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

Daniel Feijoo, Paula Garrido-Mellado, Marcos V. Conde, Jaesung Rim, Alvaro Garcia +2 more
10/14/2025
cs.CV

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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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Cite this paper

@article{feijoo2025efficient,
  title  = {Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report},
  author = {Daniel Feijoo and Paula Garrido-Mellado and Marcos V. Conde and Jaesung Rim and Alvaro Garcia and Sunghyun Cho and Radu Timofte},
  year   = {2025},
  eprint = {2510.12788},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.12788v1}
}

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