基于组稀疏表示和加权全变分的图像压缩感知重构

doi:10.3969/j.issn.1001-506X.2020.10.04

Abstract

Abstract:

The traditional compression sensing (CS) reconstruction algorithm based on group sparse representation (GSR) uses the sparsity and nonlocal similarity of the signal to reconstruct the image signal. However, the local smoothness of the image is not sufficiently consiclered, which affects the reconstruction performance of the algorithm. Considering the three prior informations of signal sparsity, nonlocal similarity and smoothness, an image CS reconstruction algorithm based on GSR and weighted total variation (WTV) is proposed. Aiming at the problem that global weighting will introduce wrong texture and edge artifacts for the traditional WTV, a new WTV strategy only sets the weight of high frequency cmponet of image is used to protect the image reconstruction quality. In addition, aiming at the problem that the hard threshold iteration ignores the low frequency principal component coefficient, the hard threshold modulus square method is used to better protect the non-principal component coefficient. Experimental results show that the peak signal to noise ratio of the proposed algorithm is improved 5.4 dB and 0.62 dB compared with the total variation nonlocal regularization and CS algorithm based on GSR at the same sampling rate respectively, which proves that the proposed algorithm effectively protects the details of the image.

Key words: compressed sensing (CS), group sparse representation, weighted total variation (WTV), image reconstruction

CLC Number:

TP391

Hui ZHAO, Lufa FANG, Tianqi ZHANG, Zhiwei LI, Xianming XU. Image compressive sensing reconstruction via group sparse representation and weighted total variation[J]. Systems Engineering and Electronics, 2020, 42(10): 2172-2180.

Figures/Tables 9

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References 26

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测试图像	采样率	PSNR/dB
测试图像	采样率	NLRTV	GSR	ALSB	GSR-NCR	GSR-WTV
House	0.1	29.68	33.76	32.52	32.01	34.00
	0.2	33.06	36.78	35.73	36.57	37.32
	0.3	35.53	38.93	38.15	39.38	39.52
	0.4	37.21	40.60	40.14	41.12	41.15
Barbara	0.1	22.48	29.99	29.09	27.99	30.16
	0.2	24.46	34.59	31.61	33.93	34.78
	0.3	25.62	36.92	34.73	37.19	37.31
	0.4	27.90	38.99	37.26	39.20	39.21
Monarch	0.1	24.12	25.29	24.12	23.72	26.02
	0.2	27.35	29.55	28.23	28.64	31.07
	0.3	29.84	33.17	31.48	33.53	34.16
	0.4	31.76	36.07	34.33	36.88	36.99
Starfish	0.1	22.81	23.60	23.23	22.87	24.41
	0.2	25.75	29.42	27.38	27.23	29.62
	0.3	27.96	33.00	30.26	31.75	32.90
	0.4	30.67	35.63	33.29	35.34	35.77
Average	0.1	24.77	28.16	27.24	26.64	28.64
	0.2	28.21	32.58	30.73	31.59	33.20
	0.3	29.73	35.49	33.66	35.46	36.00
	0.4	31.89	37.82	36.26	38.13	38.34

测试图像	采样率	SSIM
测试图像	采样率	NLRTV	GSR	ALSB	GSR-NCR	GSR-WTV
House	0.1	0.833 1	0.869 5	0.859 2	0.856 8	0.883 2
	0.2	0.881 2	0.913 6	0.914 3	0.913 9	0.932 5
	0.3	0.910 4	0.940 3	0.946 6	0.955 3	0.957 0
	0.4	0.930 8	0.958 2	0.962 5	0.968 0	0.968 7
Barbara	0.1	0.616 4	0.858 2	0.821 8	0.854 1	0.903 0
	0.2	0.741 8	0.938 3	0.927 5	0.942 7	0.957 5
	0.3	0.809 8	0.963 2	0.959 9	0.971 1	0.973 2
	0.4	0.880 5	0.975 1	0.973 3	0.980 3	0.980 9
Monarch	0.1	0.780 7	0.857 1	0.815 9	0.779 7	0.878 0
	0.2	0.882 7	0.929 2	0.908 0	0.904 5	0.944 6
	0.3	0.925 4	0.954 5	0.945 0	0.957 9	0.965 8
	0.4	0.945 6	0.968 8	0.967 0	0.975 1	0.977 0
Starfish	0.1	0.674 6	0.756 1	0.722 2	0.702 8	0.756 1
	0.2	0.802 3	0.869 5	0.843 9	0.837 5	0.892 0
	0.3	0.869 6	0.920 6	0.911 9	0.917 9	0.937 0
	0.4	0.918 9	0.947 9	0.947 6	0.953 9	0.961 7
Average	0.1	0.726 2	0.835 2	0.804 8	0.798 3	0.855 0
	0.2	0.827 0	0.912 6	0.898 4	0.899 6	0.931 6
	0.3	0.878 8	0.944 6	0.940 8	0.950 5	0.958 2
	0.4	0.918 9	0.962 5	0.962 6	0.969 3	0.972 0

图像	采样率	NLRTV	GSR	ALSB	GSR-NCR	GSR-WTV
House	0.1	142	396	414	831	430
	0.2	94	293	305	697	306
	0.3	72	237	266	585	287
	0.4	67	196	167	482	143
平均时间		94	281	288	649	291

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Image compressive sensing reconstruction via group sparse representation and weighted total variation

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