Towards building long-range relationship for super-resolution

Zifan Wang

doi:10.1117/12.2667905

28 March 2023 Towards building long-range relationship for super-resolution

Zifan Wang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256606 (2023) https://doi.org/10.1117/12.2667905
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

A new network super-resolution algorithm is proposed in the light of multi-feature fusion to surmount: the problem of manual feature extraction as well as low information throughput in traditional super-resolution algorithms. The algorithm adopts a learning method of the indirect mapping from the hazing image to the clear super-resolution image, which calculates the similarity between the point and other points, normalize the similarity to obtain the weight between each point, and then multiply it by the feature map value of the corresponding point and use the similarity. In the algorithm, utilizing multi-scale feature fusion enables to rebuild details of the image. Different depths of field combine the contour information obtained by shallow convolution with the detail information obtained by deep convolution to enhance the overall super-resolution’s effect. The experimental results and related data show that the channel throughput has been significantly improved, and compared with other algorithms, it has better detail information and contrast, which provides a new idea for dehazing methods.

Citation Download Citation

Zifan Wang "Towards building long-range relationship for super-resolution", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256606 (28 March 2023); https://doi.org/10.1117/12.2667905

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