【作者】 陈健；

【导师】 高慧斌；

【作者基本信息】 中国科学院研究生院(长春光学精密机械与物理研究所) ， 机械电子工程， 2014， 博士

【摘要】 随着红外成像相关产业的兴起，红外成像技术具有的隐蔽性好、探测范围广、定位精度高、穿透距离远，以及轻质小巧、低耗可靠等优点备受青睐，已成为当前智能化光电探测发展的主流方向。然而，红外弱小目标的图像细节特征少、信噪比低等特点成为红外图像应用的瓶颈，如何提高红外弱小目标成像效果成为目前的研究热点。本文以“复原为本”为研究着眼点，利用超分辨率复原相关理论和技术，研究红外弱小目标超分辨率复原的方法和技术。本文主要围绕基于POCS的红外弱小目标超分辨率复原算法展开研究。针对红外弱小目标超分辨率复原中出现的问题，对传统POCS超分辨率复原算法进行了优化，提出了四种改进算法，提高了复原算法的性能，同时使其达到实时或接近实时，进而可以在实际红外图像处理系统中应用。本文提出了四种改进的POCS算法和一种新的超分辨率复原评价方法，并分别通过基于红外动态场景仿真系统实验和基于红外图像采集及处理系统实验，验证了改进算法和评价方法的有效性。本文的主要工作及创新之处在于：（1）针对传统POCS复原方法对噪声比较敏感的问题，将目前去噪效果较好的BM3D滤波方法和POCS复原方法相结合，对BM3D方法进行了优化，提出了使用图像块的均值预筛选和限制分组图像块数目的方法，降低了BM3D方法的运算量。实验表明基于BM3D的POCS超分辨率复原算法能够在低分辨率图像包含噪声时，取得比传统POCS方法更好的复原效果，复原的高分辨率图像主观上基本看不出噪声。（2）针对传统的超分辨率复原评价体系只关注图像某一方面统计特性的问题，提出了基于SSIM_NCCDFT的超分辨率复原评价方法。该评价方法结合了空间域的灰度均值、对比度以及频域自相关，能够同时评价超分辨率复原结果在空间域的复原效果和对频域信息的复原精度，实验表明该评价方法能够很好的评价超分辨率复原的结果，对超分辨率评价方法具有一定的指导意义。（3）针对POCS超分辨率复原算法迭代时间较长，无法满足光电探测系统实时性的问题，提出了基于梯度图的快速POCS超分辨率复原算法。该算法根据图像的梯度分布对图像中的像素点进行分类，采用不同的迭代系数进行计算。改进算法能够较好的保留边缘信息并抑制噪声，进而在保证超分辨率复原性能的基础上大大缩短了运算时间。同时，提出了另外一种改进算法：基于区域选择的快速POCS超分辨率复原算法。光电探测系统中我们关注的重点是目标区域，而这一区域通常只占很少的像素位置，因此通过阈值分割和合并找到所有目标区域并集，然后仅在这个目标区域并集上进行超分辨率复原。这样，去除了复原背景的巨大运算量，大大缩短了运算时间，使其达到实时或接近实时，进而可以在实际红外图像处理系统中应用。更多还原

【Abstract】 With the spring up of the infrared imaging related industry, the infrared imagingtechnology has become the mainstream development direction of the intelligentphotoelectrical detection due to its good concealment, wide detection range, highpositioning accuracy, long distant penetration, light weight, little volume, low powerdissipation and high solidity. However, the features of the image of infrareddim-small target such as less details and low SNR become the bottleneck of theapplication of infrared image. How to enhance the imaging effect of the infrareddim-small target becomes the hotspot of the research. Starting from the point of“restoration as foundation”, the theory and technology of the infrared dim-smalltarget super-resolution restoration by utilizing the theory and technology of thesuper-resolution restoration are explored in this thesis.This thesis mainly focuses on the research of super-resolution restorationalgorithms of the infrared dim-small target based on POCS. Aiming at solving thesuper-resolution restoration problem of the infrared dim-small target, the traditionalsuper-resolution restoration algorithm of POCS is optimized. And four improvedalgorithms are proposed which improved the performance. Meanwhile, thealgorithms are realized in real-time or near real-time which can be applied in thepractical infrared image processing system. This thesis proposes four improved POCS algorithms and a new evaluationmethod of the super-resolution restoration. And the effectiveness of the improvedalgorithms and the evaluation method are evaluated by the infrared dynamic scenesimulation system and the infrared image processing system.The main work and innovation of this thesis are:(1) For the noise sensitive problem of the traditional POCS restorationalgorithm, the BM3D filtering method with better de-noising effect and the POCSrestoration algorithm are combined in this thesis. We optimize the BM3D methodand propose the method of mean pre-screened image block and limiting the numberof packet image blocks to reduce the computation of BM3D method. Experimentalresults show that the proposed POCS based on BM3D can achieve better restorationeffect than that of the traditional POCS method when the low resolution imagecontains noise, furthermore no noise in the high resolution image can be perceivedbasically.(2) For the disadvantage of the traditional super-resolution restorationevaluation system only concerning about a particular aspect of the statisticalproperties of the image, we propose the super-resolution restoration evaluationmethod based on SSIM_NCCDFT, which combines the gray value and contrast ofthe spatial domain and the autocorrelation of frequency domain. Therefore, theproposed evaluation method can evaluate the results of the super-resolutionrestoration in both spatial domain and frequency domain. Experimtnal results showthat the evaluation method can well evaluate the super-resolution restoration results.Furthermore this evaluation method has some significance for super-resolutionrestoration evaluation(3) For the long iteration of the POCS super-resolution restoration algorithmand the shortcomings of incapability to meet the real-time detecting of opticaldetection system, we propose a fast POCS super-resolution restoration algorithmbased on the gradient image, which classifies image pixel according to the gradientof 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 gradientis smaller. The improved algorithm can preserve edge information and suppressnoise. Therefore, it can guarantee the performance of the super-resolution restorationand greatly reduce the running time. Simultaneously, another fast POCSsuper-resolution restoration algorithm based on region selection is proposed. Thetarget area is the key point we focus on in the optical detection system, while thisarea contains only very small number of pixels. Therefore, we use thresholdsegmentation and combination to acquire the union of all target areas. Then weexecute super-resolution restoration only in the union of all target areas. In this waywe decrease the huge computation of background restoration and greatly reduce theoperation time to achieve real-time or near real-time. So this super-resolutionrestoration algorithm can be applied in the practical infrared image processingsystem.更多还原