Single-Image Snow Removal Based on an Attention Mechanism and a Generative Adversarial Network

Abstract

Bad weather, such as snowfall, can seriously decrease the quality of images and pose great challenges to computer vision algorithms. In view of the negative effect of snowfall, this paper presents a single-image snow removal method based on a generative adversarial network (GAN). Unlike previous GANs, our GAN includes an attention mechanism in the generator component. By injecting attention information, the network can pay increased attention to areas covered by snow and improve its capability to perform local repairs. At the same time, we improve the traditional U-Net network by combining it with the residual network to enhance the effect of the model when removing snowflakes from a single image. Our experiments on both synthetic and real-word images show that our method produces better results than those of other state-of-the-art methods.INDEX TERMS Snow removal, generative adversarial networks, attention mechanisms.

Relaxation-Based Radiometric Normalization for Multitemporal Cross-Sensor Satellite Images

“…These images were acquired between February and March 2021, at the end of the winter season. The primary objective of this dataset is to investigate the impact of normalization on snowy images, which can be challenging to process due to their high reflectance, which often results in loss of detail and information in bright areas [18]. Datasets #4 and #5 are composed of surface-reflectance images of urban areas in Manaus, Brazil, and Dubbo, Australia, respectively.…”