Encoder-decoder network with self-attention module for image restoration

Qing Jin; Qi Yu; Jiying Liu; Xintong Tan

doi:10.1117/12.2623391

16 February 2022 Encoder-decoder network with self-attention module for image restoration

Qing Jin, Qi Yu, Jiying Liu, Xintong Tan

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 1208313 (2022) https://doi.org/10.1117/12.2623391
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Due to the absorption and scattering effect of the atmosphere, it is difficult to extract effective target information from the image sensor under severe hazy conditions. We propose an end-to-end convolutional neural network designed to solve the problem of image restoration in scattering imaging. And we explicitly consider the atmospheric scattering model of the hazing process in the network design. The encoder and decoder modules are used for feature extraction, and the pyramid pooling network is used to preserve the multi-scale features of the image. The attention mechanism is introduced in the network. Encoder module is adopted for BoTNet that incorporates self-attention for multiple computer vision tasks. Due to the lack of real hazy images, we collected scattered images under low visibility conditions and corresponding haze-free images. We examine the proposed method by the challenge datasets, the experiments demonstrate that the proposed method can effectively extract and recover the feature information of the target. The results show that, compared with the traditional signal processing method, our model achieves significant practical performance gains and restores the detailed information of the image.

Citation Download Citation

Qing Jin, Qi Yu, Jiying Liu, and Xintong Tan "Encoder-decoder network with self-attention module for image restoration", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 1208313 (16 February 2022); https://doi.org/10.1117/12.2623391

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