The deep-feedback deraining network based on the self-attention mechanism

Na Dong; Hongguang Pan; Fan Wen

doi:10.1117/12.2631141

18 March 2022 The deep-feedback deraining network based on the self-attention mechanism

Na Dong, Hongguang Pan, Fan Wen

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121682I (2022) https://doi.org/10.1117/12.2631141
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

In recent years, image deraining has received extensive attention in image restoration and computer vision, and scholars mainly use it in fields such as video surveillance and autonomous driving. On rainy days, the rain marks will block the background of the scene, and the visibility of many scenes is low. In rainy images, the appearing alternately rain marks make it hard to differentiate between the background details and rain marks of the image, and the contrast and color of the target in the image will be attenuated to varying degrees, which leads to unclear expression of background information and makes some video or image systems unable to work properly. Therefore, it is very important to eliminate the influence of the rainy day on the scene of the image. This paper proposes the deraining network via the deep feedback based on the self-attention mechanism, the network uses feedback connections to feedback high-order information to low-order information and corrects the low-order information. Then inputs the image representation tensor fully processed by multiple feedback blocks into the self-attention mechanism, and the important features are fully extracted. The results prove that the network has strong reconstruction ability, and can generate clear and high-quality deraining images.

Citation Download Citation

Na Dong, Hongguang Pan, and Fan Wen "The deep-feedback deraining network based on the self-attention mechanism", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121682I (18 March 2022); https://doi.org/10.1117/12.2631141

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KEYWORDS

Convolution

Visualization

Information fusion

Computer vision technology

Convolutional neural networks

Image processing

Image quality

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