基于块效应抑制的压缩降质模糊图像盲复原
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摘要
针对被压缩重构后的模糊图像复原问题,提出基于块效应抑制的图像去模糊方法.采用多尺度分解逐层进行模糊核估计,每一层利用去块效应的模糊图像作为有效显著边缘估计的参考图像,并在模糊核估计优化函数中加入模糊核梯度约束保证其连续平滑性,通过在频域交替迭代优化确定模糊核.最终复原约束项为图像全变分以及L2范数块效应抑制项,该项通过迭代使得复原图像和去块效应图像逐步逼近.实验结果表明,提出的方法能够在较高压缩率的模糊图像中估计出相对准确的模糊核,得到兼顾消除块效应与保留图像细节的复原效果.
An image deblurring algorithm based on block effect suppression was proposed for compressed degraded blur image.Multi-scale decomposition was adopted layer by layer in the kernel estimation stage.The blurred image whose block artifacts were removed was used as the reference image in the effective significant edge estimation in each layer.Then the kernel gradient norm constraint was added in the kernel estimation optimization function to ensure that the kernels shape was continuous and smooth.The kernel was determined by alternately iterative optimization in frequency domain.Final restoration constraints were image total variation and the L2 norm block suppression terms.The L2 term made the restoration image and the block effect eliminated image approach during iteration.Experimental results show that the proposed method can estimate relatively accurate kernel even when the blur image compression rate is high,and can acquire a satisfied restored image with block effect eliminated and image details preserved.
An image deblurring algorithm based on block effect suppression was proposed for compressed degraded blur image.Multi-scale decomposition was adopted layer by layer in the kernel estimation stage.The blurred image whose block artifacts were removed was used as the reference image in the effective significant edge estimation in each layer.Then the kernel gradient norm constraint was added in the kernel estimation optimization function to ensure that the kernels shape was continuous and smooth.The kernel was determined by alternately iterative optimization in frequency domain.Final restoration constraints were image total variation and the L2 norm block suppression terms.The L2 term made the restoration image and the block effect eliminated image approach during iteration.Experimental results show that the proposed method can estimate relatively accurate kernel even when the blur image compression rate is high,and can acquire a satisfied restored image with block effect eliminated and image details preserved.
引文
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[2]SHAN Q,JIA J,AGARWALA A.High-quality motion deblurring from a single image[J].Acm Transactions on Graphics,2008,27(3):15-19.
[3]KRISHNAN D,TAY T,FERGUS R.Blind deconvolution using a normalized sparsity measure[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Providence:IEEE,2011:233-240.
[4]CHO S,LEE S.Fast motion deblurring[J].Acm Transactions on Graphics,2009,28(5):89-97.
[5]JIA J,XU L.Two-phase kernel estimation for robust motion deblurring[C]∥European Conference on Computer Vision.Greece:[s.n.],2010:81-84.
[6]XU L,ZHENG S,JIA J.Unnatural L0 sparse representation for natural image deblurring[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Washington:IEEE,2013:1107-1114.
[7]DONG Wen-de,FENG Hua-jun,XU Zhi-hai,et al.Blind image deconvolution using the Fields of Experts prior[J].Optics Communications,2012,285(24):5051-5061.
[8]LAI Wei-sheng,DING Jian-jiun,LIN Yen-yu,et al.Blur kernel estimation using normalized color-line priors[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:64-72.
[9]ZUO W,REN D,ZHANG D,et al.Learning iterationwise generalized shrinkage-thresholding operators for blind deconvolution[J].IEEE Transactions on Image Processing,2016,25(4):1751-1764.
[10]王国栋,徐洁,潘振宽,等.基于归一化超拉普拉斯先验项的运动模糊图像盲复原[J].光学精密工程,2013,21(5):1340-1348.WANG Guo-dong,XU Jie,PAN Zhen-kuan,et al.Blind image restoration based on normalized hyper laplacian prior term[J].Optics and Precision Engineering,2013,21(5):1340-1348.
[11]ZHANG X,WANG R,TIAN Y,et al.Image deblurring using robust sparsity priors[C]∥IEEE Conference on Image Processing.Quebec:IEEE:2015:138-142.
[12]MICHAELI T,IRANI M.Blind deblurring using internal patch recurrence[J].European Conference on Computer Vision.Zurich:[s.n.],2014:783-798.
[13]FOI A,KATKOVNIK V,EGIAZARIAN K.Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color Images[J].IEEE Transactions on Image Processing,2007,16(5):1395-1411.
[14]LUO Y,WARD R K.Removing the blocking artifacts of block-based DCT compressed images[J].IEEE Transactions on Image Processing,2003,12(7):838-42.
[15]WEERASINGHE C,LIEW W C,YAN H.Artifact reduction in compressed images based on region homogeneity constraints using the projection onto convex sets algorithm[J].IEEE Transactions on Circuits and Systems for Video Technology,2002,12(10):891-897.
[16]LEVIN A,WEISS Y,DURAND F,et al.Understanding and evaluating blind deconvolution algorithms[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Miami:IEEE,2009:1964-1971.
[17]TOMASI C,MANDUCHI R.Bilateral filtering for gray and color images[C]∥IEEE International Conference on Computer Vision.Washington:IEEE,1998:839-846.
[18]OSHER S,RUDIN L I.Feature-oriented image enhancement using shock filters[J].Siam Journal on Numerical Analysis,1990,27(4):919-940.
[19]WANG Z,BOVICK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612.