{"type":"ScholarlyArticle","title":"DANet: Image Deraining via Dynamic Association Learning","authors":[{"name":"Kui Jiang","slug":"kui-jiang","given":"Kui","family":"Jiang","affiliation":"NERCMS , School of Computer Science , Wuhan University"},{"name":"Zhongyuan Wang","slug":"zhongyuan-wang","given":"Zhongyuan","family":"Wang","affiliation":"NERCMS , School of Computer Science , Wuhan University"},{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":"NERCMS , School of Computer Science , Wuhan University"},{"name":"Peng Yi","slug":"peng-yi","given":"Peng","family":"Yi","affiliation":"NERCMS , School of Computer Science , Wuhan University"},{"name":"Junjun Jiang","slug":"junjun-jiang","given":"Junjun","family":"Jiang","affiliation":"School of Computer Science and Technology , Harbin Institute of Technology"},{"name":"Jinsheng Xiao","slug":"jinsheng-xiao","given":"Jinsheng","family":"Xiao","affiliation":"School of Electronic Information , Wuhan University"},{"name":"Chia-Wen Lin","slug":"chia-wen-lin","given":"Chia-Wen","family":"Lin","affiliation":"National Tsing Hua University"}],"abstract":"Rain streaks and background components in a rainy input are highly correlated, making the deraining task a composition of the rain streak removal and background restoration. However, the correlation of these two components is barely considered, leading to unsatisfied deraining results. To this end, we propose a dynamic associated network (DANet) to achieve the association learning between rain streak removal and background recovery. There are two key aspects to fulfill the association learning: 1) DANet unveils the latent association knowledge between rain streak prediction and background texture recovery, and leverages it as an extra prior via an associated learning module (ALM) to promote the texture recovery. 2) DANet introduces the parametric association constraint for enhancing the compatibility of deraining model with background reconstruction, enabling it to be automatically learned from the training data. Moreover, we observe that the sampled rainy image enjoys the similar distribution to the original one. We thus propose to learn the rain distribution at the sampling space, and exploit super-resolution to reconstruct high-frequency background details for computation and memory reduction. Our proposed DANet achieves the approximate deraining performance to the state-of-the-art MPRNet but only requires 52.6\\% and 23\\% inference time and computational cost, respectively.","doi":"10.24963/ijcai.2022/137","url":"https://ijcai.org/proceedings/2022/137","pdf":"https://ijcai.org/proceedings/2022/0137.pdf","markdown":"https://ijcai.org/proceedings/2022/137.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/137","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Computer Vision","track":"Main Track","number":137,"pages":{"first":980,"last":986},"keywords":["Computer Vision: Other","Computer Vision: Applications","Computer Vision: Computational photography"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986485","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984628","length_sec":317,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Deng et al., 2020] S. 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