USIR-Net: sand-dust image restoration based on unsupervised learning

Abstract

In sand-dust weather, the influence of sand-dust particles on imaging equipment often results in images with color deviation, blurring, and low contrast, among other issues. These problems making many traditional image restoration methods unable to accurately estimate the semantic information of the images and consequently resulting in poor restoration of clear images. Most current image restoration methods in the field of deep learning are based on supervised learning, which requires pairing and labeling a large amount of data, and the possibility of manual annotation errors. In light of this, we propose an unsupervised sand-dust image restoration network. The overall model adopts an improved CycleGAN to fit unpaired sand-dust images. Firstly, multiscale skip connections in the multiscale cascaded attention module are used to enhance the feature fusion effect after downsampling. Secondly, multi-head convolutional attention with multiple input concatenations is employed, with each head using different kernel sizes to improve the ability to restore detail information. Finally, the adaptive decoder-encoder module is used to achieve adaptive fitting of the model and output the restored image. According to the experiments conducted on the dataset, the qualitative and quantitative indicators of USIR-Net are superior to the selected comparison algorithms, furthermore, in additional experiments conducted on haze removal and underwater image enhancement, we have demonstrated the wide applicability of our model.