FiberR2R: RGB-to-RAW Reconstruction with Learned Forward ISP Sensitivity Guidance

RGB-to-RAW reconstruction aims to infer sensor-domain RAW measurements from RGB images, but the image signal processor (ISP) can apply lossy transformations that make the corresponding RAW measurements difficult to recover. To address this challenge, we propose FiberR2R, a three-stage RGB-to-RAW reconstruction framework with learned forward ISP sensitivity guidance. FiberR2R first learns a differentiable forward ISP model from paired RAW–RGB data, then predicts an RGB-conditioned RAW Anchor, and finally trains a residual refiner around the Anchor. We use squared vector–Jacobian product (VJP) responses to provide estimates of the local RGB response energy of the learned forward ISP. These scores jointly parameterize Anchor-centered perturbation sampling and complementary residual supervision. At inference, FiberR2R predicts the Anchor RAW and applies one bounded residual refinement without evaluating the learned forward ISP or its VJPs. Across five camera-specific datasets, FiberR2R has the highest SSIM on all five datasets and the highest PSNR on four, with only 11.51M parameters and 10.38 GFLOPs. These results support learned forward ISP sensitivity as an effective training guidance for RGB-to-RAW reconstruction task.