Research on Image Super-resolution Reconstruction Based on Loss Extraction Feedback Attention Network

doi:10.16182/j.issn1004731x.joss.21-0986

Abstract

Abstract:

Since the first application of convolutional neural network to the field of super-resolution image reconstruction (super-resolution convolutional neural network, SRCNN), a large number of studies have proved that deep learning can improve the effect of image reconstruction. Aiming at the too many parameters in the image super-resolution network and the insufficient utilization of image features resulting in less available high-frequency information, a loss extraction feedback attention network (LEFAN) is proposed to reuse parameters in a circular way and increase the reuse of low-resolution image features to capture more high-frequency information. The loss caused in the reconstruction process is extracted and fused into the final super-resolution image. The experimental results show that the algorithm can obtain a better image reconstruction effect by extracting the potential loss and fusing it into the final super-resolution image on the basis of the multiple utilization of low-resolution images.

Key words: feedback mechanism, attention mechanism, loss extraction, super-resolution image reconstruction

CLC Number:

TP391

Hong Sun, Yuxiang Zhang, Yuelan Ling. Research on Image Super-resolution Reconstruction Based on Loss Extraction Feedback Attention Network[J]. Journal of System Simulation, 2023, 35(2): 308-317.

Figures/Tables 13

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模型名称	缩放因子	混合域注意力机制	损失提取策略	PSNR/dB	模型名称	缩放因子	混合域注意力机制	损失提取策略	PSNR/dB
I	×2	×	×	37.72	III	×2	×	√	37.84
	×3	×	×	33.95		×3	×	√	34.33
	×4	×	×	31.80		×4	×	√	32.25
II	×2	√	×	38.03	IV	×2	√	√	38.15
	×3	√	×	34.52		×3	√	√	34.74
	×4	√	×	32.31		×4	√	√	32.48

数据集	缩放因子	Bicubic (PSNR/SSIM)	SRCNN (PSNR/SSIM)	VDSR (PSNR/SSIM)	DRRN (PSNR/SSIM)	EDSR (PSNR/SSIM)	SRFBN (PSNR/SSIM)	本文算法 (PSNR/SSIM)
Set 5	×2	33.66/0.930	36.66/0.954	37.53/0.959	37.74/0.959	38.11/0.960	38.11/0.961	38.15/0.961
	×3	30.39/0.868	32.75/0.909	33.67/0.921	34.03/0.924	34.65/0.928	34.70/0.929	34.74/0.929
	×4	28.42/0.810	30.48/0.863	31.35/0.883	31.68/0.888	32.46/0.897	32.47/0.8983	32.480.899
Set 14	×2	30.24/0.869	33.45/0.907	33.05/0.913	33.23/0.913	33.92/0.920	33.82/0.9196	33.92/0.919
	×3	27.55 /0.774	29.30 /0.822	29.78 /0.83	29.96/0.835	30.52/0.846	30.51/0.8461	30.56/0.846
	×4	26.00/0.703	27.50/0.751	28.02/0.768	28.21/0.773	28.80/0.787	28.81/0.7868	28.85/0.788
BSD100	×2	29.56/0.843	31.36/0.888	31.90/0.896	32.05/0.897	32.32/0.901	32.29/0.901	32.32/0.92
	×3	27.21 /0.739	28.41/0.786	28.83 /0.799	28.95/0.800	29.25/0.809	29.24/0.808	29.26/0.809
	×4	25.96/0.668	26.90/0.710	27.29/0.073	27.38/0.728	27.71/0.742	27.72/0.741	27.71/0.741
Urban100	×2	26.88/0.840	29.50/0.895	30.77/0.914	31.23/0.919	32.93/0.935	32.62/0.9328	32.72/0.934
	×3	24.46 /0.735	26.24/0.799	27.14 /0.829	27.53/0.837	28.80/0.865	28.73/0.8641	28.81/0.864
	×4	23.14/0.658	24.52/0.722	25.18/0.754	25.44/0.764	26.64/0.803	26.60/0.802	26.65/0.803

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