CN113554552B - Micro-scanning super-resolution method for infrared point target detection - Google Patents
Micro-scanning super-resolution method for infrared point target detection Download PDFInfo
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Abstract
The invention belongs to the field of optical system image processing, and particularly discloses a micro-scanning super-resolution method for infrared point target detection, which comprises the steps that an upper computer sends a super-resolution instruction and sends a set external trigger signal to a miniaturized high-frequency controller; the miniaturized high-frequency controller processes and amplifies signals according to the trigger signals, outputs the signals to the high-precision micro-scanning platform, drives the platform to perform micro-displacement motion, starts integration after the high-precision micro-scanning platform reaches a stable position, so as to obtain low-resolution original image data, transmits the original image data to the image algorithm processor, performs super-resolution image processing and point target extraction on the input low-resolution original image data according to an upper computer instruction, and outputs a processed result to the back-end equipment. The invention uses the down sampling low-fraction sequence obtained by the reconstructed super-fraction image, thereby greatly reducing the clutter introduced by the phase difference and improving the detection distance.
Description
Technical Field
The invention relates to the field of imaging processing of a mobile optical system, in particular to a micro-scanning super-resolution method for infrared point target detection.
Background
Due to the field of view of the infrared imaging system and the detector pixel aperture, the resolution of the infrared target is generally low, often in the form of a point target, and generally occupies only a few pixels. This characteristic of infrared targets makes target detection techniques difficult. First, the far infrared target occupies a small area, the target has almost no characteristic information such as size, shape, texture and the like, and the useful information amount provided for the detection algorithm is very small. Second, the signal-to-noise ratio of the infrared target is low, and the infrared target propagates in the atmosphere and is influenced by the environments such as atmospheric attenuation, rain, snow and the like, so that the strength of the target signal received by the infrared sensor is low. Furthermore, the background information is complex. Atmospheric cloud layers, sea surfaces, trees and the like in the nature can generate larger interference on infrared point source targets, form randomly distributed background noise points, are close to the characteristics of the infrared targets, easily cause false alarms, and bring about small difficulty to target detection.
Infrared point target detection is actually a process of automatically detecting targets in a cluttered background and noise environment by using an image processing algorithm. The existing algorithm can not effectively inhibit background clutter for complex background, so that a target detection result has higher false alarm rate, and therefore a micro-scanning super-resolution method for infrared point target detection is needed.
Disclosure of Invention
The invention aims to provide a micro-scanning super-resolution method for infrared point target detection, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides a micro-scanning super-resolution method for infrared point target detection, which comprises the following steps:
s1, an upper computer sends a super-resolution instruction and sends a set external trigger signal to a miniaturized high-frequency controller;
S2, the miniaturized high-frequency controller processes and amplifies signals according to the trigger signals and outputs the signals to the high-precision micro-scanning platform to drive the platform to perform micro-displacement movement;
s3, after the high-precision micro-scanning platform reaches a stable position, the detector starts integrating, so that a low-resolution image sequence is obtained;
S4, transmitting the original image data to an image algorithm processor, and performing super-resolution image processing and point target extraction on the input low-resolution image sequence by the image algorithm processor according to an upper computer instruction;
And S5, outputting the processed result to the back-end equipment.
Preferably, the super-resolution image processing and point target extraction process in S4 includes the steps of:
S4a, reading a multi-frame low-resolution image sequence Y k with sub-pixel displacement;
s4b, reading an imaging system calibration function H k;
s4c, calculating an image noise level V k;
S4d, calculating displacement between image sequences by adopting an optical flow method motion estimation algorithm to obtain a motion information matrix F k;
S4e, constructing an initial superdivision diagram
S4f, constructing a system matrix W by using the parameters obtained in the steps S4b, S4c and S4 d;
W=d kHkFk where D k is the downsampling operator;
s4g, reversely solving the super-resolution image by utilizing the parameters obtained in the steps S4e and S4f
Yk＝DkHkFkX+Vk,k=1,2,...K
S4h, super-resolution image obtained in step S4gAfter forward degradation is carried out on the system matrix W, an estimated value Y k' of a group of low-component image sequences is obtained;
S4i, performing differential operation on an estimated value Y k' of the low-resolution image sequence and an input low-resolution image sequence Y k to obtain a differential image DIF k;
S4j, calculating the maximum value Max of the low-resolution image sequence, and determining a segmentation threshold value;
S4k, performing image segmentation on the differential image DIF k obtained in the step 4i according to a segmentation threshold T_thres, namely, if the image gray value is higher than the segmentation threshold, the image gray value is regarded as a point target;
S4l, performing secondary screening on the target according to the multi-frame point target position information obtained in the step 4k, and screening out unreasonable point targets;
S4m, marking the point target position;
s4n, repeating the steps S4a-S4m until the processing of all the images is completed.
Compared with the prior art, the invention has the beneficial effects that:
1. The invention introduces an image micro-scanning technology in the detection process of the infrared point target, utilizes a controllable displacement module to generate accurate sub-pixel displacement information, greatly increases the probability of the point target falling into a pixel center area by micro-scanning, and also enhances a target signal.
2. The invention adopts multi-frame image information to well inhibit image noise by adopting the micro-scanning super-resolution algorithm for infrared point target detection, and the micro-scanning super-resolution algorithm for infrared point target detection is introduced into an imaging system for calibration to obtain the physical characteristics of the imaging system, thereby improving the algorithm performance and the algorithm reliability.
3. According to the invention, the physical model relation between the original high-resolution image and the low-resolution image is constructed, the high-resolution image is reversely solved by utilizing the sequence information of the multi-frame low-resolution image with sub-pixel information, and the resolution of an imaging system are improved.
4. The algorithm and the method of the invention utilize the downsampled low-resolution sequence obtained by the reconstructed super-resolution image to carry out differential operation with the input low-resolution image sequence, greatly reduce clutter introduced by phase difference, extract point targets, well reserve the energy of the point targets and improve the detection distance.
Drawings
FIG. 1 is a logical block diagram of the present invention;
fig. 2 is a schematic diagram illustrating an execution flow of a super-resolution image processing procedure in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to FIGS. 1-2, the invention provides a micro-scanning super-resolution method for infrared point target detection, which comprises the following steps:
s1, an upper computer sends a super-resolution instruction and sends a set external trigger signal to a miniaturized high-frequency controller;
S2, the miniaturized high-frequency controller processes and amplifies signals according to the trigger signals and outputs the signals to the high-precision micro-scanning platform to drive the platform to perform micro-displacement movement;
s3, after the high-precision micro-scanning platform reaches a stable position, the detector starts integrating, so that a low-resolution image sequence is obtained;
S4, transmitting the original image data to an image algorithm processor, and performing super-resolution image processing and point target extraction on the input low-resolution image sequence by the image algorithm processor according to an upper computer instruction;
And S5, outputting the processed result to the back-end equipment.
In this embodiment, the super-resolution image processing and point target extraction process in S4 includes the steps of:
S4a, reading a multi-frame low-resolution image sequence Y k with sub-pixel displacement;
s4b, reading an imaging system calibration function H k;
s4c, calculating an image noise level V k;
S4d, calculating displacement between image sequences by adopting an optical flow method motion estimation algorithm to obtain a motion information matrix F k;
S4e, constructing an initial superdivision diagram
S4f, constructing a system matrix W by using the parameters obtained in the steps S4b, S4c and S4 d;
W=d kHkFk where D k is the downsampling operator;
s4g, reversely solving the super-resolution image by utilizing the parameters obtained in the steps S4e and S4f
Yk＝DkHkFkX+Vk,k=1,2,...K
S4h, super-resolution image obtained in step S4gAfter forward degradation is carried out on the system matrix W, an estimated value Y k' of a group of low-component image sequences is obtained;
S4i, performing differential operation on an estimated value Y k' of the low-resolution image sequence and an input low-resolution image sequence Y k to obtain a differential image DIF k;
S4j, calculating the maximum value Max of the low-resolution image sequence, and determining a segmentation threshold value;
S4k, performing image segmentation on the differential image DIF k obtained in the step 4i according to a segmentation threshold T_thres, namely, if the image gray value is higher than the segmentation threshold, the image gray value is regarded as a point target;
s4l, performing secondary screening on the target according to the multi-frame point target position information obtained in the step S4k, and screening out unreasonable point targets;
S4m, marking the point target position;
s4n, repeating the steps S4a-S4m until the processing of all the images is completed.
The invention is actually by moving the lens (or group of lenses) in the optical system such that the image is shifted by a sub-pixel shift in the focal plane. A plurality of original images with sub-pixel displacement relative to each other are acquired. After the processing of the micro-scanning super-resolution algorithm, an infrared point target is detected.
In the embodiment, an image micro-scanning technology is introduced in the detection process of the infrared point target, a controllable displacement module is utilized to generate accurate sub-pixel displacement information, the probability that the point target falls into a pixel center area is greatly increased by micro-scanning, and a target signal is also enhanced.
In the embodiment, the high resolution image is reversely solved by constructing a physical model relation between an original high resolution image and a low resolution image and utilizing multi-frame low resolution image sequence information with sub-pixel information, so that the resolution and the resolution of an imaging system are improved, and the algorithm and the method utilize a downsampled low resolution sequence obtained by the reconstructed super resolution image to carry out differential operation with an input low resolution image sequence, so that clutter caused by phase difference is greatly reduced, point targets can be extracted, point target energy is well reserved, and the detection distance is improved.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (1)
1. The micro-scanning super-resolution method for infrared point target detection is characterized by comprising the following steps of:
s1, an upper computer sends a super-resolution instruction and sends a set external trigger signal to a miniaturized high-frequency controller;
S2, the miniaturized high-frequency controller processes and amplifies signals according to the trigger signals and outputs the signals to the high-precision micro-scanning platform to drive the platform to perform micro-displacement movement;
s3, after the high-precision micro-scanning platform reaches a stable position, the detector starts integrating, so that a low-resolution image sequence is obtained;
S4, transmitting the original image data to an image algorithm processor, and performing super-resolution image processing and point target extraction on the input low-resolution image sequence by the image algorithm processor according to an upper computer instruction;
S5, outputting the processed result to back-end equipment;
The super-resolution image processing and point target extracting process in the S4 comprises the following steps:
S4a, reading a multi-frame low-resolution image sequence Y k with sub-pixel displacement;
s4b, reading an imaging system calibration function H k;
s4c, calculating an image noise level V k;
S4d, calculating displacement between image sequences by adopting an optical flow method motion estimation algorithm to obtain a motion information matrix F k;
S4e, constructing an initial superdivision diagram
S4f, constructing a system matrix W by using the parameters obtained in the steps S4b, S4c and S4 d;
W=d kHkFk where D k is the downsampling operator;
s4g, reversely solving the super-resolution image by utilizing the parameters obtained in the steps S4e and S4f
Yk＝DkHkFkX+Vk,k=1,2,...K
S4h, super-resolution image obtained in step S4gAfter forward degradation is carried out on the system matrix W, an estimated value Y k' of a group of low-component image sequences is obtained;
S4i, performing differential operation on an estimated value Y k' of the low-resolution image sequence and an input low-resolution image sequence Y k to obtain a differential image DIF k;
S4j, calculating the maximum value Max of the low-resolution image sequence, and determining a segmentation threshold value;
The step S4 also comprises the segmentation processing of the differential image and the screening and marking of the target position information, and specifically comprises the following steps:
S4k, performing image segmentation on the differential image DIF k obtained in the step 4i according to a segmentation threshold T_thres, namely, if the image gray value is higher than the segmentation threshold, the image gray value is regarded as a point target;
s4l, performing secondary screening on the target according to the multi-frame point target position information obtained in the step S4k, and screening out unreasonable point targets;
S4m, marking the point target position;
s4n, repeating the steps S4a-S4m until the processing of all the images is completed.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679748A (en) * | 2013-11-18 | 2014-03-26 | 北京空间机电研究所 | Dim point target extraction method and device of infrared remote sensing image |
| CN111338387A (en) * | 2018-12-18 | 2020-06-26 | 上海乂义实业有限公司 | Micro-scanning super-resolution control system and method based on piezoelectric drive |
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| US7602997B2 (en) * | 2005-01-19 | 2009-10-13 | The United States Of America As Represented By The Secretary Of The Army | Method of super-resolving images |
| CN109492543A (en) * | 2018-10-18 | 2019-03-19 | 广州市海林电子科技发展有限公司 | The small target detecting method and system of infrared image |
| CN110400294B (en) * | 2019-07-18 | 2023-02-07 | 湖南宏动光电有限公司 | Infrared target detection system and detection method |
| CN111664944B (en) * | 2020-06-16 | 2022-10-18 | 上海乂义实业有限公司 | Image stabilization, non-uniform correction and super-resolution system based on micro-scanning platform |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679748A (en) * | 2013-11-18 | 2014-03-26 | 北京空间机电研究所 | Dim point target extraction method and device of infrared remote sensing image |
| CN111338387A (en) * | 2018-12-18 | 2020-06-26 | 上海乂义实业有限公司 | Micro-scanning super-resolution control system and method based on piezoelectric drive |
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| Title |
|---|
| "红外显微图像超分辨重建算法研究";陈景杨;《全国优秀硕士学位论文全文数据库》;20150131;第2章 * |
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