NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results
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.
Cite
Text
Lee et al. "NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025.Markdown
[Lee et al. "NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025.](https://mlanthology.org/cvprw/2025/lee2025cvprw-ntire/)BibTeX
@inproceedings{lee2025cvprw-ntire,
title = {{NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results}},
author = {Lee, Sangmin and Park, Eunpil and Canelo, Angel and Park, Hyunhee and Kim, Youngjo and Chun, Hyung-Ju and Jin, Xin and Li, Chongyi and Guo, Chun-Le and Timofte, Radu and Wu, Qi and Qiu, Tianheng and Dong, Yuchun and Ding, Shenglin and Pan, Guanghua and Zhou, Weiyu and Hu, Tao and Feng, Yixu and Dai, Duwei and Cao, Yu and Wu, Peng and Dong, Wei and Zhang, Yanning and Yan, Qingsen and Larsen, Simon J. and Xu, Senyan and Wang, Xingbo and Jiang, Ruixuan and Lu, Xin and Conde, Marcos V. and Abad-Hernández, Javier and García-Lara, Álvaro and Feijoo, Daniel and García, Álvaro and Xiao, Zeyu and Li, Zhuoyuan},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2025},
pages = {1002-1017},
url = {https://mlanthology.org/cvprw/2025/lee2025cvprw-ntire/}
}