{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/desnownet-context-aware-deep-network-for-snow","title":"DesnowNet: Context-Aware Deep Network for Snow Removal","arxiv_id":"1708.04512","date":"2017-08-15","proceeding":null,"authors":["Yun-Fu Liu","Da-Wei Jaw","Shih-Chia Huang","Jenq-Neng Hwang"],"abstract":"Existing learning-based atmospheric particle-removal approaches such as those\nused for rainy and hazy images are designed with strong assumptions regarding\nspatial frequency, trajectory, and translucency. However, the removal of snow\nparticles is more complicated because it possess the additional attributes of\nparticle size and shape, and these attributes may vary within a single image.\nCurrently, hand-crafted features are still the mainstream for snow removal,\nmaking significant generalization difficult to achieve. In response, we have\ndesigned a multistage network codenamed DesnowNet to in turn deal with the\nremoval of translucent and opaque snow particles. We also differentiate snow\ninto attributes of translucency and chromatic aberration for accurate\nestimation. Moreover, our approach individually estimates residual complements\nof the snow-free images to recover details obscured by opaque snow.\nAdditionally, a multi-scale design is utilized throughout the entire network to\nmodel the diversity of snow. As demonstrated in experimental results, our\napproach outperforms state-of-the-art learning-based atmospheric phenomena\nremoval methods and one semantic segmentation baseline on the proposed Snow100K\ndataset in both qualitative and quantitative comparisons. The results indicate\nour network would benefit applications involving computer vision and graphics.","url_abs":"http://arxiv.org/abs/1708.04512v1","url_pdf":"http://arxiv.org/pdf/1708.04512v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"single-image-desnowing","task_name":"Single Image Desnowing"},{"task_slug":"snow-removal","task_name":"Snow Removal"}],"methods":[],"datasets_introduced":[{"slug":"snow100k","name":"Snow100K","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1708.04512","atlas_url":"https://app.syntology.ai/?focus=1708.04512","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}