Automated surveillance is widely opted for applications such as traffic monitoring, vehicle identification, etc . But, various weather degradation factors such as rain and snow streaks, along with atmospheric veil severely affect the perceptual quality of an image, eventually affecting the performance of these applications. There exist weather specific (rain, haze, snow, etc .) methods focusing on respective restoration task. As image restoration is a preprocessing step for high level surveillance applications, it is practically inapplicable to have different architectures for different weather restoration. In this paper, we propose a lightweight unified network, having 1.1M parameters (1 / 40 th and 1 / 6 th of the existing state-of-the-art rain with veil removal, and snow with veil removal methods respectively) for removal of rain and snow along with the veiling effect present in the images. In this network, we propose two parallel streams to handle the degradations and restoration: First, degradation removal stream (DRS) focuses mainly on removing randomly repeating degradations i.e., rain and snow streaks, through the proposed adaptive multi-scale feature sharing block (AMFSB) and stage-wise subtractive block (SSB). Second, feature corrector stream (FCS) mainly focuses on refining the partial outputs of the first stream, reducing the veiling effect and acts supplementary to the first stream. Finally, we leverage contrastive regularization for better convergence of the proposed network.
School of Computer Science and Statistics