基于自适应梯度先验的旋转模糊图像复原算法
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摘要
弹载红外成像系统随弹体作高速旋转运动时,在曝光时间内获取的图像存在严重旋转模糊问题,给后续的目标识别与图像跟踪造成极大困扰。针对此问题,提出了一种基于自适应梯度先验的旋转模糊图像复原算法。该算法通过使用自适应梯度先验的正则化项,对从图像中沿旋转模糊路径提取的一维向量在频域内进行反卷积运算。同时针对使用Bresenham算法提取像素方法产生的空穴点问题,设计了一种查表决策的自适应中值滤波算法。仿真实验结果表明,相对于改进的维纳滤波、约束最小二乘滤波、梯度加载滤波,该算法能有效地适应低信噪比干扰环境,具有较强的噪声抑制和削弱振铃效应的能力。
When the missile-borne infrared imaging system rotates with missile at a high speed rotation,a serious rotation blurring exists in the image acquired during the exposure,which causes a great confusion for subsequent target recognition and image tracking. A novel rotational blur restoration algorithm based on adaptive gradient priori regularization is proposed. In the proposed algorithm,the adaptive gradient priori regularization term is used for the deconvolution operation of the one-dimensional vectors extracted along the blurred fuzzy path from the image in the frequency domain. An adaptive median filtering algorithm using look-up table is designed to solve the problem about the hole points generated by the pixel extraction method using Bresenham algorithm. The experimentally simulated results show that,compared with improved Wiener filtering,constrained least square filtering,and gradient loading filtering,the proposed algorithm can effectively adapt to low SNR interference environment,and has strong ability to suppress noise and weaken the ringing-effect.
When the missile-borne infrared imaging system rotates with missile at a high speed rotation,a serious rotation blurring exists in the image acquired during the exposure,which causes a great confusion for subsequent target recognition and image tracking. A novel rotational blur restoration algorithm based on adaptive gradient priori regularization is proposed. In the proposed algorithm,the adaptive gradient priori regularization term is used for the deconvolution operation of the one-dimensional vectors extracted along the blurred fuzzy path from the image in the frequency domain. An adaptive median filtering algorithm using look-up table is designed to solve the problem about the hole points generated by the pixel extraction method using Bresenham algorithm. The experimentally simulated results show that,compared with improved Wiener filtering,constrained least square filtering,and gradient loading filtering,the proposed algorithm can effectively adapt to low SNR interference environment,and has strong ability to suppress noise and weaken the ringing-effect.
引文
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[13]邹谋炎.反卷积和信号复原[M].北京:国防工业出版社,2001.ZOU M Y. Deconvolution and signal recovery[M]. Beijing:National Defense Industry Press,2001.(in Chinese)
[14] GASTAL E S L,OLIVEIRA M M. Domain transform for edge-aware image and video processing[J]. ACM Transactions on Graphics(TOG),2011,30(4):1-12.
[2] JOSHI N,KANG S B,ZITNICK C L,et al. Image deblurring using inertial measurement sensors[J]. Acm Transactions on Graphics,2010,29(4):1-9.
[3] GUPTA A,JOSHI N,ZITNICK C L,et al. Single image deblurring using motion density functions[C]∥Ptoceedings of European Conference on Computer Vision. Heraklion, Crete, Greece:Springer,2010:171-184.
[4] HONG H Y,ZHANG T X. Fast restoration approach for rotational motion blurred image based on deconvolution along the blurring paths[J]. Optical Engineering,2003,42(12):3471-3486.
[5] ZHENG Y,LI J F,ZHU Z F. Robust image restoration for rotary motion blur based on frequency analysis[J]. Optical Engineering,2008,47(9):097004.
[6]罗院红,许廷发,朱振福.基于路径提取的旋转模糊图像复原算法[J].兵工学报,2014,35(9):1393-1399.LUO Y H,XU T F,ZHU Z F. Study of rotary motion-blurred image restoration algorithm[J]. Acta Armamentarii,2014,35(9):1393-1399.(in Chinese)
[7] FORTUNATO H E,OLIVEIRA M M. Fast high-quality non-blind deconvolution using sparse adaptive priors[J]. The Visual Computer,2014,30(6):661-671.
[8] JAVARAN T A,HASSANPOUR H,ABOLGHASEMI V. Local motion deblurring using an effective image prior based on both the first-and second-order gradients[J]. Machine Vision and Applications,2017,28(3/4):431-444.
[9] SADA M M,GOYANI M M. Image deblurring techniques-a detail review[J]. International Journal of Scientific Research in Science,Engineering and Technology,2018,4(2):176-188.
[10] SRINIVASAN K S,EBENEZER D. A new fast and efficient decision-based algorithm for removal of high-density impulse noises[J]. IEEE Signal Processing Letters,2007,14(3):189-192.
[11] ESAKKIRAJAN S,VEERAKUMAR T,SUBRAMANYAM A N,et al. Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter[J].IEEE Signal Processing Letters,2011,18(5):287-290.
[12]宋勇,郝群,王涌天.电荷耦合器件积分时间对微型成像系统成像质量的影响[J].兵工学报,2003,24(3):338-341.SONG Y,HAO Q,WANG Y T. Effect of charge coupled device integration time on the imaging quality of a micro imaging system[J]. Acta Armamentarii,2003,24(3):338-341.(in Chinese)
[13]邹谋炎.反卷积和信号复原[M].北京:国防工业出版社,2001.ZOU M Y. Deconvolution and signal recovery[M]. Beijing:National Defense Industry Press,2001.(in Chinese)
[14] GASTAL E S L,OLIVEIRA M M. Domain transform for edge-aware image and video processing[J]. ACM Transactions on Graphics(TOG),2011,30(4):1-12.