RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report
Marcos V. Conde, Radu Timofte, Radu Berdan, Beril Besbinar, Daisuke Iso, Pengzhou Ji, Xiong Dun, Zeying Fan, Chen Wu, Zhansheng Wang, Pengbo Zhang, Jiazi Huang, Qinglin Liu, Wei Yu, Shengping Zhang, Xiangyang Ji, Kyungsik Kim, Minkyung Kim, Hwalmin Lee, Hekun Ma, Huan Zheng

TL;DR
This paper reports on the NTIRE 2025 challenge where over 150 teams developed models to reconstruct RAW sensor images from sRGB images on smartphones, advancing the state-of-the-art in reverse ISP tasks.
Contribution
It introduces a large-scale challenge with new benchmark data for RAW reconstruction from sRGB images, fostering progress in this underexplored area.
Findings
Established new state-of-the-art models for RAW reconstruction
Demonstrated the effectiveness of challenge-based benchmarking
Provided insights into model efficiency and accuracy trade-offs
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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Taxonomy
TopicsIndustrial Vision Systems and Defect Detection · Image Processing and 3D Reconstruction · Digital Image Processing Techniques
