Image denoising algorithm based on self-attention residual network

Wei Wu; Hao Wu

doi:10.1117/12.3031897

8 June 2024 Image denoising algorithm based on self-attention residual network

Wei Wu, Hao Wu

Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 131710L (2024) https://doi.org/10.1117/12.3031897
Event: 3rd International Conference on Algorithms, Microchips and Network Applications (AMNA 2024), 2024, Jinan, China

Abstract

Image denoising algorithm based on depth learning generally uses convolution sparse self-coding network as the main framework of the denoising network. However, although convolution sparse self-coding network can effectively suppress the noise information in the image, it has the problem of loss of certain details in the image after denoising. Aiming at this defect, on the basis of convolutional sparse self-encoding network, the detail information of each layer feature map is extracted from the output of each encoder layer using self-attention mechanism, and the detail information is integrated into the input layer of the corresponding decoder using residual connection method. Experimental results show that compared with the traditional convolutional self-coding noise reduction network, the proposed convolutional self-coding network based on self-attention residuals can effectively improve the level of network noise reduction. At the same time, compared with the mainstream noise reduction network, the proposed algorithm can also achieve better noise reduction effect.

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

Citation Download Citation

Wei Wu and Hao Wu "Image denoising algorithm based on self-attention residual network", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 131710L (8 June 2024); https://doi.org/10.1117/12.3031897

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KEYWORDS

Denoising

Convolution

Image denoising

Education and training

Data modeling

Visualization

Mathematical optimization

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