Edit-aware RAW Reconstruction

• URL: http://arxiv.org/abs/2512.05859v1
• Date: Fri, 05 Dec 2025 16:35:24 GMT
• Title: Edit-aware RAW Reconstruction
• Authors: Abhijith Punnappurath, Luxi Zhao, Ke Zhao, Hue Nguyen, Radek Grzeszczuk, Michael S. Brown,
• Abstract summary: Existing RAW reconstruction methods can recover RAW data from sRGB images, but these approaches are typically optimized for pixel-wise RAW reconstruction fidelity.<n>We introduce a plug-and-play, edit-aware loss function that can be integrated into any existing RAW reconstruction framework.<n>Our loss improves sRGB reconstruction quality by up to 1.5-2 dB PSNR across various editing conditions.
• Score: 20.003629187239024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Users frequently edit camera images post-capture to achieve their preferred photofinishing style. While editing in the RAW domain provides greater accuracy and flexibility, most edits are performed on the camera's display-referred output (e.g., 8-bit sRGB JPEG) since RAW images are rarely stored. Existing RAW reconstruction methods can recover RAW data from sRGB images, but these approaches are typically optimized for pixel-wise RAW reconstruction fidelity and tend to degrade under diverse rendering styles and editing operations. We introduce a plug-and-play, edit-aware loss function that can be integrated into any existing RAW reconstruction framework to make the recovered RAWs more robust to different rendering styles and edits. Our loss formulation incorporates a modular, differentiable image signal processor (ISP) that simulates realistic photofinishing pipelines with tunable parameters. During training, parameters for each ISP module are randomly sampled from carefully designed distributions that model practical variations in real camera processing. The loss is then computed in sRGB space between ground-truth and reconstructed RAWs rendered through this differentiable ISP. Incorporating our loss improves sRGB reconstruction quality by up to 1.5-2 dB PSNR across various editing conditions. Moreover, when applied to metadata-assisted RAW reconstruction methods, our approach enables fine-tuning for target edits, yielding further gains. Since photographic editing is the primary motivation for RAW reconstruction in consumer imaging, our simple yet effective loss function provides a general mechanism for enhancing edit fidelity and rendering flexibility across existing methods.

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