Blind image super-resolution reconstruction based on dual regression network

HongPeng Tian; Shengzhou Jiang

doi:10.1117/12.2667901

28 March 2023 Blind image super-resolution reconstruction based on dual regression network

HongPeng Tian, Shengzhou Jiang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256620 (2023) https://doi.org/10.1117/12.2667901
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

Existing deep learning-based Super Resolution (SR) reconstruction algorithms achieve remarkable performance on images with known degradation. Most of the degradation models exists problems in self-adaptations when facing with the deviation of the degradation model of the image of the real scene, and the effect is not good. Therefore, this paper proposes a blind image super-resolution reconstruction algorithm based on dual regression, which aims to solve the problem of poor performance of super-resolution networks in real scenes. Firstly, the closed-loop network is used to constrain the mapping space, and the optimal reconstruction function is found to improve the network reconstruction performance. Secondly, the attention mechanism is adopted into the residual block of feature extraction to expand the receptive field of the feature map, improve the reuse of features, and strengthen the reconstruction of high-frequency information. Finally, the frequency-domain blur kernel map estimates the down sampling kernel and reconstructs the low-resolution image, adaptively extracts the feature expression, enhances the ability to restore texture details, and reconstructs the real-world image better.

Citation Download Citation

HongPeng Tian and Shengzhou Jiang "Blind image super-resolution reconstruction based on dual regression network", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256620 (28 March 2023); https://doi.org/10.1117/12.2667901

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KEYWORDS

Super resolution

Reconstruction algorithms

Feature extraction

Data modeling

Image enhancement

Lawrencium

Convolution

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