{"title":"X-NET用于单个图像雨滴去除","authors":"Jiamin Lin, Longquan Dai","doi":"10.1109/ICIP40778.2020.9191073","DOIUrl":null,"url":null,"abstract":"Photos taken on rainy days are likely degraded by raindrops adhered to camera lenses. Removing raindrops from images is a tough task. Its difficulties lie in restoring high frequency information from corrupted images while keeping the color of restored images consistent with human perception. To solve these problems, we propose an end-to-end convolutional neural network consisting of X-Net and RAD-Net (Raindrop Automatic Detection Net). X-Net takes advantage of Long Skip Connections and Cross Branch Connections to generate raindrop-free image with enough details. RAD-Net assists X-Net to produce better results by yielding raindrop location. Extensive experiments show our approach outperforms state-of-the-art methods quantitatively and qualitatively.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"X-NET For Single Image Raindrop Removal\",\"authors\":\"Jiamin Lin, Longquan Dai\",\"doi\":\"10.1109/ICIP40778.2020.9191073\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Photos taken on rainy days are likely degraded by raindrops adhered to camera lenses. Removing raindrops from images is a tough task. Its difficulties lie in restoring high frequency information from corrupted images while keeping the color of restored images consistent with human perception. To solve these problems, we propose an end-to-end convolutional neural network consisting of X-Net and RAD-Net (Raindrop Automatic Detection Net). X-Net takes advantage of Long Skip Connections and Cross Branch Connections to generate raindrop-free image with enough details. RAD-Net assists X-Net to produce better results by yielding raindrop location. Extensive experiments show our approach outperforms state-of-the-art methods quantitatively and qualitatively.\",\"PeriodicalId\":405734,\"journal\":{\"name\":\"2020 IEEE International Conference on Image Processing (ICIP)\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Image Processing (ICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP40778.2020.9191073\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Image Processing (ICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP40778.2020.9191073","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Photos taken on rainy days are likely degraded by raindrops adhered to camera lenses. Removing raindrops from images is a tough task. Its difficulties lie in restoring high frequency information from corrupted images while keeping the color of restored images consistent with human perception. To solve these problems, we propose an end-to-end convolutional neural network consisting of X-Net and RAD-Net (Raindrop Automatic Detection Net). X-Net takes advantage of Long Skip Connections and Cross Branch Connections to generate raindrop-free image with enough details. RAD-Net assists X-Net to produce better results by yielding raindrop location. Extensive experiments show our approach outperforms state-of-the-art methods quantitatively and qualitatively.