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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.12089 |
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| _version_ | 1866918023705657344 |
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| author | Lee, Sangmin Park, Eunpil Canelo, Angel Park, Hyunhee Kim, Youngjo Chun, Hyung-Ju Jin, Xin Li, Chongyi Guo, Chun-Le Timofte, Radu Wu, Qi Qiu, Tianheng Dong, Yuchun Ding, Shenglin Pan, Guanghua Zhou, Weiyu Hu, Tao Feng, Yixu Dai, Duwei Cao, Yu Wu, Peng Dong, Wei Zhang, Yanning Yan, Qingsen Larsen, Simon J. Jiang, Ruixuan Xu, Senyan Wang, Xingbo Lu, Xin Conde, Marcos V. Abad-Hernandez, Javier Garcıa-Lara, Alvaro Feijoo, Daniel Garcıa, Alvaro Xiao, Zeyu Li, Zhuoyuan |
| author_facet | Lee, Sangmin Park, Eunpil Canelo, Angel Park, Hyunhee Kim, Youngjo Chun, Hyung-Ju Jin, Xin Li, Chongyi Guo, Chun-Le Timofte, Radu Wu, Qi Qiu, Tianheng Dong, Yuchun Ding, Shenglin Pan, Guanghua Zhou, Weiyu Hu, Tao Feng, Yixu Dai, Duwei Cao, Yu Wu, Peng Dong, Wei Zhang, Yanning Yan, Qingsen Larsen, Simon J. Jiang, Ruixuan Xu, Senyan Wang, Xingbo Lu, Xin Conde, Marcos V. Abad-Hernandez, Javier Garcıa-Lara, Alvaro Feijoo, Daniel Garcıa, Alvaro Xiao, Zeyu Li, Zhuoyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12089 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results Lee, Sangmin Park, Eunpil Canelo, Angel Park, Hyunhee Kim, Youngjo Chun, Hyung-Ju Jin, Xin Li, Chongyi Guo, Chun-Le Timofte, Radu Wu, Qi Qiu, Tianheng Dong, Yuchun Ding, Shenglin Pan, Guanghua Zhou, Weiyu Hu, Tao Feng, Yixu Dai, Duwei Cao, Yu Wu, Peng Dong, Wei Zhang, Yanning Yan, Qingsen Larsen, Simon J. Jiang, Ruixuan Xu, Senyan Wang, Xingbo Lu, Xin Conde, Marcos V. Abad-Hernandez, Javier Garcıa-Lara, Alvaro Feijoo, Daniel Garcıa, Alvaro Xiao, Zeyu Li, Zhuoyuan Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.12089 |