基于深度展开自注意力网络的压缩感知图像重构

doi:10.13229/j.cnki.jdxbgxb.20221564

Abstract

Abstract:

Convolutional neural network applied to image compressive sensing reconstruction at low sampling rate， which contains less information， and it is difficult for the reconstruction network to pay attention to the context information of the image. To overcome this problem， a compressive sensing image reconstruction based on deep unfolded self-attention network is proposed. The network combines sampling matrix and self-attention mechanism for image depth reconstruction， and fully utilizes the information of measurement values through multi-stage reconstruction module to enhance the quality of image reconstruction. The experimental results show that the network proposed can make full use of the sampling information of the image， outperform the existing state-of-the-art methods on different datasets， and the visual effect of the reconstructed image is better.

Key words: computer application, compressive sensing, image reconstruction, self-attention, residual

CLC Number:

TP393

Jin-peng TIAN,Bao-jun HOU. Compressive sensing image reconstruction based on deep unfolding self-attention network[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(10): 3018-3026.

Figures/Tables 8

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Dataset	Algorithm	PSNR/dB
Dataset	Algorithm	MR=1%	MR=4%	MR=10%	MR=25%	Avg
Set 11	TVAL3	15.37	22.36	25.47	29.31	23.13
	MH	17.65	21.64	27.62	31.43	24.59
	GSR	17.91	22.33	28.76	32.21	25.3
	D-AMP	5.6	20.23	29.21	34.52	22.39
	DUSANet	21.11	25.43	29.67	34.89	27.78

Dataset	MR	ReconNet		DR2-Net		ISTA-Net		TIP-CSNet		AMP-Net		DUSANet
Dataset	MR	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM
Set 5	1%	18.07	0.413 8	18.5	0.452 8	18.55	0.440 8	24.07	0.627 2	22.42	0.618 3	24.10	0.639 9
	4%	21.61	0.545 3	22.74	0.618 0	23.45	0.661 9	28.74	0.829 2	27.81	0.817 2	29.11	0.837 1
	10%	24.58	0.676 2	26.56	0.757 4	28.61	0.831 5	32.28	0.909 7	32.10	0.902 4	33.54	0.923 1
	25%	27.22	0.771 2	31.01	0.867 7	34.17	0.927 2	36.24	0.953 2	36.79	0.953 2	38.13	0.963 1
	Avg	22.87	0.601 6	24.70	0.674 0	26.20	0.715 4	30.33	0.829 8	29.78	0.822 8	31.22	0.840 8
Set 11	1%	17.28	0.382 4	17.42	0.429 4	17.45	0.412 8	20.95	0.543 3	20.20	0.558 1	21.11	0.559 6
	4%	20.00	0.525 7	20.80	0.580 6	21.55	0.623 6	24.83	0.760 7	25.26	0.772 2	25.43	0.784 2
	10%	21.69	0.599 1	24.25	0.717 5	26.46	0.803 1	28.06	0.862 9	29.40	0.877 9	29.67	0.890 5
	25%	25.57	0.776 9	28.66	0.843 2	32.38	0.923 2	32.30	0.929 5	34.63	0.9480	34.89	0.953 8
	Avg	21.14	0.5710	22.78	0.642 7	24.46	0.690 7	26.54	0.774 1	27.37	0.789 1	27.78	0.7970
Set 14	1%	18.09	0.390 7	18.31	0.415 0	18.22	0.401 4	22.74	0.549 5	21.65	0.543 4	23.04	0.563 4
	4%	20.62	0.488 9	21.33	0.536 6	22.08	0.570 8	26.16	0.717 9	25.49	0.700 4	26.71	0.733 1
	10%	22.91	0.597 3	24.43	0.663 7	26.00	0.728 9	28.91	0.828 1	28.77	0.818 2	30.11	0.848 2
	25%	25.30	0.711 1	28.11	0.798 4	30.62	0.870 1	32.34	0.902 1	32.51	0.914 4	34.45	0.930 2
	Avg	21.73	0.547 0	23.05	0.603 4	24.23	0.642 8	27.54	0.749 4	27.11	0.744 1	28.58	0.768 7
Urban 100	1%	16.09	0.303 2	16.14	0.323 6	16.22	0.318 3	18.74	0.416 9	18.89	0.436 4	20.20	0.453 4
	4%	18.09	0.405 0	18.55	0.456 3	18.94	0.492 8	21.03	0.601 1	21.97	0.642 6	23.36	0.678 0
	10%	19.95	0.523 1	21.20	0.612 0	22.64	0.583 6	24.08	0.762 7	25.32	0.805 5	26.90	0.839 1
	25%	22.19	0.658 3	24.84	0.777 8	28.29	0.887 3	30.05	0.914 6	30.42	0.925 9	32.04	0.939 8
	Avg	19.08	0.472 4	20.19	0.542 5	21.52	0.570 5	23.48	0.673 8	24.12	0.702 6	25.63	0.727 6
Dataset Avg		21.21	0.548 0	22.68	0.615 7	24.10	0.654 9	26.97	0.756 8	27.09	0.764 65	28.30	0.783 5

Compressive sensing image reconstruction based on deep unfolding self-attention network

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