Dual attention-guided residual U-Net for image denoising

Huaji Li; Jianghua Cheng; Tong Liu; Bang Cheng

doi:10.1117/12.2624825

13 December 2021 Dual attention-guided residual U-Net for image denoising

Huaji Li, Jianghua Cheng, Tong Liu, Bang Cheng

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871Y (2021) https://doi.org/10.1117/12.2624825
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

We propose a Dual Attention-guided Residual U-Net (DARUNet) for Image denoising. This learning is based on the U-Net with several dual attention residual modules embedded in the network to realize the image denoising of Gaussian synthesis noise. Specifically, our method combines the advantages of the reconstruction and transformation of U-Net, the ability of strengthening model training of residual structure and the guiding role of attention mechanism. Among them, the attention mechanism adopts the dual attention parallel structure including spatial attention and channel attention, which effectively guides the model to reduce noise. The model can retain the detail features of the original image, and has excellent ability for image denoising. The resulting images are more natural and have higher quality. A large number of experimental results show that our method achieves advanced performance qualitatively and quantitatively.

Citation Download Citation

Huaji Li, Jianghua Cheng, Tong Liu, and Bang Cheng "Dual attention-guided residual U-Net for image denoising", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871Y (13 December 2021); https://doi.org/10.1117/12.2624825

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KEYWORDS

Image denoising

Denoising

Convolution

Image processing

Performance modeling

Visualization

Image enhancement

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