71支队伍比拼单图去模糊，高效模型实现31.13dB性能
Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report
- 限定500万参数、200GMACs预算，用单图恢复真实模糊图像
- 冠军方案在新测试集上达31.13dB PSNR，逼近高效极限
- 适合关注轻量级图像修复与实际部署的研究者参考
本文回顾了AIM 2025高效单图真实世界去模糊挑战赛，旨在推动高效真实模糊图像复原技术发展。挑战基于知名RSBlur数据集构建的新测试集，采用双相机系统采集模糊与退化图像对。参赛者需在严格效率约束下提出解决方案：模型参数少于500万，计算量低于200GMACs。共71支队伍注册，最终4支团队提交有效成果。最优方案在测试集上达到31.1298dB的PSNR，展现了高效方法在该领域的潜力。本文全面梳理挑战流程，对比分析各参赛方案，为高效真实世界图像去模糊研究提供重要参考。
原文摘要 · Abstract (English)
This paper reviews the AIM 2025 Efficient Real-World Deblurring using Single Images Challenge, which aims to advance in efficient real-blur restoration. The challenge is based on a new test set based on the well known RSBlur dataset. Pairs of blur and degraded images in this dataset are captured using a double-camera system. Participant were tasked with developing solutions to effectively deblur these type of images while fulfilling strict efficiency constraints: fewer than 5 million model parameters and a computational budget under 200 GMACs. A total of 71 participants registered, with 4 teams finally submitting valid solutions. The top-performing approach achieved a PSNR of 31.1298 dB, showcasing the potential of efficient 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 in efficient real-world image deblurring.
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