mlsanityData to 14 Oct 2026

NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge

arXiv 2405.09923 ↗First posted 16 May 2024cs.CV eess.IVcited by 9

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Abstract (arXiv)

In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, including real-world images with/without reference ground truth in various scenarios from real applications. The participants were required to restore the real-captured images from complex and unknown degradation, where generative perceptual quality and fidelity are desired in the restoration result. The challenge consisted of two tasks. Task one employed real referenced data pairs, where quantitative evaluation is available. Task two used unpaired images, and a comprehensive user study was conducted. The challenge attracted more than 200 registrations, where 39 of them submitted results with more than 400 submissions. Top-ranked methods improved the state-of-the-art restoration performance and obtained unanimous recognition from all 18 judges. The proposed datasets are available at https://drive.google.com/file/d/1DqbxUoiUqkAIkExu3jZAqoElr_nu1IXb/view?usp=sharing and the homepage of this challenge is at https://codalab.lisn.upsaclay.fr/competitions/17632.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 41 of its 57 references that are in the map. First posted on arXiv 16 May 2024.

Builds on

41 of 57 references.

References · 41

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29 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 1.

9 papers.

  1. 2024Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolutioncited by 26
  2. 2025Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Trainingcited by 21
  3. 2025NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challengecited by 5
  4. 2024NTIRE 2024 Challenge on Night Photography Renderingcited by 4
  5. 2024OmniLens: Towards Universal Lens Aberration Correction via LensLib-to-Specific Domain Adaptationcited by 4
  6. 2025InstructRestore: Region-Customized Image Restoration with Human Instructionscited by 1
  7. 2024NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Resultscited by 0
  8. 2025EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolutioncited by 0
  9. 2026Towards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysiscited by 0