Abstract: The goal of image desnowing is to restore clear scene information from images degraded by complex snowy scenes.Unlike the regularity and semi-transparency of rain,snow exhibits various forms and scales of degradation,with severely degraded regions often obstructing important scene details.Recent methods have employed self-attention mechanisms to address different degradation phenomena.However,global self-attention computation across all image regions is computationally expensive,leading these methods to restrict attention to smaller windows.Yet,due to the occlusion effects in severely degraded areas,the recovery of these regions relies heavily on capturing information from surrounding areas,which results in a receptive field bottleneck,limi-ting the ability to aggregate sufficient information.As a result,these methods struggle to effectively restore large-scale degraded regions.To improve desnowing performance,this paper proposes a novel approach,introducing a new network architecture called FE-DARFormer,which combines a Degradation-Aware Routing Transformer and a Dual-Frequency Enhancement Transformer.FE-DARFormer dynamically routes and applies global self-attention to severely degraded regions,enabling a global receptive field for effective restoration of large degraded areas while reducing computational cost.Additionally,it uses discrete wavelet decomposition to handle multi-scale snow degradation,enhancing the recovery of diverse snowflake shapes and textures.

Key words: Image desnowing, Degradation-aware routing, Dual-frequency enhancement, Global receptive field