{"type":"ScholarlyArticle","title":"From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement","authors":[{"name":"Wanyu Wu","slug":"wanyu-wu","given":"Wanyu","family":"Wu","affiliation":"Wuhan University of Science and Technology"},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":"Wuhan University of Science and Technology"},{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":"Wuhan University"},{"name":"Kui Jiang","slug":"kui-jiang","given":"Kui","family":"Jiang","affiliation":"Harbin Institute of Technology"},{"name":"Xin Xu","slug":"xin-xu","given":"Xin","family":"Xu","affiliation":"Wuhan University of Science and Technology"}],"abstract":"Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images. However, these methods have limited exploration in another major visibility damage, the glow effects in real night scenes. Glow effects are inevitable in the presence of artificial light sources and cause further diffused blurring when directly enhanced. To settle this issue, we innovatively consider the glow suppression task as learning physical glow generation via multiple scattering estimation according to the Atmospheric Point Spread Function (APSF). In response to the challenges posed by uneven glow intensity and varying source shapes, an APSF-based Nighttime Imaging Model with Near-field Light Sources (NIM-NLS) is specifically derived to design a scalable Light-aware Blind Deconvolution Network (LBDN). The glow-suppressed result is then brightened via a Retinex-based Enhancement Module (REM). Remarkably, the proposed glow suppression method is based on zero-shot learning and does not rely on any paired or unpaired training data. Empirical evaluations demonstrate the effectiveness of the proposed method in both glow suppression and low-light enhancement tasks.","doi":"10.24963/ijcai.2023/170","url":"https://ijcai.org/proceedings/2023/170","pdf":"https://ijcai.org/proceedings/2023/0170.pdf","markdown":"https://ijcai.org/proceedings/2023/170.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/170","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":170,"pages":{"first":1533,"last":1541},"keywords":["Computer Vision: CV: Machine learning for vision","Computer Vision: CV: Computational photography","Computer Vision: CV: Segmentation"],"videos":[],"references":[{"position":1,"raw":"[Almeida and Almeida, 2009] Mariana SC Almeida and Luis B Almeida. 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