{"type":"ScholarlyArticle","title":"Boundary-aware Decoupled Flow Networks for Realistic Extreme Rescaling","authors":[{"name":"Jinmin Li","slug":"jinmin-li","given":"Jinmin","family":"Li","affiliation":"Tsinghua Shenzhen International Graduate School , Tsinghua University"},{"name":"Tao Dai","slug":"tao-dai","given":"Tao","family":"Dai","affiliation":"College of Computer Science and Software Engineering , Shenzhen University"},{"name":"Jingyun Zhang","slug":"jingyun-zhang","given":"Jingyun","family":"Zhang","affiliation":"WeChat Pay Lab33 , Tencent"},{"name":"Kang Liu","slug":"kang-liu","given":"Kang","family":"Liu","affiliation":"Tsinghua Shenzhen International Graduate School , Tsinghua University"},{"name":"Jun Wang","slug":"jun-wang","given":"Jun","family":"Wang","affiliation":"WeChat Pay Lab33 , Tencent"},{"name":"Shaoming Wang","slug":"shaoming-wang","given":"Shaoming","family":"Wang","affiliation":"WeChat Pay Lab33 , Tencent"},{"name":"Shu-Tao Xia","slug":"shu-tao-xia","given":"Shu-Tao","family":"Xia","affiliation":"Tsinghua Shenzhen International Graduate School , Tsinghua University; Research Center of Artificial Intelligence , Peng Cheng Laboratory"},{"name":"Rizen Guo","slug":"rizen-guo","given":"Rizen","family":"Guo","affiliation":"WeChat Pay Lab33 , Tencent"}],"abstract":"Recently developed generative methods, including invertible rescaling network (IRN) based and generative adversarial network (GAN) based methods, have demonstrated exceptional performance in image rescaling. However, IRN-based methods tend to produce over-smoothed results, while GAN-based methods easily generate fake details, which thus hinders their real applications. To address this issue, we propose  Boundary-aware Decoupled Flow Networks (BDFlow) to generate  realistic and visually pleasing results. Unlike previous  methods that model  high-frequency information  as standard Gaussian distribution directly, our BDFlow  first decouples the high-frequency information into semantic high-frequency that adheres to a Boundary distribution and non-semantic high-frequency counterpart that adheres to a Gaussian distribution. Specifically, to capture semantic high-frequency parts accurately,  we use Boundary-aware Mask (BAM) to constrain the model to produce rich textures, while  non-semantic high-frequency part is randomly sampled from a Gaussian distribution. Comprehensive experiments demonstrate that our BDFlow significantly outperforms other state-of-the-art methods while maintaining lower complexity. Notably, our BDFlow improves the PSNR by 4.4 dB and the SSIM by 0.1 on average over GRAIN, utilizing only 74% of the parameters and 20% of the computation. The code will be available at https://github.com/THU-Kingmin/BAFlow.","doi":"10.24963/ijcai.2024/110","url":"https://ijcai.org/proceedings/2024/110","pdf":"https://ijcai.org/proceedings/2024/0110.pdf","markdown":"https://ijcai.org/proceedings/2024/110.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/110","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":110,"pages":{"first":992,"last":1000},"keywords":["Computer Vision: CV: Image and video synthesis and generation","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Behrmann et al., 2019] Jens Behrmann, Will Grathwohl, Ricky TQ Chen, David Duvenaud, and Jörn-Henrik Jacob- sen. 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