Efficient feature fusion for lightweight super-resolution network

Yan Wang

doi:10.1117/12.3034771

11 July 2024 Efficient feature fusion for lightweight super-resolution network

Yan Wang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132100G (2024) https://doi.org/10.1117/12.3034771
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Efficient super-resolution networks have been more well-known in recent years due to their effectiveness in increasing image resolution with the least amount of processing cost. This research introduces a novel lightweight super-resolution method that prioritizes efficiency by integrating channel information, fusing spatial elements, and paying attention to multiscale characteristics. The suggested network makes use of cutting-edge deep learning methods, concentrating on the extraction of spatial characteristics to maintain fine details when upscaling. Furthermore, multi-scale characteristics are carefully integrated to handle various image structure levels. The channel information fusion improves the network's capacity to identify intricate linkages in the data. The suggested method achieves significant gains in image quality and resolution, as shown by the experimental findings, demonstrating the usefulness of the lightweight design in practical applications. The results we have shown highlight our network's ability to achieve a higher super-resolution performance while maintaining computational efficiency, which makes it a viable option for real-world picture enhancing applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yan Wang "Efficient feature fusion for lightweight super-resolution network", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132100G (11 July 2024); https://doi.org/10.1117/12.3034771

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KEYWORDS

Super resolution

Feature fusion

Feature extraction

Image enhancement

Image fusion

Convolution

Image processing

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