{"type":"ScholarlyArticle","title":"ALL-E: Aesthetics-guided Low-light Image Enhancement","authors":[{"name":"Ling Li","slug":"ling-li","given":"Ling","family":"Li","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"},{"name":"Dong Liang","slug":"dong-liang","given":"Dong","family":"Liang","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"},{"name":"Yuanhang Gao","slug":"yuanhang-gao","given":"Yuanhang","family":"Gao","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"},{"name":"Sheng-Jun Huang","slug":"sheng-jun-huang","given":"Sheng-Jun","family":"Huang","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"},{"name":"Songcan Chen","slug":"songcan-chen","given":"Songcan","family":"Chen","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"}],"abstract":"Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training enhancement models. In this paper, we propose a new paradigm, i.e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward. Each pixel, functioning as an agent, refines itself by recursive actions, i.e., its corresponding adjustment curve is estimated sequentially. Extensive experiments show that integrating aesthetic assessment improves both subjective experience and objective evaluation. Our results on various benchmarks demonstrate the superiority of ALL-E over state-of-the-art methods. Source code: https://dongl-group.github.io/project pages/ALLE.html","doi":"10.24963/ijcai.2023/118","url":"https://ijcai.org/proceedings/2023/118","pdf":"https://ijcai.org/proceedings/2023/0118.pdf","markdown":"https://ijcai.org/proceedings/2023/118.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/118","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":118,"pages":{"first":1062,"last":1070},"keywords":["Computer Vision: CV: Computational photography","AI Ethics, Trust, Fairness: ETF: Societal impact of AI","Humans and AI: HAI: Personalization and user modeling"],"videos":[],"references":[{"position":1,"raw":"[Buchsbaum, 1980] Gershon Buchsbaum. A spatial proces- sor model for object colour perception. Journal of the Franklin institute, 1980.","title":"A spatial processor model for object colour perception","authors":"Gershon Buchsbaum","year":"1980","venue":"Journal of the Franklin institute","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cho et al., 2020] Se Woon Cho, Na Rae Baek, Ja Hyung Koo, Muhammad Arsalan, and Kang Ryoung Park. Se- mantic segmentation with low light images by modi- fied cyclegan-based image enhancement. IEEE Access, 8:93561-93585, 2020.","title":"Semantic segmentation with low light images by modified cyclegan-based image enhancement","authors":"Na Rae Se Woon Cho, Ja Hyung Baek, Muhammad Koo, Kang Ryoung Arsalan, Park","year":"2020","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Deng et al., 2018] Yubin Deng, Chen Change Loy, and Xi- aoou Tang. Aesthetic-driven image enhancement by ad- versarial learning. In ACM International Conference on Multimedia, pages 870-878, 2018.","title":"Aesthetic-driven image enhancement by adversarial learning","authors":"Yubin Deng, Chen Change Loy, Xiaoou Tang","year":"2018","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Esfandarani and Milanfar, 2018] Hossein Talebi Esfan- darani and Peyman Milanfar. Nima: Neural image assessment. IEEE Transactions on Image Processing, 27(8):3998-4011, 2018.","title":"Nima: Neural image assessment","authors":"Hossein Talebi, Esfandarani, Peyman Milanfar","year":"2018","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Fan et al., 2020] Minhao Fan, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Integrating semantic segmenta- tion and retinex model for low-light image enhancement. In ACM International Conference on Multimedia, Virtual Event, pages 2317-2325, 2020.","title":"Integrating semantic segmentation and retinex model for low-light image enhancement","authors":"Fan","year":"2020","venue":"ACM International Conference on Multimedia, Virtual Event","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Furuta et al., 2019] Ryosuke Furuta, Naoto Inoue, and Toshihiko Yamasaki. Fully convolutional network with multi-step reinforcement learning for image processing. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 33, pages 3598-3605, 2019.","title":"Fully convolutional network with multi-step reinforcement learning for image processing","authors":"Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Guo et al., 2016] Xiaojie Guo, Yu Li, and Haibin Ling. Lime: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, pages 982-993, 2016.","title":"Lime: Low-light image enhancement via illumination map estimation","authors":"Xiaojie Guo, Yu Li, Haibin Ling","year":"2016","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Guo et al., 2020] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In CVPR, pages 1780-1789, 2020.","title":"Zero-reference deep curve estimation for low-light image enhancement","authors":"Guo","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hosu et al., 2019] Vlad Hosu, Bastian Goldlucke, and Diet- mar Saupe. Effective aesthetics prediction with multi-level spatially pooled features. In CVPR, pages 9375-9383, 2019.","title":"Effective aesthetics prediction with multi-level spatially pooled features","authors":"Vlad Hosu, Bastian Goldlucke, Dietmar Saupe","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jiang et al., 2021] Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. Enlightengan: Deep light en- hancement without paired supervision. IEEE Transactions on Image Processing, pages 2340-2349, 2021.","title":"Enlightengan: Deep light enhancement without paired supervision","authors":"Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang","year":"2021","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jobson et al., 1997] Jobson, Daniel, J., Rahman, Zia-ur, Woodell, Glenn, and A. A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing, 1997.","title":"A multiscale retinex for bridging the gap between color images and the human observation of scenes","authors":"Daniel Jobson, J Rahman, Zia-Ur Woodell, Glenn, A","year":"1997","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kang et al., 2020] Chen Kang, Giuseppe Valenzise, and Frédéric Dufaux. Eva: An explainable visual aesthetics dataset. In Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends, pages 5-13, 2020.","title":"Eva: An explainable visual aesthetics dataset","authors":"Chen Kang, Giuseppe Valenzise, Frédéric Dufaux","year":"2020","venue":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kao et al., 2017] Yueying Kao, Ran He, and Kaiqi Huang. Deep aesthetic quality assessment with semantic in- formation. IEEE Transactions on Image Processing, 26(3):1482-1495, 2017.","title":"Deep aesthetic quality assessment with semantic information","authors":"Kao","year":"2017","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kong et al., 2016] Shu Kong, Xiaohui Shen, Zhe Lin, Radomir Mech, and Charless Fowlkes. Photo aesthetics ranking network with attributes and content adaptation. In European Conference on Computer Vision, pages 662- 679. Springer, 2016.","title":"Photo aesthetics ranking network with attributes and content adaptation","authors":"Shu Kong, Xiaohui Shen, Zhe Lin, Radomir Mech, Charless Fowlkes","year":"2016","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"Edwin H Land. The retinex theory of color vi- sion. Scientific american, pages 108-129, 1977.","title":"The retinex theory of color vision","authors":"H Edwin, Land","year":"1977","venue":"Scientific american","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lee et al., 2012] Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation. In IEEE International Conference on Im- age Processing, pages 965-968, 2012.","title":"Contrast enhancement based on layered difference representation","authors":"Lee","year":"2012","venue":"IEEE International Conference on Image Processing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2020] Leida Li, Hancheng Zhu, Sicheng Zhao, Guiguang Ding, and Weisi Lin. Personality-assisted multi- task learning for generic and personalized image aesthet- ics assessment. IEEE Transactions on Image Processing, page 3898-3910, Feb 2020.","title":"Personality-assisted multitask learning for generic and personalized image aesthetics assessment","authors":"Leida Li, Hancheng Zhu, Sicheng Zhao, Guiguang Ding, Weisi Lin","year":"2020","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liang et al., 2022] Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, and Huiyu Zhou. Semantically contrastive learning for low- light image enhancement. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 1555-1563, 2022.","title":"Semantically contrastive learning for lowlight image enhancement","authors":"Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, Huiyu Zhou","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liu et al., 2021] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In CVPR, pages 10561-10570, 2021.","title":"Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement","authors":"Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, Zhongxuan Luo","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lore et al., 2017] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. Llnet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recogni- tion, pages 650-662, 2017.","title":"Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. Llnet: A deep autoencoder approach to natural low-light image enhancement","authors":"Lore","year":"2017","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lu et al., 2014] Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, and James Z Wang. Rapid: Rating pictorial aesthetics us- ing deep learning. In ACM International Conference on Multimedia, pages 457-466, 2014.","title":"Rapid: Rating pictorial aesthetics using deep learning","authors":"Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, James Z Wang","year":"2014","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Lu et al., 2015] Xin Lu, Zhe Lin, Xiaohui Shen, Radomir Mech, and James Z Wang. Deep multi-patch aggregation network for image style, aesthetics, and quality estimation. In Proceedings of the IEEE international conference on computer vision, pages 990-998, 2015.","title":"Deep multi-patch aggregation network for image style, aesthetics, and quality estimation","authors":"Xin Lu, Zhe Lin, Xiaohui Shen, Radomir Mech, James Z Wang","year":"2015","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ma et al., 2015] Kede Ma, Kai Zeng, and Zhou Wang. Per- ceptual quality assessment for multi-exposure image fu- sion. IEEE Transactions on Image Processing, pages 3345-3356, 2015.","title":"Perceptual quality assessment for multi-exposure image fusion","authors":"Ma","year":"2015","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Ma et al., 2017] Shuang Ma, Jing Liu, and Chang-Wen Chen. A-lamp: Adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assess- ment. In CVPR, pages 722-731, 07 2017.","title":"A-lamp: Adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assessment","authors":"Shuang Ma, Jing Liu, Chang-Wen Chen","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ma et al., 2022] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and ro- bust low-light image enhancement. In CVPR, pages 5637- 5646, 2022.","title":"Toward fast, flexible, and robust low-light image enhancement","authors":"Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, Zhongxuan Luo","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Mittal et al., 2013] Anish Mittal, R. Soundararajan, and A. Bovik. Making a \"completely blind\" image quality an- alyzer. IEEE Signal Processing Letters, pages 209-212, 2013.","title":"Making a \"completely blind\" image quality analyzer","authors":"R Anish Mittal, A Soundararajan, Bovik","year":"2013","venue":"IEEE Signal Processing Letters","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Mnih et al., 2016] Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asyn- chronous methods for deep reinforcement learning. In International Conference on Machine Learning, pages 1928-1937. PMLR, 2016.","title":"Asynchronous methods for deep reinforcement learning","authors":"Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu","year":"2016","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Murray et al., 2012] Naila Murray, Luca Marchesotti, and Florent Perronnin. Ava: A large-scale database for aes- thetic visual analysis. In CVPR, pages 2408-2415. IEEE, 2012.","title":"Ava: A large-scale database for aesthetic visual analysis","authors":"Naila Murray, Luca Marchesotti, Florent Perronnin","year":"2012","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Park et al., 2018] Jongchan Park, Joon-Young Lee, Donggeun Yoo, and In So Kweon. Distort-and-recover: Color enhancement using deep reinforcement learning. In CVPR, pages 5928-5936, 2018.","title":"Jongchan Park, Joon-Young Lee, Donggeun Yoo, and In So Kweon. Distort-and-recover: Color enhancement using deep reinforcement learning","authors":"Park","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Pizer et al., 1990] S. Pizer, R. Johnston, J. P. Ericksen, B. Yankaskas, and K. Muller. Contrast-limited adaptive histogram equalization: speed and effectiveness. Con- ference on Visualization in Biomedical Computing, pages 337-345, 1990.","title":"Contrast-limited adaptive histogram equalization: speed and effectiveness","authors":"S Pizer, R Johnston, J P Ericksen, B Yankaskas, K Muller","year":"1990","venue":"Conference on Visualization in Biomedical Computing","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Ren et al., 2017] Jian Ren, Xiaohui Shen, Zhe Lin, Radomir Mech, and David J. Foran. Personalized image aesthetics. In 2017 IEEE International Conference on Computer Vi- sion (ICCV), Dec 2017.","title":"Personalized image aesthetics","authors":"Jian Ren, Xiaohui Shen, Zhe Lin, Radomir Mech, David J Foran","year":"2017","venue":"IEEE International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ren et al., 2019] Wenqi Ren, Sifei Liu, Lin Ma, Qianqian Xu, Xiangyu Xu, Xiaochun Cao, Junping Du, and Ming- Hsuan Yang. Low-light image enhancement via a deep hybrid network. IEEE Transactions on Image Processing, pages 4364-4375, 2019.","title":"Low-light image enhancement via a deep hybrid network","authors":"Sifei Wenqi Ren, Lin Liu, Qianqian Ma, Xiangyu Xu, Xiaochun Xu, Junping Cao, Ming-Hsuan Du, Yang","year":"2019","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang et al., 2013] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algo- rithm for non-uniform illumination images. IEEE Trans- actions on Image Processing, pages 3538-3548, 2013.","title":"Naturalness preserved enhancement algorithm for non-uniform illumination images","authors":"Shuhang Wang, Jin Zheng, Hai-Miao Hu, Bo Li","year":"2013","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2018] Guolong Wang, Junchi Yan, and Zheng Qin. Collaborative and attentive learning for personalized image aesthetic assessment. In Proceedings of the Twenty- Seventh International Joint Conference on Artificial Intel- ligence, Jul 2018.","title":"Collaborative and attentive learning for personalized image aesthetic assessment","authors":"Wang","year":"2018","venue":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/133"},{"position":35,"raw":"[Wei et al., 2018] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retinex decomposition for low- light enhancement. In British Machine Vision Conference, 2018.","title":"Deep retinex decomposition for lowlight enhancement","authors":"Chen Wei, Wenjing Wang, Wenhan Yang, Jiaying Liu","year":"2018","venue":"British Machine Vision Conference","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wu et al., 2022] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex- net: Retinex-based deep unfolding network for low-light image enhancement. In CVPR, pages 5901-5910, 2022.","title":"Uretinexnet: Retinex-based deep unfolding network for low-light image enhancement","authors":"Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, Jianmin Jiang","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Xu et al., 2020] Ke Xu, Xin Yang, Baocai Yin, and Ryn- son W.H. Lau. Learning to restore low-light images via decomposition-and-enhancement. In CVPR, 2020.","title":"Learning to restore low-light images via decomposition-and-enhancement","authors":"Ke Xu, Xin Yang, Baocai Yin, Rynson W H Lau","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yang et al., 2022] Yuzhe Yang, Liwu Xu, Leida Li, Nan Qie, Yaqian Li, Peng Zhang, and Yandong Guo. Person- alized image aesthetics assessment with rich attributes. In CVPR, pages 19861-19869, 2022.","title":"Person-alized image aesthetics assessment with rich attributes","authors":"Yuzhe Yang, Liwu Xu, Leida Li, Nan Qie, Yaqian Li, Peng Zhang, Yandong Guo","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yu and Moon, 2004] So-Young Yu and Sung-Been Moon. An exploratory study of image retrieval using aesthetic im- pressions. Journal of the Korean Society for information Management, 21(4):187-208, 2004.","title":"So-Young Yu and Sung-Been Moon. An exploratory study of image retrieval using aesthetic impressions","authors":"Moon Yu","year":"2004","venue":"Journal of the Korean Society for information Management","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Yu et al., 2018] Ke Yu, Chao Dong, Liang Lin, and Chen Change Loy. Crafting a toolchain for image restora- tion by deep reinforcement learning. In CVPR, pages 2443-2452, 2018.","title":"Crafting a toolchain for image restoration by deep reinforcement learning","authors":"Ke Yu, Chao Dong, Liang Lin, Chen Change Loy","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yu et al., 2021a] Jing Yu, Lulu Zhao, et al. A novel deep cnn method based on aesthetic rule for user preferential images recommendation. Journal of Applied Science and Engineering, 24(1):49-55, 2021.","title":"A novel deep cnn method based on aesthetic rule for user preferential images recommendation","authors":"Yu","year":"2021","venue":"Journal of Applied Science and Engineering","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yu et al., 2021b] Jun Yu, Xinlong Hao, and Peng He. Single-stage face detection under extremely low-light con- ditions. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pages 3523-3532, October 2021.","title":"Single-stage face detection under extremely low-light conditions","authors":"Yu","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2019] Yonghua Zhang, Jiawan Zhang, and Xi- aojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACM International Conference on Multimedia, page 1632-1640, 2019.","title":"Kindling the darkness: A practical low-light image enhancer","authors":"Yonghua Zhang, Jiawan Zhang, Xiaojie Guo","year":"2019","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2021a] Rongkai Zhang, Lanqing Guo, Siyu Huang, and Bihan Wen. Rellie: Deep reinforcement learn- ing for customized low-light image enhancement. In ACM International Conference on Multimedia, pages 2429- 2437, 2021.","title":"Rellie: Deep reinforcement learning for customized low-light image enhancement","authors":"Zhang","year":"2021","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2021b] Weixia Zhang, Kede Ma, Guangtao Zhai, and Xiaokang Yang. Uncertainty-aware blind image quality assessment in the laboratory and wild. IEEE Trans- actions on Image Processing, pages 3474-3486, 2021.","title":"Uncertainty-aware blind image quality assessment in the laboratory and wild","authors":"Zhang","year":"2021","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhao et al., 2020] Lin Zhao, Meimei Shang, Fei Gao, Rongsheng Li, Fei Huang, and Jun Yu. Representation learning of image composition for aesthetic prediction. Computer Vision and Image Understanding, 199:103024, 2020.","title":"Representation learning of image composition for aesthetic prediction","authors":"Lin Zhao, Meimei Shang, Fei Gao, Rongsheng Li, Fei Huang, Jun Yu","year":"2020","venue":"Computer Vision and Image Understanding","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhao et al., 2021] Lin Zhao, Shao-Ping Lu, Tao Chen, Zhenglu Yang, and Ariel Shamir. Deep symmetric net- work for underexposed image enhancement with recurrent attentional learning. In Proceedings of the IEEE/CVF in- ternational conference on computer vision, pages 12075- 12084, 2021.","title":"Deep symmetric network for underexposed image enhancement with recurrent attentional learning","authors":"Lin Zhao, Shao-Ping Lu, Tao Chen, Zhenglu Yang, Ariel Shamir","year":"2021","venue":"Proceedings of the IEEE/CVF international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhu et al., 2020] Hancheng Zhu, Leida Li, Jinjian Wu, Sicheng Zhao, Guiguang Ding, and Guangming Shi. Per- sonalized image aesthetics assessment via meta-learning with bilevel gradient optimization. IEEE Transactions on Cybernetics, 52(3):1798-1811, Jun 2020.","title":"Personalized image aesthetics assessment via meta-learning with bilevel gradient optimization","authors":"Hancheng Zhu, Leida Li, Jinjian Wu, Sicheng Zhao, Guiguang Ding, Guangming Shi","year":"2020","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}