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RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report

Main:13 Pages
13 Figures
Bibliography:4 Pages
8 Tables
Abstract

Numerous low-level vision tasks operate in the RAW domain due to its linear properties, bit depth, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than the already large and public sRGB datasets. For this reason, many approaches try to generate realistic RAW images using sensor information and sRGB images. This paper covers the second challenge on RAW Reconstruction from sRGB (Reverse ISP). We aim to recover RAW sensor images from smartphones given the corresponding sRGB images without metadata and, by doing this, ``reverse" the ISP transformation. Over 150 participants joined this NTIRE 2025 challenge and submitted efficient models. The proposed methods and benchmark establish the state-of-the-art for generating realistic RAW data.

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@article{conde2025_2506.01947,
  title={ RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report },
  author={ Marcos V. Conde and Radu Timofte and Radu Berdan and Beril Besbinar and Daisuke Iso and Pengzhou Ji and Xiong Dun and Zeying Fan and Chen Wu and Zhansheng Wang and Pengbo Zhang and Jiazi Huang and Qinglin Liu and Wei Yu and Shengping Zhang and Xiangyang Ji and Kyungsik Kim and Minkyung Kim and Hwalmin Lee and Hekun Ma and Huan Zheng and Yanyan Wei and Zhao Zhang and Jing Fang and Meilin Gao and Xiang Yu and Shangbin Xie and Mengyuan Sun and Huanjing Yue and Jingyu Yang Huize Cheng and Shaomeng Zhang and Zhaoyang Zhang and Haoxiang Liang },
  journal={arXiv preprint arXiv:2506.01947},
  year={ 2025 }
}
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