Super-resolution reconstruction of image based on generative adversarial network with attention module

Tong Zhang; Huajun Wang; Ruihong Cheng; Zhongyu Li; Yu Ma; Linfeng Wu

doi:10.1117/12.2600169

1 June 2021 Super-resolution reconstruction of image based on generative adversarial network with attention module

Tong Zhang, Huajun Wang, Ruihong Cheng, Zhongyu Li, Yu Ma, Linfeng Wu

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 1184805 (2021) https://doi.org/10.1117/12.2600169
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

In order to solve the problems of unstable training and texture blurring of generated images, we proposed a generative adversarial network combining residual and attention block. The attention module is added to the network, which reduces the dependence on the network depth and reduces the depth of the model. The dense connection in the residual module can extract richer image details. The number of parameters is reduced and the calculation efficiency is greatly improved. Generative adversarial network is used to further improve the texture details of the image. Generator loss functions include a content loss, a perceptual loss, a texture loss and an adversarial loss. The texture loss is used to enhance the matching degree of local information, and the perceptual loss is used to obtain more detailed features by using the feature information before an activation layer. The experimental results show that the peak signal to noise ratio is 32.10 dB, and the structural similarity is 0.92. Compared with bicubic, SRCNN, VDSR and SRGAN, the proposed algorithm improves the texture details of reconstructed images.

Citation Download Citation

Tong Zhang, Huajun Wang, Ruihong Cheng, Zhongyu Li, Yu Ma, and Linfeng Wu "Super-resolution reconstruction of image based on generative adversarial network with attention module", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 1184805 (1 June 2021); https://doi.org/10.1117/12.2600169

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