Benchmarking RAW and RGB Restoration in Image Signal Processors

Zihao Lu; Radu Timofte; Marcos V. Conde

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arXiv · 2609.02831

Benchmarking RAW and RGB Restoration in Image Signal Processors

Zihao Lu·Radu Timofte·Marcos V. Conde

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Abstract

Modern cameras transform RAW sensor measurements into sRGB images through an image signal processor (ISP). We benchmark two placements for blind restoration around a fixed ISP: (A) pre-ISP restoration in the RAW domain and (B) post-ISP restoration in the sRGB domain. The benchmark covers four smartphone device groups, two learned ISPs, three degradation regimes--noise, blur, and joint noise and blur--, and several representative RAW and RGB restoration models. Our results show that placement alone does not determine performance. The RAW restoration strategy outperforms the best generic RGB restoration models. However, RGB restoration models trained considering the ISP transformations, achieve the best overall performance. Our novel benchmark demonstrates that the image reconstruction performance strongly depends on the alignment between the restoration model and the target imaging pipeline. We consequently recommend reporting restoration placement and ISP-aware supervision as key experimental factors. Our code is available at https://github.com/mv-lab/AISP

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Zihao Lu, Radu Timofte, Marcos V. Conde. 2026-09-02. Benchmarking RAW and RGB Restoration in Image Signal Processors. https://arxiv.org/abs/2609.02831

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