高帧率运动模糊去噪挑战赛总结，展示9支团队的先进算法与新数据集成果。
AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results
- 基于新数据集MIORe，学习复杂运动模式的视觉特征进行去模糊
- 9支队伍提交有效方案，显著提升高帧率单图去模糊效果
- 适合关注视频去模糊、计算机视觉重建的开发者和研究者
本文全面回顾了AIM 2025高帧率非均匀运动去模糊挑战赛，介绍参赛方案与最终结果。挑战目标是识别能应对多样且复杂条件的有效网络，通过学习复杂的运动类型组合特征生成更清晰、视觉表现力更强的图像。共有68名参与者注册，9支团队提交了有效作品。论文系统评估了当前高帧率单图运动去模糊的前沿进展，展示了该领域的重要突破，并利用新数据集MIORe中的挑战性样本进行验证。
原文摘要 · Abstract (English)
This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions, by learning representative visual cues for complex aggregations of motion types. A total of 68 participants registered for the competition, and 9 teams ultimately submitted valid entries. This paper thoroughly evaluates the state-of-the-art advances in high-FPS single image motion deblurring, showcasing the significant progress in the field, while leveraging samples of the novel dataset, MIORe, that introduces challenging examples of movement patterns.
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