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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.09030 |
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| _version_ | 1866908951424008192 |
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| author | Qu, Lishen Liu, Yao Liang, Jie Zeng, Hui Dai, Wen Qin, Guanyi Guan, Ya-nan Zhou, Shihao Yang, Jufeng Zhang, Lei Timofte, Radu Yuan, Xiyuan Sun, Wanjie Li, Shihang Zhang, Bo Chen, Bin Lin, Jiannan Chen, Yuxu Gao, Qinquan Tong, Tong Gao, Song Tang, Jiacong Hu, Tao Ma, Xiaowen Yan, Qingsen Xu, Sunhan Wang, Juan Sun, Xinyu Qi, Lei Xu, He Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun |
| author_facet | Qu, Lishen Liu, Yao Liang, Jie Zeng, Hui Dai, Wen Qin, Guanyi Guan, Ya-nan Zhou, Shihao Yang, Jufeng Zhang, Lei Timofte, Radu Yuan, Xiyuan Sun, Wanjie Li, Shihang Zhang, Bo Chen, Bin Lin, Jiannan Chen, Yuxu Gao, Qinquan Tong, Tong Gao, Song Tang, Jiacong Hu, Tao Ma, Xiaowen Yan, Qingsen Xu, Sunhan Wang, Juan Sun, Xinyu Qi, Lei Xu, He Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun |
| contents | This paper presents NTIRE 2026, the 3rd Restore Any Image Model (RAIM) challenge on multi-exposure image fusion in dynamic scenes. We introduce a benchmark that targets a practical yet difficult HDR imaging setting, where exposure bracketing must be fused under scene motion, illumination variation, and handheld camera jitter. The challenge data contains 100 training sequences with 7 exposure levels and 100 test sequences with 5 exposure levels, reflecting real-world scenarios that frequently cause misalignment and ghosting artefacts. We evaluate submissions with a leaderboard score derived from PSNR, SSIM, and LPIPS, while also considering perceptual quality, efficiency, and reproducibility during the final review. This track attracted 114 participating teams and received 987 submissions. The winning methods significantly improved the ability to remove artifacts from multi-exposure fusion and recover fine details. The dataset and the code of each team can be found at the repository: https://github.com/qulishen/RAIM-HDR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09030 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2) Qu, Lishen Liu, Yao Liang, Jie Zeng, Hui Dai, Wen Qin, Guanyi Guan, Ya-nan Zhou, Shihao Yang, Jufeng Zhang, Lei Timofte, Radu Yuan, Xiyuan Sun, Wanjie Li, Shihang Zhang, Bo Chen, Bin Lin, Jiannan Chen, Yuxu Gao, Qinquan Tong, Tong Gao, Song Tang, Jiacong Hu, Tao Ma, Xiaowen Yan, Qingsen Xu, Sunhan Wang, Juan Sun, Xinyu Qi, Lei Xu, He Tu, Jiachen Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun Computer Vision and Pattern Recognition This paper presents NTIRE 2026, the 3rd Restore Any Image Model (RAIM) challenge on multi-exposure image fusion in dynamic scenes. We introduce a benchmark that targets a practical yet difficult HDR imaging setting, where exposure bracketing must be fused under scene motion, illumination variation, and handheld camera jitter. The challenge data contains 100 training sequences with 7 exposure levels and 100 test sequences with 5 exposure levels, reflecting real-world scenarios that frequently cause misalignment and ghosting artefacts. We evaluate submissions with a leaderboard score derived from PSNR, SSIM, and LPIPS, while also considering perceptual quality, efficiency, and reproducibility during the final review. This track attracted 114 participating teams and received 987 submissions. The winning methods significantly improved the ability to remove artifacts from multi-exposure fusion and recover fine details. The dataset and the code of each team can be found at the repository: https://github.com/qulishen/RAIM-HDR. |
| title | NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2) |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.09030 |