{"title":"学习深度感知分解,实现单幅图像去毛刺","authors":"","doi":"10.1016/j.cviu.2024.104069","DOIUrl":null,"url":null,"abstract":"<div><p>Image dehazing under deficient data is an ill-posed and challenging problem. Most existing methods tackle this task by developing either CycleGAN-based hazy-to-clean translation or physical-based haze decomposition. However, geometric structure is often not effectively incorporated in their straightforward hazy-clean projection framework, which might incur inaccurate estimation in distant areas. In this paper, we rethink the image dehazing task and propose a depth-aware perception framework, <strong>DehazeDP</strong>, for robust haze decomposition on deficient data. Our DehazeDP is insthe pired by Diffusion Probabilistic Model to form an end-to-end training pipeline that seamlessly ines the hazy image generation with haze disentanglement. Specifically, in the forward phase, the haze is added to a clean image step-by-step according to the depth distribution. Then, in the reverse phase, a unified U-Net is used to predict the haze and recover the clean image progressively. Extensive experiments on public datasets demonstrate that the proposed DehazeDP performs favorably against state-of-the-art approaches. We release the code and models at <span><span>https://github.com/stallak/DehazeDP</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning depth-aware decomposition for single image dehazing\",\"authors\":\"\",\"doi\":\"10.1016/j.cviu.2024.104069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Image dehazing under deficient data is an ill-posed and challenging problem. Most existing methods tackle this task by developing either CycleGAN-based hazy-to-clean translation or physical-based haze decomposition. However, geometric structure is often not effectively incorporated in their straightforward hazy-clean projection framework, which might incur inaccurate estimation in distant areas. In this paper, we rethink the image dehazing task and propose a depth-aware perception framework, <strong>DehazeDP</strong>, for robust haze decomposition on deficient data. Our DehazeDP is insthe pired by Diffusion Probabilistic Model to form an end-to-end training pipeline that seamlessly ines the hazy image generation with haze disentanglement. Specifically, in the forward phase, the haze is added to a clean image step-by-step according to the depth distribution. Then, in the reverse phase, a unified U-Net is used to predict the haze and recover the clean image progressively. Extensive experiments on public datasets demonstrate that the proposed DehazeDP performs favorably against state-of-the-art approaches. We release the code and models at <span><span>https://github.com/stallak/DehazeDP</span><svg><path></path></svg></span>.</p></div>\",\"PeriodicalId\":50633,\"journal\":{\"name\":\"Computer Vision and Image Understanding\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Vision and Image Understanding\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1077314224001504\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224001504","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Learning depth-aware decomposition for single image dehazing
Image dehazing under deficient data is an ill-posed and challenging problem. Most existing methods tackle this task by developing either CycleGAN-based hazy-to-clean translation or physical-based haze decomposition. However, geometric structure is often not effectively incorporated in their straightforward hazy-clean projection framework, which might incur inaccurate estimation in distant areas. In this paper, we rethink the image dehazing task and propose a depth-aware perception framework, DehazeDP, for robust haze decomposition on deficient data. Our DehazeDP is insthe pired by Diffusion Probabilistic Model to form an end-to-end training pipeline that seamlessly ines the hazy image generation with haze disentanglement. Specifically, in the forward phase, the haze is added to a clean image step-by-step according to the depth distribution. Then, in the reverse phase, a unified U-Net is used to predict the haze and recover the clean image progressively. Extensive experiments on public datasets demonstrate that the proposed DehazeDP performs favorably against state-of-the-art approaches. We release the code and models at https://github.com/stallak/DehazeDP.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems