi-valued luminance data.

Reference numeral 103 denotes a buffer for storing luminance data of each of R, G, and B color components;
Is a first parameter extraction unit that extracts parameters necessary to determine whether or not to execute the edge enhancement process from the luminance data of each of the R, G, and B color components stored in the first parameter extraction unit (details will be described later). Reference numeral 105 denotes an emphasis processing determination unit that determines whether to perform the emphasis processing based on the parameters extracted by the first parameter extraction unit 104.

Reference numeral 106 denotes R stored in the buffer 103,
A second parameter extraction unit extracts parameters necessary for executing the edge enhancement process from the luminance data of each of the G and B color components (details will be described later). Reference numeral 107 denotes the second parameter extraction unit 106. This is a buffer for storing parameters necessary for edge enhancement processing.

Reference numeral 108 denotes an edge amount calculation unit that calculates the edge amount of the pixel of interest based on the extracted parameters (details will be described later).

An edge amount adding unit 109 adds the edge amount obtained by the edge amount calculating unit 108 to the target pixel.

Reference numeral 110 denotes a luminance / density conversion unit for converting luminance data into density data, and is constituted by a conversion table such as an LUT. This conversion table is RO
M or RAM. The input luminance data and output density data are 8-bit data for each color component.

Reference numeral 111 denotes a density correction unit for correcting the density data converted by the brightness / density conversion unit 110, and is constituted by a plurality of correction tables such as an LUT and an arithmetic circuit.
Each color component of the density data input here and the density data after the density correction is 8-bit data. This correction table is constituted by a ROM or a RAM.

Reference numeral 112 denotes density data, which is multi-value data, of 2
An error diffusion processing unit that quantizes the value data. A printer 113 records an image based on the binary data from the error diffusion processing unit 112. This printer is configured by a printer of a thermal transfer system, an electrophotographic system using a laser beam, or the like, which is used for a normal recording apparatus, an inkjet system, or the like.

Next, the entire processing content of the image processing apparatus according to the first embodiment will be described with reference to the flowchart of FIG.

First, in step S201, the scanner 10
1 shows a process of reading a document performed in step S1. The analog video signal 101 read by the scanner 101
a is sent to the preprocessing unit 102.

Next, in step S202, the pre-processing unit 10
2 shows the processing executed. In the pre-processing unit 102, the analog video signal 101a is quantized into 8-bit digital image data 102a as shown in FIG. The digital image data 102 a is luminance data of each color component of R, G, and B, and is sent to the buffer 103.

In the next step S202, the parameter extracting section 104, the emphasis processing determining section 105, and the parameter extracting section 1
06, a buffer 107, an edge amount calculating unit 108, and an edge amount adding unit 109. Parameter extraction unit 104, emphasis processing determination unit 1
05, the edge extraction processing of the image data is performed by the processing executed by the parameter extraction unit 106, the buffer 107, the edge amount calculation unit 108, and the edge amount addition unit 109, and the edge-enhanced image data is sent to the luminance / density conversion unit 110. send.

The edge emphasizing process in the present embodiment is performed based on the parameters described later in the image data.
In particular, this is performed for image data indicating a character area.

In step S204, the brightness / density conversion unit 11
0 shows a conversion process executed at 0.

In step S205, the density correction unit 1
10 shows a conversion process executed in step S10. The luminance / density conversion unit 110 performs luminance / density conversion of the luminance data subjected to the edge enhancement processing in step S <b> 203, and the density correction unit 111 performs density correction on the converted density data. The density data after density correction is output to the error diffusion processing unit 112.
Sent to Finally, step S206 shows a binarization process in the error diffusion processing unit 112. The error diffusion processing unit 112 performs a binarization process using an error diffusion method.

Next, the edge emphasizing process (step S203) shown in FIG. 2 of the first embodiment will be described with reference to the flowchart of FIG.

Step S301 shows a process of extracting a parameter for determining whether or not to perform edge enhancement. In the present embodiment, the maximum value and the minimum value are picked up from among the luminance values of R, G, and B (each having 8 bits has already been described).

Next, step S302 is replaced with step S3.
01 is compared with an arbitrary threshold value. Specifically, a comparison is made as to whether the maximum value extracted is smaller than a certain threshold th1 or whether the minimum value extracted is larger than a certain threshold th2. As a result of the comparison, if any of the inequalities is satisfied, the process proceeds to step S303. If none of the inequalities is satisfied, it is determined that the enhancement process is not performed.

Step S303 is a process in the case where it is determined in step S302 that the emphasizing process is to be performed. In step S303, a parameter for calculating an edge amount, in this embodiment, a minimum value of luminance is acquired.

In step S304, an edge amount is calculated using the calculation parameters extracted in step S303. Specifically, a calculation based on Laplacian is performed.

In step S305, it is determined whether the edge amount obtained in step S304 is to be added. More specifically, the threshold value th is compared with the edge amount, and if the threshold value is equal to or smaller than the threshold value, the edge enhancement ends.

In step S306, step S305
When it is determined that the edge amount is to be added, the obtained edge amount is added. Although the processing cannot be performed in parallel by software, in a hardware configuration, the parameter extraction in step S301 and the
It is possible to perform parameter extraction of 3 at the same time,
The other processing is efficiently performed by pipeline processing.

The general operation processing of each component in the embodiment has been described above. The details will be described.

First, the detailed configurations of the scanner 101 and the preprocessing unit 102 will be described with reference to FIG.

FIG. 3 shows a scanner 101 according to the first embodiment.
FIG. 3 is a block diagram showing a detailed configuration of a preprocessing unit 102. In the figure, reference numeral 401 denotes C for controlling the entire apparatus.
Although it is a PU, it may be a single CPU for only the scanner 101 and the preprocessing unit 102.

Reference numeral 402 denotes a driving unit for driving the scanner 403, and reference numeral 404 denotes a document to be read. 405 is a CCD
A sensor 406 is an A / D converter for converting an analog output signal from the CCD sensor 405 into a digital signal. Here, a 10-bit A / D converter is assumed. An image signal correction circuit 407 corrects an input signal based on a digital signal of the A / D converter 406.

Scan control is performed by the CPU 401. The CPU 401 first outputs a signal to the drive system 402 so that the scanner 403 scans the document 404, and thereafter, reflected light from the document 404 receiving light emitted from the light source of the scanner is a detection unit. CCD
Image information of the document 404 is detected by the sensor 405,
After being converted (quantized) into 10-bit RGB digital signals by the A / D converter 406, the input signal is corrected (sensitivity) when an image signal correction circuit converts each color component to an 8-bit output based on this signal. The CPU 401 outputs a signal to the drive system 402 based on this signal, and controls the operation of the scanner. At the time of pre-scanning, scanning is performed using one color signal such as only the G component of RGB. The CCD sensor 405 may be a CS (contact sensor).

Next, the buffer 103 and the parameter extracting unit 1
The detailed configuration and operation of the device 04 will be described with reference to FIG.

FIG. 5 is a block diagram showing a configuration of the buffer 103 and the parameter extracting unit 104 according to the first embodiment.

In the image data stored in the memory 501 in the buffer 103, a parameter extracting circuit 502 (parameter) for extracting the maximum value and the minimum value as parameters from the R, G, and B color components of the target pixel. The parameters are extracted through the extraction unit 104). The extraction of the parameters is performed by comparing the magnitudes of the luminance values of the R, G, and B colors.

Here, an example of the configuration of the parameter extraction circuit 502 will be described with reference to FIG.

FIG. 6 is a block diagram showing an example of the configuration of the parameter extracting circuit 502 according to the first embodiment.

A memory 601 stores luminance data of each color component of the target pixel R, G, B.

Reference numeral 602 denotes a comparator for comparing the magnitudes of the luminance values of the G component 601 and the B component 601c of the luminance data of the target pixel stored in the memory 601. The configuration of the comparator will be described later in detail.

Reference numeral 602a denotes a luminance data component having a larger luminance value when the maximum value is calculated, and a luminance data component having a smaller luminance value when the minimum value is calculated.

Reference numeral 603 denotes a latch circuit, and reference numeral 603a denotes the same luminance data component as the above.

Reference numeral 604 denotes a comparator.
R component 60 of the luminance data of the pixel of interest stored in
1a is compared with the target luminance data component obtained by the comparator 601.

By adopting such a circuit configuration, it is possible to select and extract, as a parameter, a larger luminance when calculating the maximum value and a smaller luminance when calculating the minimum value.

FIG. 7 is a block diagram showing another example of the configuration of the parameter extraction circuit 502 of the first embodiment.

A memory 701 stores luminance data of each color component of the target pixel R, G, B.

A multiplexer 703 outputs either the G component or the B component of the luminance data of each color component of the target pixel stored in the memory 701 in response to a clock input. The same multiplexer 702 first outputs the R component of the luminance data. 704 is a comparator,
The R component is compared with the luminance value of one of the G component and the B component. A latch circuit 705 latches the output of the comparator 704. The output is passed through a multiplexer 702 and compared by a comparator 704 with either the G component or the B component that was not selected previously. As a result, the maximum value and the minimum value of the luminance data of each of the R, G, and B color components can be calculated.

With such a circuit configuration, the parameter extraction circuit 502 can be configured with a smaller circuit configuration than the parameter extraction circuit 502 shown in FIG.

However, the parameter extraction circuit 5 shown in FIG.
02 requires a longer processing time than the parameter extraction circuit 502 shown in FIG. 6, so any one of the circuit configurations shown in FIGS. 6 and 7 may be selected and used according to the purpose.

Here, the configuration of the comparator used in FIGS. 6 and 7 will be described with reference to FIG.

FIG. 8 is a block diagram showing the configuration of the comparator used in FIGS. 6 and 7 of the first embodiment.

Reference numeral 801 denotes a comparator having the number of bits corresponding to the input luminance data, and reference numeral 801a denotes an output signal. A selector 802 selects two pieces of luminance data (data a and data b) to be compared based on an output signal of the comparator 801. This comparator is also used in FIGS. 9, 11, 17, and 23 described later.

Next, the configuration of the emphasis processing determination unit 105 will be described with reference to FIG.

FIG. 9 shows an emphasis processing judging section 1 according to the first embodiment.
FIG. 5 is a circuit configuration diagram of the embodiment of FIG.

A comparator 901 compares the maximum value of the luminance signal obtained by the parameter extraction circuit 104 with an arbitrary threshold value th1. Here, the maximum value of the luminance signal is equal to the threshold value t.
If it is larger than h1, a signal indicating that edge enhancement processing is to be performed is output.

A comparator 902 compares the minimum value of the luminance signal obtained by the parameter extracting circuit 104 with an arbitrary threshold value th2. Here, the minimum value of the luminance signal is a threshold value t.
If it is larger than h2, a outputs a signal indicating that edge enhancement processing is to be performed.

The two signals obtained here pass through an OR gate, that is, when one of them is subjected to the emphasis processing, the signals are sent to the edge amount adding section 109, and the edge amount calculated by the edge amount calculating section 108 is obtained. Is used to determine whether to add.

Next, the parameter extraction unit 106 and the buffer 1
07 and its operation will be described with reference to FIG.

FIG. 10 is a block diagram showing the configuration of the parameter extraction unit 106 and the buffer 107 according to the first embodiment.

A parameter extraction circuit 1001 for extracting, as a parameter, a maximum value, a minimum value, or an intermediate value from among the R, G, and B color components of the pixel of interest among the pixels of the image data stored in the buffer 103. The parameters are extracted via the (parameter extracting unit 106).

The extracted parameters are stored in the memory (buffer 106) 1002.

Here, an example of the configuration of the parameter extraction circuit 1001 will be described with reference to FIG.

FIG. 11 is a block diagram showing an example of the configuration of the parameter extraction circuit 1001 of the first embodiment.

Reference numeral 1101 denotes a memory that stores luminance data of each of the R, G, and B color components of the target pixel.

Reference numeral 1102 denotes a G component 1101b and a B component 1 of the luminance data of the pixel of interest stored in the memory 1101.
From 101c, a comparator for comparing the magnitudes of these luminance values.

Reference numeral 1102a denotes the luminance data component having the larger luminance value when the maximum value is calculated, and the luminance data component having the smaller luminance value when the minimum value is calculated.

Reference numeral 1103 denotes a latch circuit;
Is a component of luminance data of the same purpose as above.

Reference numeral 1104 denotes a comparator.
The R component 1101a of the luminance data of the pixel of interest stored in 101 is compared with the component of the target luminance data obtained by the comparator 1102.

With such a circuit configuration, it is possible to select and extract a component of luminance data which is larger when calculating the maximum value and which is smaller when calculating the minimum value as a parameter. The circuit configuration is the same as in FIG.

Next, an edge emphasis filter used in the edge amount calculator 108 will be described with reference to FIG.

FIG. 12 is a diagram showing an edge emphasis filter used in the edge amount calculation unit 108 according to the first embodiment.

The Laplacian filter, which is an edge emphasizing filter used in the edge amount calculation unit 108, is for sharpening an image. For the purpose of improving the resolution in the main scanning direction, a Laplacian filter is used for a target pixel of image data. It has a 3 × 3 matrix shape corresponding to a second-order differentiation operation. Then, in the edge amount calculation unit 108, the edge amount is extracted by the parameter extraction unit 106 of the target pixel A, and
The value obtained by subtracting each parameter W value of a, b, c, and d from four times the parameter value buffered in step 7 is obtained.

That is, the edge amount of the target pixel is obtained as 4 × A− (a + b + c + d). Here, A indicates a parameter of the target pixel selected by the parameter extracting unit 106, and a to d are parameters selected by the parameter extracting unit 106 at each pixel position at the target pixel position.

Further, after multiplying the product with the edge emphasis gain, it is added to each color component of the target pixel. In addition, in order to suppress noise enhancement, an operation is performed in which the edge amount is not added to the target pixel when the edge amount is equal to or less than an arbitrary threshold.

By such processing, edge emphasis is performed particularly on image data indicating a character area.

The detailed configuration of the edge amount adder 108 will be described with reference to FIG.

FIG. 13 is a block diagram showing a detailed configuration of the edge amount adding unit 108 according to the first embodiment.

The edge amount calculated by the edge amount calculation unit 108 is input to the selector 1301. Here, the other input data is 0. The select signal is a signal for determining whether or not the edge amount is below a certain threshold value.
In the case 301a, the calculated edge amount is obtained when the determination result is large, and the edge amount is 0 (that is, the edge amount is not added) when the result is small.

Next, the selector 1302 selects the selector 130
The edge amount output at step 1 and the edge amount “0” are selected. The select signal is output by the enhancement processing determination unit 105. If the enhancement processing determination unit 105 selects to perform enhancement processing, the output signal of the selector 1301 is output. Otherwise, the edge amount 0 is output. Output as 1302a.

The finally obtained edge amount is added to the adder 13.
03, 1304, and 1305 are added to R, G, and B pixels, respectively.

Next, the detailed configurations and operations of the luminance / density conversion unit 110 and the density correction unit 111 will be described with reference to FIG.

FIG. 14 shows the density converter 11 according to the first embodiment.
FIG. 2 is a block diagram showing a detailed configuration of a density correction unit 111 and 0.

The luminance data of each of the R, G, and B color components to which the edge amount has been added is converted into density data 1 by a log converter 1401.
It is converted to 401a. The log conversion unit 1401 calculates RO
It is configured by an LUT configured by M or RAM. Here, the luminance data of each color component of R, G, and B is converted into density data 1401a of Y, M, and C.

The black generation unit 1402 calculates the density data 1402a having the smallest density value from the density data 1401a of each of the Y, M, and C components, and calculates the density data 1402a.
Send to 03.

The UCR processing unit 1403 calculates the K (black) component, and further removes the K component, which is an undercolor, from each of the Y, M, and C components. The Y, M, and C components from which the K component has been removed and the K component 1403a are sent to a masking processing unit 1404. The masking processing unit 1404 performs density correction according to the characteristics of the output destination such as a printer.

The density data 1 obtained by the above processing
The binarization process in the error diffusion processing unit 112 described below is performed on the 404a.

FIG. 15 shows a configuration in which the masking processing section 1404 of FIG.

The mapping processing section 1505 realizes the same effect as the processing executed by the masking processing section 1404 by reference processing using an LUT in which upper and lower bits are separated. Note that processing performed before the mapping processing unit 1504 is the same as that in FIG.

Next, the detailed configuration of the log converter 1401 will be described with reference to FIG.

FIG. 16 shows a log converter 1 according to the first embodiment.
FIG. 3 is a block diagram showing a detailed configuration of a reference numeral 401.

As shown in FIG. 16, the log converter 140
In step 1, 8-bit density data (D0 to D7) is output in response to input of 8-bit luminance data (A0 to A7). Then, for each of the R, G, and B color components, the LUT in the figure is used.
Is required, and the LUT is constituted by a ROM or a RAM.

Next, the detailed configuration of the black generator 1402 will be described with reference to FIG.

The illustration shows the black generator 1402 of the first embodiment.
FIG. 3 is a block diagram showing a detailed configuration of FIG. Reference numeral 1701 denotes a comparator for comparing the magnitudes of the density data M and C,
Output a small value. Reference numeral 1702 denotes a latch circuit which compares the smaller of the latched density data M and C with Y by a comparator 1703. As a result, the component having the minimum value (K component) among the components of the density data Y, M, and C can be obtained.

Next, the detailed configuration of UCR processing section 1403 will be described with reference to FIG.

FIG. 18 shows the UCR processing unit 1 according to the first embodiment.
FIG. 403 is a block diagram illustrating a detailed configuration of the 403.

Reference numeral 1801 denotes a UCR component generation unit for obtaining a K component from a component having the minimum value among the components of the density data Y, M, and C. The obtained K component is used as density data Y, M, C
Subtractors 1302 to 1304 perform subtraction processing on each component of Y ′, M ′, C ′, and K ′.
Are determined.

Next, the detailed configuration of mapping processing section 1504 will be described with reference to FIG.

FIG. 19 is a block diagram showing a detailed configuration of the mapping processing unit 1504 according to the first embodiment.

Y ′ obtained by UCR processing section 1403,
This circuit performs density correction in accordance with the characteristics of the output destination from each of the components M ', C', and K. The upper 4 bits and lower 4 bits of each color are provided.
The bits are separated, each is converted by an LUT, and the final density data is obtained from the result.

Next, a detailed configuration of the error diffusion processing section 112 will be described with reference to FIG.

FIG. 20 is a block diagram showing a detailed configuration of the error diffusion processing section 112 according to the first embodiment.

The data Xi. j (density data) is the error data E * i.j from the adder 2015 generated when the binarization processing has already been performed. j and an adder 2001. The data Di. corrected by the error. j is represented by the following equation.

Di, j = Xi, j + Ei, j This Di, j is a threshold value (T
= 128) and is binarized. That is, the binarized output Yi.j
Is expressed as follows.

When Di, j ≧ T, Yi, j = 255 When Di, j <T, Yi, j = 0 On the other hand, Di, j obtained from the adder 2001 is sent to the error diffusion calculator 2007. The error calculator 2007 calculates an error Ei, j distributed to local pixels based on Di, j and the binarized output Yi, j.
Is calculated. That is, Ei, j can be expressed as follows.

Ei. j = Di, j-Yi, j This E (i, j) is sent to the error distribution value calculation circuit 2008, and the error distribution value calculation circuit 2008
The amount of error to be distributed to pixels is calculated.

Here, the weight matrix in the error diffusion processing will be described with reference to FIG. FIG. 1 is a diagram showing a weight matrix in the error diffusion processing according to the first embodiment.

This matrix shows the positions and ratios of the pixels to which the error Ei, j generated in the target pixel X is distributed.
Then, the error distribution value calculation circuit 2008 determines the error amounts Ai, j and Bi, j as follows.

Ai, j = 2 × Int (Ei, j × 1/6) Bi, j = Int (Ei, j × 1/6) where Int (x) is a function that returns an integer part of x. . That is, the error distribution value calculation circuit 2008 has a configuration in which the fractional part is rounded down, and can execute only the integer calculation. The error Ei, j generated at the pixel of interest by truncating the decimal point and the error distribution value calculation circuit 200
Error amounts Ai, j, B distributed to the four surrounding pixels calculated in 8
A remainder Ri, j is generated between i and j. This is expressed by the following equation.

Ri, j = Ei, j−2 × (Ai, j + Bi, j) This remainder Ri, j is sent to the latch 2009, delayed by one pixel, and added to the input data Xi + 1, j of the next pixel. Is added.

On the other hand, the error Ai, j is sent to the adder 2013 for distribution to the pixel (i + 1, j) and to the adder 2004 for distribution to the pixel (i, j + 1). Also, the error B
i, j is a latch 2 for distributing to pixel (i + 1, j + 1).
003 and the pixel (i−1, j + 1) are sent to the adder 2006 respectively. The memory 2011 is j + 1
A memory for storing an error distributed to the line can store error data of pixels of at least one line. The timing generation circuit 2010 includes the latch circuit 200
9, 2003, 2005, 2012, 2014, and various signals such as an address signal to the memory 2011 are generated.

When the above processing is completed for one line and the processing for the next line is shifted, the error generated in the previous line is read from the memory. The error read from the memory is added to the error generated one pixel before by an adder 2013, and the result is latched.
014. The reading of the error from the line memory 2011 is controlled by the timing generation circuit 2015 so as to correspond to the previous line.

The timing generation circuit 2015 controls to read the address of Mi-3 in the memory 2011 if the target pixel is Xi. Here, Mi-3 is the i of the memory in which the sum of the error values generated up to Mi-3 is written.
-3 is shown.

By performing the processing described above on all the input image data, the error diffusion method 2
Value conversion can be performed. The error diffusion processing unit 112
Since the image data binarized by the above is image data including image data in which the character area is edge-enhanced by the above-described edge enhancement processing, the error diffusion processing unit 112
The image data obtained by binarizing the image data is image data in which the halftone area is faithfully reproduced and the character area is sharpened.

By performing recording using the image data by the printer 113, an image in which the halftone area is faithfully reproduced and the character area is sharpened can be formed.

As described above, according to the first embodiment, when image data obtained from a document in which a read character area and a halftone area are mixed is reproduced as an image, the image data is particularly processed. Control is performed so that edge enhancement processing of image data indicating a character area is executed. This makes it possible to generate image data in which the halftone region is faithfully reproduced and the character region is sharpened without performing the quantization process corresponding to each region.

Further, since the quantization processing is single, not only the selection operation by an external key for switching the quantization processing corresponding to each area for each area is not required, but also the quantization processing corresponding to each area is not required. It is possible to prevent image quality deterioration such as image discontinuity caused by switching for each area.

Further, since it is not necessary to perform a threshold process for distinguishing a region consisting of a high density portion such as a background portion (white background), a natural image, or a photograph from a halftone region, the granularity noise of an image to be formed is reduced. It can be suppressed, and noise is suppressed.

The processing in the luminance / density conversion unit 110 and the density correction unit 111 is performed using a table such as an LUT, and a single parameter is used as a parameter for determining whether or not to execute the edge enhancement processing. Since the configuration is performed, simplification of the circuit configuration, reduction in circuit scale and size, and increase in processing speed can be realized.

In the first embodiment, input data is handled as luminance data. However, even when density data is input, the same effect can be obtained only by eliminating the need for the configuration of the luminance / density conversion unit 110 and the like. Needless to say.

In the first embodiment, the density preserving type error diffusion method (E
D method) is described as an example, but other methods such as the average error minimization method equivalent to the error diffusion method and the average density
Good for halftone images such as dithering as well as binarization
It is also possible to apply a binarization method that can be binarized.

<Second Embodiment> In the first embodiment,
As parameters extracted by the parameter extraction unit 106,
Although the configuration using any of the maximum value, the minimum value, and the intermediate value among the luminance data of the R, G, and B color components has been described, the present invention is not limited to this.

For example, the arithmetic mean of the maximum value and the minimum value of the luminance data of each of the R, G, and B color components is used as a parameter to be extracted by the parameter extracting unit 106 as described in the second embodiment. It may be configured.

Note that the parameter extracting unit 1 of the second embodiment
The processing other than the processing performed in step 06 is the same as that of the first embodiment, and the description of that part will be omitted.

First, the processing executed by the parameter extracting unit 106 according to the second embodiment will be described with reference to the flowchart in FIG. Note that the parameter extraction unit may follow the processing shown in the figure, so that hardware or software may be used as means for realizing it.

First, in step S2201, R,
The maximum value and the minimum value are extracted from the luminance data of each of the G and B color components. Next, the process proceeds to step S2202, and an arithmetic mean of the maximum value and the minimum value obtained in step S2201 is obtained.

Next, the configuration of a parameter extraction circuit constituting the parameter extraction unit 106 of the second embodiment will be described.
This will be described with reference to FIG.

FIG. 23 is a block diagram showing the configuration of the parameter extraction circuit according to the second embodiment. In the figure, 2301
Reference numeral 230 denotes a memory in which luminance data of each of the R, G, and B color components is stored. Reference numeral 2302 denotes a G component 230 out of the luminance data of each color component of the pixel of interest stored in the memory 2301.
This is a comparator for comparing the magnitudes of the 1b and B components 2301c. 2302a and 2302b are luminance data of the larger and the smaller, respectively. 2303 and 2304
Is a latch circuit, and 2303a and 2304a are larger luminance data and smaller luminance data, respectively. Reference numeral 2305 denotes a comparator for comparing the larger luminance data 2305a with the R component 2301a stored in the memory 2301. This is a circuit that selects the larger one, that is, selects the one with the largest luminance data among the luminance data of the R, G, and B color components as the maximum value.

Reference numeral 2306 denotes the smaller luminance data 2304
a and the R component 2301a stored in the memory 2301
Is a circuit that selects the smaller one, that is, selects the one having the smallest luminance data among the luminance data of the R, G, and B color components as the minimum value. Reference numerals 2307 and 2308 denote latch circuits, and reference numerals 2307a and 2308a denote luminance data of the maximum value and the minimum value of the three components, respectively. Reference numeral 2309 denotes an adder for adding the maximum value and the minimum value.
a is a value shifted to the right by 1 bit, that is, an arithmetic average is output.

<Third Embodiment> As a parameter to be extracted by the parameter extracting unit 106, an average value of luminance data of each of R, G, and B color components as in the third embodiment described below is used as a parameter. It may be configured.

The parameter extracting unit 1 of the third embodiment
Processing other than the processing executed in step 06 is the same as in the first embodiment, and a description thereof will be omitted.

First, the processing executed by the parameter extracting unit 104 according to the third embodiment will be described with reference to the flowchart in FIG.

First, in step S2401, any one of the luminance data of each of the R, G, and B color components is selected. Next, the process proceeds to step S2402, and step S2401
A process is performed in which the sum of the luminance data selected in step 4 and the luminance data of each of the R, G, and B color components is calculated and divided by 4.

Next, the configuration of a parameter extracting circuit constituting the parameter extracting section 106 of the third embodiment will be described with reference to FIG.

FIG. 25 is a block diagram showing a configuration of a parameter extraction circuit according to the third embodiment. In the figure, 2501
Reference numeral 2502 denotes a memory storing luminance data of each of the R, G, and B color components. Reference numeral 2502 denotes an R component of the luminance data of each color component of the pixel of interest stored in the memory 2501.
1a and an adder for adding the G component 2501b. 2
502a is the value added. Reference numeral 2503 denotes a selector for selecting any one of the luminance data of each of the R, G, and B color components, and the selected luminance data is 2503a. 2505, R stored in the memory 2501;
B component 2501c of the luminance data of each color component of G and B
And the luminance data 2503a selected by the selector 2503. 2505a is an added value. Reference numerals 2504 and 2506 denote latch circuits.
a and 2506a are values added by 2502 and 2505, respectively. An adder 2507 adds the values of 2504a and 2506a, and the output 2507a is 2b.
It is a value shifted to the right of it, that is, a value used as a pseudo average value.

With the above configuration, the same operation and effect as those of the first embodiment can be obtained.

Even if the present invention is applied to a system including a plurality of devices (for example, a host computer, a scanner, a printer, etc.), the present invention can be applied to an apparatus (for example, a copying machine, a facsimile machine, etc.) including one device. May be applied.

Further, an object of the present invention is to supply a storage medium storing a program code of software for realizing the functions of the above-described embodiments to a system or an apparatus, and to provide a computer (or CPU) of the system or apparatus.
And MPU) read and execute the program code stored in the storage medium.

In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiment, and the storage medium storing the program code constitutes the present invention.

As a storage medium for supplying the program code, for example, a floppy disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD
-R, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used.

When the computer executes the readout program code, not only the functions of the above-described embodiment are realized, but also the OS (Operating System) running on the computer based on the instruction of the program code. ) May perform some or all of the actual processing, and the processing may realize the functions of the above-described embodiments.

Further, after the program code read from the storage medium is written into a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer, based on the instruction of the program code, It goes without saying that the CPU included in the function expansion board or the function expansion unit performs part or all of the actual processing, and the processing realizes the functions of the above-described embodiments.

When the present invention is applied to the storage medium, the storage medium stores program codes corresponding to the above-described flowcharts. Will be stored in the storage medium.

That is, an "acquisition module" for acquiring a parameter based on the luminance of input image data, and determining whether or not an edge amount obtained by executing edge enhancement on the acquired parameter is equal to or larger than a predetermined threshold value. A determination module for determining whether the edge amount is equal to or greater than a predetermined threshold, and a program code for each module of an "execution module" for performing edge enhancement on the input image data. May be stored.

Although the embodiment has been described mainly on the assumption that the input image data is RGB luminance data, the input image data may be density data such as YMC. In this case, the brightness / density conversion unit 110 becomes unnecessary.

[0152]

As described above, according to the present invention, when an image is reproduced based on image data obtained by reading a document in which a halftone area and a character area are mixed, the halftone area can be simply constructed. Can be reproduced with respect to the character area while faithfully reproducing.

[0153]

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating a configuration of an image processing apparatus according to a first embodiment.

FIG. 2 is a flowchart illustrating an overall processing procedure in the embodiment.

FIG. 3 illustrates a scanner 101 and a preprocessing unit 1 according to the first embodiment.
FIG. 2 is a block diagram showing a detailed configuration of the second embodiment.

FIG. 4 is a detailed configuration diagram of a scanner 101 and a preprocessing unit 102 according to the first embodiment.

FIG. 5 is a block diagram illustrating a configuration of a buffer 103 and a parameter extraction unit 104 according to the first embodiment.

FIG. 6 is a block diagram illustrating a configuration example of a parameter extraction circuit 502 according to the first embodiment.

FIG. 7 is a block diagram illustrating another configuration example of the parameter extraction circuit 502 according to the first embodiment.

FIG. 8 is a block diagram showing a configuration of a comparator used in FIGS. 6 and 7 of the first embodiment.

FIG. 9 is a circuit configuration diagram of an emphasis processing determination unit 105 according to the first embodiment.

FIG. 10 is a block diagram showing a configuration of a parameter extraction unit 106 and a buffer 107 according to the first embodiment.

FIG. 11 is a parameter extraction circuit 100 according to the first embodiment;
FIG. 1 is a block diagram showing one configuration example.

FIG. 12 is a diagram illustrating an edge emphasis filter used in the edge amount calculation unit according to the first embodiment.

FIG. 13 is a block diagram illustrating a detailed configuration of an edge amount adding unit according to the first embodiment.

FIG. 14 is a block diagram illustrating a detailed configuration of a density conversion unit 110 and a density correction unit 111 according to the first embodiment.

15 is a diagram in which the masking processing unit 1404 in FIG. 10 is configured by a mapping processing unit 1505.

FIG. 16 is a block diagram illustrating a detailed configuration of a log converter 1401 according to the first embodiment.

FIG. 17 is a detailed configuration block diagram of a black generation unit 1402.

FIG. 18 is a block diagram illustrating a detailed configuration of a UCR processing unit 1403 according to the first embodiment.

FIG. 19 is a mapping processing unit 1504 according to the first embodiment.
FIG. 3 is a block diagram showing a detailed configuration of FIG.

FIG. 20 is a block diagram illustrating a detailed configuration of an error diffusion processing unit 112 according to the first embodiment.

FIG. 21 is a diagram illustrating a weight matrix in the error diffusion processing according to the first embodiment.

FIG. 22 is a parameter extraction unit 1 according to the second embodiment.
It is a flowchart which shows the process content of 06.

FIG. 23 is a block diagram illustrating a configuration of a parameter extraction circuit according to the second embodiment.

FIG. 24 is a parameter extraction unit 1 according to the third embodiment.
14 is a flowchart showing the processing contents of Step 04.

FIG. 25 is a block diagram illustrating a configuration of a parameter extraction circuit according to a third embodiment.

FIG. 26 is a diagram showing a memory map of a storage medium when the processing of the embodiment is realized by a program.

Claims (25)

[Claims] 1. An image processing apparatus for processing input image data, comprising: first obtaining means for obtaining a parameter based on luminance of the input image data; and said first obtaining means. First determining means for determining whether or not to perform edge enhancement on a parameter to be obtained, second obtaining means for obtaining a parameter based on luminance of the input image data, and second obtaining means A second determining means for determining whether or not an edge amount obtained by performing edge enhancement on the parameter obtained in step 2 is equal to or greater than a predetermined threshold; and a result of the determination by the second determining means, An image processing apparatus comprising: an execution unit configured to execute edge enhancement on the input image data when the amount is equal to or more than a predetermined threshold. 2. The method according to claim 1, wherein the first acquisition unit acquires, as parameters, a maximum luminance value and a minimum luminance value among R, G, and B luminance values, which are color components of image data. The image processing apparatus according to claim 1. 3. The method according to claim 1, wherein the first acquisition unit acquires, as parameters, a maximum density value and a minimum density value among Y, M, and C density values, which are color components of the image data. The image processing apparatus according to claim 1. 4. The method according to claim 1, wherein the first determining unit determines whether to perform edge enhancement based on a result of comparing arbitrary different threshold values th1 and th2 with parameters obtained by the first obtaining unit. The image processing apparatus according to claim 1, wherein: 5. The image processing apparatus according to claim 1, wherein the second acquisition unit acquires, as a parameter, a maximum luminance value among R, G, and B luminance values which are color components of the image data. The image processing apparatus according to any one of the preceding claims. 6. The apparatus according to claim 1, wherein said second acquisition means acquires a maximum density value among Y, M, and C density values, which are color components of image data, as a parameter. The image processing apparatus according to any one of the preceding claims. 7. The method according to claim 1, wherein the second acquisition unit acquires, as a parameter, a minimum luminance value among R, G, and B luminance values which are color components of image data. The image processing apparatus according to any one of the preceding claims. 8. The apparatus according to claim 1, wherein said second acquisition means acquires, as a parameter, a minimum density value among Y, M, and C density values, which are color components of image data. The image processing apparatus according to any one of the preceding claims. 9. The method according to claim 1, wherein the second acquisition unit acquires, as a parameter, an intermediate luminance value among R, G, and B luminance values that are color components of image data. The image processing apparatus according to any one of the preceding claims. 10. The method according to claim 1, wherein the second acquisition unit acquires, as a parameter, an arithmetic mean of maximum and minimum values among R, G, and B luminance values, which are color components of image data. The image processing device according to claim 1. 11. The method according to claim 11, wherein the second acquisition unit acquires an arithmetic mean of the maximum value and the minimum value among the densities of Y, M, and C, which are color components of the image data, as a parameter. Item 2. The image processing device according to Item 1. 12. The second obtaining means includes calculating means for calculating an average value of luminance values based on R, G, and B luminance values which are color components of image data, and wherein the calculated luminance is calculated. The image processing apparatus according to claim 1, wherein an average value is obtained as a parameter. 13. The image processing apparatus according to claim 1, wherein the second obtaining unit includes a calculating unit that calculates an average value of the density values based on Y, M, and C density values that are color components of the image data. The image processing apparatus according to claim 1, wherein an average value is obtained as a parameter. 14. The calculating means calculates a sum of luminance values of R, G, and B, which are color components of the image data, and a luminance value of an arbitrary color component among the color components, and calculates a luminance value from the sum. The image processing apparatus according to claim 12, wherein an average value is calculated. 15. The calculating means calculates a sum of density values of R, G, and B, which are color components of the image data, and a density value of an arbitrary color component among the color components, and calculates a density value from the sum. The image processing apparatus according to claim 13, wherein an average value is calculated. 16. The image according to claim 1, wherein the edge amount is an operation value obtained by operation of a parameter obtained by the second obtaining unit by a Laplacian filter composed of a matrix. Processing equipment. 17. The image processing apparatus according to claim 16, wherein said second obtaining means obtains a parameter based on a luminance value of a pixel corresponding to a center position of the Laplacian filter including the matrix. apparatus. 18. The image processing apparatus according to claim 16, wherein said second obtaining means obtains a parameter based on a density value of a pixel corresponding to a center position of the Laplacian filter including the matrix. apparatus. 19. The edge emphasis executed by the execution means, wherein the calculated value is added to a luminance value of a pixel corresponding to a center position of the Laplacian filter composed of the matrix. Image processing device. 20. The method according to claim 18, wherein the edge enhancement performed by the execution unit adds the calculated value to a density value of a pixel corresponding to a center position of the Laplacian filter including the matrix. Image processing device. 21. The second obtaining means obtains each parameter based on a luminance value of each pixel corresponding to a position of each matrix component of the Laplacian filter including the matrix, and obtains each parameter by obtaining the parameter. 11. The image processing apparatus according to claim 10, wherein a line buffer for storing image data is provided at least equal to or greater than the number of rows of the matrix minus one. 22. The edge emphasis executed by the execution means, wherein the operation value is added to a luminance value of each pixel corresponding to a position of each matrix component of the Laplacian filter composed of the matrix. An image processing apparatus according to claim 1. 23. The edge emphasis executed by the execution means, wherein the operation value is added to a density value of each pixel corresponding to a position of each matrix component of the Laplacian filter composed of the matrix. An image processing apparatus according to claim 1. 24. An image processing method for processing input image data, comprising: a first obtaining step of obtaining parameters based on luminance of input image data; and a first obtaining step. A first determining step of determining whether to perform edge enhancement on a parameter to be performed, a second obtaining step of obtaining a parameter based on luminance of the input image data, and the second obtaining step A second determination step of determining whether an edge amount obtained by performing edge enhancement on the parameter obtained in step 2 is equal to or greater than a predetermined threshold value; and a result of the determination in the second determination step Performing an edge emphasis on the input image data when the amount is equal to or greater than a predetermined threshold value. 25. A computer readable memory storing image processing program code, the program code of a first obtaining step of obtaining a parameter based on luminance of input image data, and the first obtaining step. A program code for a first determination step for determining whether to perform edge enhancement on a parameter obtained in the step, and a second acquisition step for obtaining a parameter based on luminance of the input image data. A program code, and a program code of a determining step of determining whether an edge amount obtained by performing edge enhancement on the parameter obtained in the second obtaining step is equal to or more than a predetermined threshold value, When the edge amount is equal to or greater than a predetermined threshold as a result of the determination step, edge enhancement is performed on the input image data. A computer-readable memory characterized by comprising a program code that executes steps.