CIOMP makes progress on Infrared dim-small target processing research

Recently, Changchun Institute of Optics Fine Mechanics and Physics, Chinese Academy of Sciences(CIOMP) mainly focuses on the research of super-resolution restoration algorithms of the infrared dim-small target based on POCS. Aiming at solving the super-resolution restoration problem of the infrared dim-small target, the traditional super-resolution restoration algorithm of POCS is optimized. And improved algorithms are proposed which improved the performance. Meanwhile, the algorithms are realized in real-time or near real-time which can be applied in the practical infrared image processing system. The research productions of this project have important practical significance for the development of the infrared dim-small target recognition and tracking. The related research productions are published on the Nature’s sub-journal ‘Scientific Reports’ (DOI: 10.1038/s41598-017-15273-0). Scientific Reports is a comprehensive science journal of Nature Press that concentrates on the research of multidisciplinary intersection. Scientific Reports is Chinese Academy of Sciences SCI journal 1 Zone and SCI influence factor is 5.228. The first author of this article is Chen Jian Research Assistant. The research above is supported by the National natural Science Foundation of China project and Optoelectronic Countermeasure Department Innovative Foundation.

 

With the spring up of the infrared imaging related industry, the infrared imaging technology has become the mainstream development direction of the intelligent photoelectrical detection due to its good concealment, wide detection range, high positioning accuracy, long distant penetration, light weight, little volume, low power dissipation and high solidity. However, the features of the image of infrared dim-small target such as less details and low SNR become the bottleneck of the application of infrared image. How to enhance the imaging effect of the infrared dim-small target becomes the hotspot of the research. Starting from the point of “restoration as foundation”, the theory and technology of the infrared dim-small target super-resolution restoration by utilizing the theory and technology of the super-resolution restoration are explored in this research.

 
This research proposes four improved POCS algorithms and a new evaluation method of the super-resolution restoration. And the effectiveness of the improved algorithms and the evaluation method are evaluated by the infrared dynamic scene simulation system and the infrared image processing system.
The main work and innovation of this research are:
(1) For the noise sensitive problem of the traditional POCS restoration algorithm, the BM3D filtering method with better de-noising effect and the POCS restoration algorithm are combined in this research. We optimize the BM3D method and propose the method of mean pre-screened image block and limiting the number of packet image blocks to reduce the computation of BM3D method. Experimental results show that the proposed POCS based on BM3D can achieve better restoration effect than that of the traditional POCS method when the low resolution image contains noise, furthermore no noise in the high resolution image can be perceived basically.
(2) For the disadvantage of the traditional super-resolution restoration evaluation system only concerning about a particular aspect of the statistical properties of the image, we propose the super-resolution restoration evaluation method based on SSIM_NCCDFT, which combines the gray value and contrast of the spatial domain and the autocorrelation of frequency domain. Therefore, the proposed evaluation method can evaluate the results of the super-resolution restoration in both spatial domain and frequency domain. Experimental results show that the evaluation method can well evaluate the super-resolution restoration results. Furthermore this evaluation method has some significance for super-resolution restoration evaluation.
(3) For the long iteration of the POCS super-resolution restoration algorithm and the shortcomings of incapability to meet the real-time detecting of optical detection system, we propose a fast POCS super-resolution restoration algorithm based on the gradient image, which classifies image pixel according to the gradient of the image, and then uses different iteration factor to calculate. The iteration step is larger when the gradient is bigger and the iteration step is smaller when the gradient is smaller. The improved algorithm can preserve edge information and suppress noise. Therefore, it can guarantee the performance of the super-resolution restoration and greatly reduce the running time. Simultaneously, another fast POCS super-resolution restoration algorithm based on region selection is proposed. The target area is the key point we focus on in the optical detection system, while this area contains only very small number of pixels. Therefore, we use threshold segmentation and combination to acquire the union of all target areas. Then we execute super-resolution restoration only in the union of all target areas. In this way we decrease the huge computation of background restoration and greatly reduce the operation time to achieve real-time or near real-time. So this super-resolution restoration algorithm can be applied in the practical infrared image processing system.

 

(a) Bilinear interpolation restoration                               (b) POCS restoration           (c) Proposed BPOCS restoration

Figure 1. Restoration effect of Lena image.(Photo by CIOMP)

(a) Bilinear interpolation restoration                               (b) POCS restoration             (c) Proposed BPOCS restoration

Figure2. Restoration effect of Barbara image.(Photo by CIOMP)

(a) Bilinear interpolation restoration                               (b) POCS restoration                   (c) Proposed BPOCS restoration

Figure3. Restoration effect of missile image.(Photo by CIOMP)

(a) Bilinear interpolation restoration                               (b) POCS restoration                 (c) Proposed BPOCS restoration

Figure 4. Restoration effect of plane image.(Photo by CIOMP)

 

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