JSTASR: Joint Size and Transparency-Aware Snow Removal Algorithm Based on Modified Partial Convolution and Veiling Effect Removal

Wei-Ting Chen, Hao-Yu Fang, Jian-Jiun Ding, Cheng-Che Tsai, Sy-Yen Kuo ;

Snow removal usually affects the performance of computer vision. Comparing with other atmospheric phenomenon (e.g., haze and rain), snow is more complicated due to its transparency, various size, and accumulation of veiling effect, which make single image de-snowing more challenging. In this paper, first, we reformulate the snow model. Different from that in the previous works, in the proposed snow model, the veiling effect is included. Second, a novel joint size and transparency-aware snow removal algorithm called JSTASR is proposed. It can classify snow particles according to their sizes and conduct snow removal in different scales. Moreover, to remove the snow with different transparency, the transparency-aware snow removal is developed. It can address both transparent and non-transparent snow particles by applying the modified partial convolution. Experiments show that the proposed method achieves significant improvement on both synthetic and real-world datasets and is very helpful for object detection on snow images."