WiperNet: A Lightweight Multi-Weather Restoration Network for Enhanced Surveillance

Adherent raindrops, rainstreaks and snow severely degrade the perceptual quality of an image, eventually affecting the performance of several computer vision based applications which are applied in outdoor scenarios, e.g., traffic monitoring, autonomous driving, etc. Due to the complex appearance properties, removal of such degradations from an image is a challenging task. Working towards mitigating this problem, in this paper, a lightweight network named as WiperNet is proposed which tackles the problem of raindrops, rain streaks and snow removal present in an image. The WiperNet makes use of the proposed Dual Restoration (DR) mechanism, where the input features are processed twice through the network. In the network, Multi-scale Context Aware Residual Block (MCARB) is proposed for integrating contextual information from various scales. Also, Adaptive Varying Receptive Fusion Block (AVRFB) is proposed for adaptively fusing the information acquired through different dilation rates. Finally, we propose a Feature Refinement Stream which makes use of multiple kernel sizes of convolution filters and spatio-channel attention blocks for focusing on relevant information for effective removal of the degradations while using the coarse outputs of the features from the initial layers of the network. Substantial experiments and ablation study scrutinize that the proposed lightweight WiperNet outperforms the existing state-of-the-art methods for raindrop, rain streak and snow removal. The code is provided at https://github.com/AshutoshKulkarni4998/WiperNet.