NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results
Sangmin Lee, Eunpil Park, Ángel Canelo, Hyunhee Park, Youngjo Kim, Hyung-Ju Chun, Xin Jin, Chongyi Li, Chunle Guo, Radu Timofte, Qi Wu, Tianheng Qiu, Yuchun Dong, Shi‐Jin Ding, Guanghua Pan, Weiyu Zhou, Tao Hu, Yixu Feng, Duwei Dai, Yulian Cao, Peng Wu, Wei Dong, Yanning Zhang, Qingsen Yan, Simon J. Larsen, Ruixuan Jiang, Senyan Xu, Xingbo Wang, Xin Lu, Marcos V. Conde, Javier Abad-Hernández, Álvaro García-Lara, Daniel Feijoo, Álvaro García, Zeyu Xiao, Zhuoyuan Li
Abstract
This paper reviews the NTIRE 2025 Efficient Burst HDR and Restoration Challenge, which aims to advance efficient multi-frame high dynamic range (HDR) and restoration techniques. The challenge is based on a novel RAW multi-frame fusion dataset, comprising nine noisy and misaligned RAW frames with various exposure levels per scene. Participants were tasked with developing solutions capable of effectively fusing these frames while adhering to strict efficiency constraints: fewer than 30 million model parameters and a computational budget under 4.0 trillion FLOPs. A total of 217 participants registered, with six teams finally submitting valid solutions. The top-performing approach achieved a PSNR of 43.22 dB, showcasing the potential of novel methods in this domain. This paper provides a comprehensive overview of the challenge, compares the proposed solutions, and serves as a valuable reference for researchers and practitioners in efficient burst HDR and restoration.