加权结构组稀疏表示的图像压缩感知重构

doi:10.11959/j.issn.1000-436x.2017041

Abstract

Abstract:

Non-local similarity prior has been widely paid attention to efficiently improve image recovery quality.To fur-ther improve the recovered image quality for compressive sensing (CS),an image compressive sensing recovery method based on reweighted structure group sparse representation (WSGSR) was proposed. $l_{1}$ -norm of WSGSR of image non-local similar patch group was used as a regularization term to optimize reconstruction,which achieved well reserving image high-frequency detail with less loss of image low-frequency component,and thus considerably improve the recon-structed image quality.A reweighted soft thresholding shrinkage method was deduced to achieve optimization solution,in which the significant coefficient with large magnitude value was shrunk by a small threshold,while the non-significant coefficient with small magnitude value was shrunk by a relative large threshold.Experimental results comparison demon-strate the effectiveness of the proposed method.

Key words: compressive sensing, image reconstruction, weighted structure group sparse representation, weighted soft thresholding shrinkage

CLC Number:

TN911.72

Jia LI,Zhi-rong GAO,Cheng-yi XIONG,Cheng ZHOU. Image compressive sensing recovery based on weighted structure group sparse representation[J]. Journal on Communications, 2017, 38(2): 196-202.

Figures/Tables 5

测试图像	测量率			PSNR(dB)/SSIM
测试图像	测量率	MH	RCoS	SGSR	RM	WSGSR
House	0.10.20.30.4	30.44/0.831 733.89/0.884 935.83/0.911 037.03/0.940 2	31.69/0.850 435.15/0.893 636.85/0.918 338.30/0.940 9	33.29/0.869 335.95/0.904 737.72/0.930 539.43/0.952 7	31.30/0.853 635.04/0.909 937.29/0.938 739.08/0.956 8	33.94/0.886 136.89/0.925 739.05/0.950 341.23/0.968 4
Barbara	0.10.20.30.4	26.70/0.815 531.23/0.912 733.52/0.941 234.62/0.953 2	23.52/0.660 927.20/0.825 630.60/0.913 733.05/0.948 9	29.09/0.878 633.63/0.942 135.98/0.962 037.77/0.973 1	27.40/0.839 631.86/0.925 334.49/0.954 135.93/0.966 3	29.31/0.880 734.05/0.948 336.71/0.968 539.21/0.980 2
Leaves	0.10.20.30.4	20.48/0.732 024.81/0.852 727.68/0.903 829.85/0.931 7	20.93/0.773 527.31/0.932 530.81/0.965 033.73/0.980 5	22.50/0.856 429.38/0.951 733.20/0.973 636.10/0.983 5	21.96/0.812 426.61/0.915 529.63/0.950 632.40/0.970 2	24.35/0.880 830.12/0.957 533.94/0.978 637.57/0.989 9
Monarch	0.10.20.30.4	23.44/0.774 927.02/0.863 829.50/0.904 131.32/0.925 5	23.95/0.794 829.39/0.917 532.33/0.950 834.96/0.967 2	24.95/0.850 029.48/0.925 133.26/0.955 135.88/0.968 5	24.48/0.822 128.36/0.904 130.91/0.936 033.13/0.955 5	26.24/0.858 530.66/0.938 034.11/0.963 037.02/0.976 8
Parrots	0.10.20.30.4	25.12/0.819 128.71/0.888 131.80/0.924 433.86/0.944 1	25.47/0.830 128.57/0.898 131.01/0.927 132.76/0.944 9	26.36/0.863 030.75/0.913 033.73/0.936 635.91/0.953 4	25.79/0.842 829.33/0.900 932.38/0.934 134.39/0.951 6	27.17/0.867 631.45/0.920 434.28/0.945 236.14/0.963 7
Vessels	0.10.20.30.4	20.26/0.665 225.66/0.852 729.85/0.921 933.46/0.961 1	19.87/0.680 226.64/0.899 531.57/0.958 435.71/0.981 6	22.76/0.817 531.16/0.947 735.10/0.973 138.05/0.983 6	20.95/0.725 126.59/0.890 031.06/0.948 034.86/0.974 1	23.92/0.826 831.44/0.952 935.90/0.978 440.26/0.990 6
平均	0.10.20.30.4	24.41/0.773 128.55/0.875 831.36/0.917 733.36/0.942 6	24.24/0.765 029.04/0.894 532.20/0.938 934.75/0.960 7	26.50/0.855 831.73/0.930 734.83/0.955 237.19/0.969 1	25.31/0.815 929.63/0.907 632.63/0.943 634.97/0.962 4	27.49/0.866 832.44/0.940 535.67/0.964 038.57/0.978 3

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