Yuchen Hong, Qian Zheng, Lingran Zhao, Xudong Jiang, A. Kot, Boxin Shi
{"title":"全景图像反射去除","authors":"Yuchen Hong, Qian Zheng, Lingran Zhao, Xudong Jiang, A. Kot, Boxin Shi","doi":"10.1109/CVPR46437.2021.00767","DOIUrl":null,"url":null,"abstract":"This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes. Although a partial view of the reflection scene is included in the panoramic image, it cannot be utilized directly due to its misalignment with the reflection-contaminated image. We propose a two-step approach to solve this problem, by first accomplishing geometric and photometric alignment for the reflection scene via a coarse-to-fine strategy, and then restoring the transmission scene via a recovery network. The proposed method is trained with a synthetic dataset and verified quantitatively with a real panoramic image dataset. The effectiveness of the proposed method is validated by the significant performance advantage over single image-based reflection removal methods and generalization capacity to limited-FoV scenarios captured by conventional camera or mobile phone users.","PeriodicalId":339646,"journal":{"name":"2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"7 22","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Panoramic Image Reflection Removal\",\"authors\":\"Yuchen Hong, Qian Zheng, Lingran Zhao, Xudong Jiang, A. Kot, Boxin Shi\",\"doi\":\"10.1109/CVPR46437.2021.00767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes. Although a partial view of the reflection scene is included in the panoramic image, it cannot be utilized directly due to its misalignment with the reflection-contaminated image. We propose a two-step approach to solve this problem, by first accomplishing geometric and photometric alignment for the reflection scene via a coarse-to-fine strategy, and then restoring the transmission scene via a recovery network. The proposed method is trained with a synthetic dataset and verified quantitatively with a real panoramic image dataset. The effectiveness of the proposed method is validated by the significant performance advantage over single image-based reflection removal methods and generalization capacity to limited-FoV scenarios captured by conventional camera or mobile phone users.\",\"PeriodicalId\":339646,\"journal\":{\"name\":\"2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\",\"volume\":\"7 22\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPR46437.2021.00767\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR46437.2021.00767","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes. Although a partial view of the reflection scene is included in the panoramic image, it cannot be utilized directly due to its misalignment with the reflection-contaminated image. We propose a two-step approach to solve this problem, by first accomplishing geometric and photometric alignment for the reflection scene via a coarse-to-fine strategy, and then restoring the transmission scene via a recovery network. The proposed method is trained with a synthetic dataset and verified quantitatively with a real panoramic image dataset. The effectiveness of the proposed method is validated by the significant performance advantage over single image-based reflection removal methods and generalization capacity to limited-FoV scenarios captured by conventional camera or mobile phone users.