SGNet: Efficient Snow Removal Deep Network with a Global Windowing Transformer

Lie Shan, Haoxiang Zhang and Bodong Cheng ()
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Lie Shan: Anhui Communications Vocational & Technical College, Hefei 230051, China
Haoxiang Zhang: Shanghai University, Shanghai 200444, China
Bodong Cheng: East China Normal University, Shanghai 200062, China

Mathematics, 2024, vol. 12, issue 10, 1-13

Abstract: Image restoration under adverse weather conditions poses a challenging task. Previous research efforts have predominantly focused on eliminating rain and fog phenomena from images. However, snow, being another common atmospheric occurrence, also significantly impacts advanced computer vision tasks such as object detection and semantic segmentation. Recently, there has been a surge of methods specifically targeting snow removal, with the majority employing visual Transformers as the backbone network to enhance restoration effectiveness. Nevertheless, due to the quadratic computations required by Transformers to model long-range dependencies, this significantly escalates the time and space consumption of deep learning models. To address this issue, this paper proposes an efficient snow removal Transformer with a global windowing network (SGNet). This method forgoes the localized windowing strategy of previous visual Transformers, opting instead to partition the image into multiple low-resolution subimages containing global information using wavelet sampling, thereby ensuring higher performance while reducing computational overhead. Extensive experimentation demonstrates that our approach achieves outstanding performance across a wide range of benchmark datasets and can rival methods employing CNNs in terms of computational cost.

Keywords: image snow removal; transformer; efficient network; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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