NTIRE 2025 Image Shadow Removal Challenge Report
Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou, Cailian Chen, Zongwei Wu, Radu Timofte, Mingjia Li, Jin Hu, Hainuo Wang, Hengxing Liu, Jiarui Wang, Qiming Hu, Xiaojie Guo, Xin Lu, Jiarong Yang, Yuanfei Bao, Anya Hu, Zihao Fan, Kunyu Wang, Jie Xiao, Xi Wang, Xueyang Fu
TL;DR
The NTIRE 2025 Shadow Removal Challenge evaluated 17 teams on shadow removal techniques focusing on fidelity and perceptual quality, using diverse datasets to benchmark current state-of-the-art methods.
Contribution
This report presents the results of the NTIRE 2025 Shadow Removal Challenge, highlighting advancements and benchmarking progress in shadow removal methods.
Findings
17 teams submitted solutions
Two evaluation tracks: fidelity and perception
Diverse dataset used for comprehensive assessment
Abstract
This work examines the findings of the NTIRE 2025 Shadow Removal Challenge. A total of 306 participants have registered, with 17 teams successfully submitting their solutions during the final evaluation phase. Following the last two editions, this challenge had two evaluation tracks: one focusing on reconstruction fidelity and the other on visual perception through a user study. Both tracks were evaluated with images from the WSRD+ dataset, simulating interactions between self- and cast-shadows with a large number of diverse objects, textures, and materials.
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Taxonomy
TopicsAdvanced Optical Sensing Technologies · Laser-induced spectroscopy and plasma · Advanced X-ray and CT Imaging