Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915554779987968 |
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| author | Feijoo, Daniel Garrido-Mellado, Paula Conde, Marcos V. Rim, Jaesung Garcia, Alvaro Cho, Sunghyun Timofte, Radu |
| author_facet | Feijoo, Daniel Garrido-Mellado, Paula Conde, Marcos V. Rim, Jaesung Garcia, Alvaro Cho, Sunghyun Timofte, Radu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12788 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report Feijoo, Daniel Garrido-Mellado, Paula Conde, Marcos V. Rim, Jaesung Garcia, Alvaro Cho, Sunghyun Timofte, Radu Computer Vision and Pattern Recognition 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. |
| title | Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.12788 |