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arXiv · 2510.12788

Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report

Daniel Feijoo·Paula Garrido-Mellado·Marcos V. Conde·Jaesung Rim·Alvaro Garcia·Sunghyun Cho·Radu Timofte
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Abstract

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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Daniel Feijoo, Paula Garrido-Mellado, Marcos V. Conde, Jaesung Rim, Alvaro Garcia, Sunghyun Cho, Radu Timofte. 2025-10-14. Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report. https://arxiv.org/abs/2510.12788

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