{"ID":2855911,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12788","arxiv_id":"2510.12788","title":"Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report","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.","short_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 s...","url_abs":"https://arxiv.org/abs/2510.12788","url_pdf":"https://arxiv.org/pdf/2510.12788v1","authors":"[\"Daniel Feijoo\",\"Paula Garrido-Mellado\",\"Marcos V. Conde\",\"Jaesung Rim\",\"Alvaro Garcia\",\"Sunghyun Cho\",\"Radu Timofte\"]","published":"2025-10-14T17:57:04Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}