{"title":"用于去除单幅图像反射的深度变量推理网络","authors":"Ya-Nan Zhang;Qiufu Li;Linlin Shen;Ailian He;Song Wu","doi":"10.1109/TETCI.2024.3359063","DOIUrl":null,"url":null,"abstract":"Reflection removal from an image with undesirable reflections is a challenging and ill-posed problem in low-level vision. In recent years, several deep learning approaches have been proposed to tackle the task of single image reflection removal (SIRR). These methods, however, do not fully utilize the fundamental image priors of reflection and lack interpretability. In this paper, we propose a deep variational inference reflection removal (VIRR) method for the SIRR problem, which has good interpretability and good generalization ability. Based on the proposed VIRR method, the posterior distributions of the latent transmission and reflection images can be estimated jointly through variational inference, using deep neural networks. Furthermore, the proposed network framework can be trained by the supervision of data-driven priors for the transmission image and reflection image, which is produced by the variational lower bound objective of marginal data likelihood. Our proposed method outperforms previous state-of-the-art approaches on four benchmark datasets, as demonstrated by extensive subjective and objective evaluations.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Variational Inference Network for Single Image Reflection Removal\",\"authors\":\"Ya-Nan Zhang;Qiufu Li;Linlin Shen;Ailian He;Song Wu\",\"doi\":\"10.1109/TETCI.2024.3359063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reflection removal from an image with undesirable reflections is a challenging and ill-posed problem in low-level vision. In recent years, several deep learning approaches have been proposed to tackle the task of single image reflection removal (SIRR). These methods, however, do not fully utilize the fundamental image priors of reflection and lack interpretability. In this paper, we propose a deep variational inference reflection removal (VIRR) method for the SIRR problem, which has good interpretability and good generalization ability. Based on the proposed VIRR method, the posterior distributions of the latent transmission and reflection images can be estimated jointly through variational inference, using deep neural networks. Furthermore, the proposed network framework can be trained by the supervision of data-driven priors for the transmission image and reflection image, which is produced by the variational lower bound objective of marginal data likelihood. Our proposed method outperforms previous state-of-the-art approaches on four benchmark datasets, as demonstrated by extensive subjective and objective evaluations.\",\"PeriodicalId\":13135,\"journal\":{\"name\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10432786/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10432786/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Deep Variational Inference Network for Single Image Reflection Removal
Reflection removal from an image with undesirable reflections is a challenging and ill-posed problem in low-level vision. In recent years, several deep learning approaches have been proposed to tackle the task of single image reflection removal (SIRR). These methods, however, do not fully utilize the fundamental image priors of reflection and lack interpretability. In this paper, we propose a deep variational inference reflection removal (VIRR) method for the SIRR problem, which has good interpretability and good generalization ability. Based on the proposed VIRR method, the posterior distributions of the latent transmission and reflection images can be estimated jointly through variational inference, using deep neural networks. Furthermore, the proposed network framework can be trained by the supervision of data-driven priors for the transmission image and reflection image, which is produced by the variational lower bound objective of marginal data likelihood. Our proposed method outperforms previous state-of-the-art approaches on four benchmark datasets, as demonstrated by extensive subjective and objective evaluations.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.