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

• URL: http://arxiv.org/abs/2510.12788v1
• Date: Tue, 14 Oct 2025 17:57:04 GMT
• Title: Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report
• Authors: Daniel Feijoo, Paula Garrido-Mellado, Marcos V. Conde, Jaesung Rim, Alvaro Garcia, Sunghyun Cho, Radu Timofte,
• Abstract summary: This paper reviews the AIM 2025 Efficient Real-World Deblurring using Single Images Challenge.<n>The challenge is based on a new test set based on the well known RSBlur dataset.<n>The top-performing approach achieved a PSNR of 31.1298 dB, showcasing the potential of efficient methods in this domain.
• Score: 59.64053782297197
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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