{"type":"ScholarlyArticle","title":"DFMDA-Net: Dense Fusion and Multi-dimension Aggregation Network for Image Restoration","authors":[{"name":"Huibin Yan","slug":"huibin-yan","given":"Huibin","family":"Yan","affiliation":"Shenzhen University , Shenzhen , China; Province Engineering Laboratory for Digital Creative Tech- nology and the; Shenzhen Key Laboratory of Digital Creative Tech- nology."},{"name":"Shuoyao Wang","slug":"shuoyao-wang","given":"Shuoyao","family":"Wang","affiliation":"Shenzhen University , Shenzhen , China"}],"abstract":"The U-shape (encoder-decoder) architecture, combined with effective blocks, has shown significant\r\nsuccess in image restoration. In U-shape models, there is insufficient focus on the feature fusion\r\nproblem between encoder and decoder features at\r\nthe same level. Current methods often employ simplistic operations like summation or concatenation,\r\nwhich makes it difficult to strike a balance between\r\nperformance and complexity. To address this issue,\r\nwe propose a compression-in-the-middle mechanism, termed Integration-Compression-Integration\r\n(ICI), which effectively conducts dense fusion and\r\navoids information loss. From the block design\r\nperspective, we design a multi-dimension aggregation (MDA) mechanism, capable of effectively\r\naggregating features from both the channel and\r\nspatial dimension. Combining the IntegrationCompression-Integration feature fusion and the\r\nmulti-dimension aggregation, our dense fusion and\r\nmulti-dimension aggregation network (DFMDANet) achieves superior performance over state-ofthe-art algorithms on 16 benchmarking datasets for\r\nnumerous image restoration tasks.","doi":"10.24963/ijcai.2024/169","url":"https://ijcai.org/proceedings/2024/169","pdf":"https://ijcai.org/proceedings/2024/0169.pdf","markdown":"https://ijcai.org/proceedings/2024/169.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/169","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":169,"pages":{"first":1525,"last":1533},"keywords":["Machine Learning: ML: Convolutional networks","Computer Vision: CV: Machine learning for vision","Computer Vision: CV: Representation learning","Machine Learning: ML: Attention models"],"videos":[],"references":[{"position":1,"raw":"[Abdelhamed et al., 2018] Abdelrahman Abdelhamed, Stephen Lin, et al. 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