Research on image blind super-resolution algorithm based on generative adversarial networks

Hailiang Sun; Jingxuan Jin; De Li

doi:10.1117/12.2680990

23 May 2023 Research on image blind super-resolution algorithm based on generative adversarial networks

Hailiang Sun, Jingxuan Jin, De Li

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126451F (2023) https://doi.org/10.1117/12.2680990
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

In the blind super-resolution reconstruction task, it is easy to fall into a single degradation. In order to solve this problem, we add the dropout mechanism before the last convolutional layer of the network, which can effectively prevent co-adapting, so that the network can be well restored to various degradations. To enhance the network's ability to extract high-frequency details, a HAM attention mechanism is introduced behind the residual block, which can reconstruct clearer textures. Aiming at the problem of ubiquitous artifacts in images generated in fine-grained and structured areas in the generative confrontation network, the LDL loss is introduced to make the area better restored. Experimental results show that the method proposed in this paper can achieve high performance and is closer to natural images in subjective vision.

Citation Download Citation

Hailiang Sun, Jingxuan Jin, and De Li "Research on image blind super-resolution algorithm based on generative adversarial networks", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126451F (23 May 2023); https://doi.org/10.1117/12.2680990

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