解决动态场景下多曝光图像融合的对齐与伪影问题
NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2)
- 构建动态场景多曝光融合基准，模拟真实拍摄中的运动与光照变化
- 100组训练序列含7级曝光，100组测试序列含5级曝光，挑战对齐精度
- 胜出方案显著减少伪影并恢复细节，适合图像增强与HDR成像研究者
本文介绍NTIRE 2026年第三届任意图像修复模型（RAIM）挑战赛中关于动态场景下多曝光图像融合的赛题。该挑战针对实际且困难的高动态范围（HDR）成像场景，要求在场景运动、光照变化和手持相机抖动条件下完成曝光序列融合。数据集包含100组训练序列（每组7个曝光级别）和100组测试序列（每组5个曝光级别），真实再现导致错位与鬼影伪影的复杂情况。参赛作品通过PSNR、SSIM和LPIPS综合评分，并在最终评审中评估感知质量、效率与可复现性。本赛道共吸引114支团队参与，提交987份作品。优胜方法显著提升了多曝光融合的去伪影能力与细节恢复效果。数据集及各团队代码见：https://github.com/qulishen/RAIM-HDR。
原文摘要 · Abstract (English)
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.
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