Fuzzy image reconstruction algorithm based on multi-scale UNet-attention neural network

Yuetong Han

doi:10.1117/12.2660061

3 February 2023 Fuzzy image reconstruction algorithm based on multi-scale UNet-attention neural network

Yuetong Han

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125112H (2023) https://doi.org/10.1117/12.2660061
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

A multi-scale UNet-attention neural network image reconstruction method is presented to increase the reconstruction quality of degraded images. To improve the reconstruction effect, a "graph-to-graph" U-Net image reconstruction network is built first. Second, a multi-scale input with an image pyramid structure is developed to extract more scale image information while retaining image details. In addition, the attention mechanism is combined to select the important information to obtain the reconstructed image with better visual quality. The experimental results show that the algorithm can recover the image better from the visual effect, and the reconstruction effect is significantly improved compared with the traditional method by using the SSIM evaluation index, which is 0.6 in value than the improved Wiener filtering algorithm and 3dB higher than the UNet structure alone in terms of PSNR index.

Citation Download Citation

Yuetong Han "Fuzzy image reconstruction algorithm based on multi-scale UNet-attention neural network", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125112H (3 February 2023); https://doi.org/10.1117/12.2660061

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KEYWORDS

Image restoration

Image processing

Image quality

Digital image processing

Neural networks

Reconstruction algorithms

Convolution

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