NTIRE 2024 Challenge on Bracketing Image Restoration and Enhancement: Datasets, Methods and Results

作者：Zhilu Zhang, Shuohao Zhang, Renlong Wu, Wangmeng Zuo, Radu Timofte, Xiaoxia Xing, Hyun‐Hee Park, Sejun Song, Changho Kim, Xiangyu Kong, Jinlong Wu, Jianxing Zhang, Jingfan Tan, Zikun Liu, Wenhan Luo, Wenjie Lin, Chengzhi Jiang, Mingyan Han, Zhen Liu, Ting Jiang, Jinting Luo, Cheng Shen, Linze Li, Xinhan Niu, Shuaicheng Liu, Kexin Dai, Kangzhen Yang, Tao Hu, Xiangyu Chen, Yu Cao, Qingsen Yan, Yanning Zhang, Genggeng Chen, Yongqing Yang, Wei Dong, Xinwei Dai, Yuanbo Zhou, Xintao Qiu, Hui Tang, Wei Deng, Qingquan Gao, Tong Tong, Peng Zhang, Yifei Chen, Wenbo Xiong, Zhijun Song, Pu Cheng, Taolue Feng, Yunqing He, Daiguo Zhou, Ying Huang, Xiaowen Ma, Peng Wu · 年份：2024 · DOI：10.1109/cvprw63382.2024.00620 · 被引用次数：32 · 研究领域：Advanced Image Processing Techniques

Low-light photography presents significant challenges. Multi-image processing methods have made numerous attempts to obtain high-quality photos, yet remain unsatisfactory. Recently, bracketing image restoration and enhancement has received increased attention. By leveraging the full potential of multi-exposure images, several tasks (including denoising, deblurring, high dynamic range enhancement, and super-resolution) can be jointly addressed. This paper reviews the NTIRE 2024 challenge on bracketing image restoration and enhancement. In the challenge, participants are required to process multi-exposure RAW images to generate noise-free, blur-free, high dynamic range, and even higher-resolution RAW images. The challenge comprises two tracks. Track 1 does not incorporate the super-resolution task, whereas Track 2 does. Each track featured five teams participating in the final testing phase. The proposed methods establish new state-of-the-art performance benchmarks.