$\mathbf{R}^3$: Reconstruction, Raw, and Rain: Deraining Directly in the Bayer Domain

Image reconstruction from corrupted (rain) images is crucial across many domains. Most deraining networks are trained on post‑ISP RGB images, eventhough the image‑signal‑processing pipeline irreversibly mixes colors,clips dynamic range and blurs fine detail. This paper indicates that these lossesare avoidable and show that learning directly on raw Bayermosaics yields superior reconstructions from a single camera.To substantiate the claim we (i) curate Raw‑Rain, the firstpublic benchmark of real rainy scenes captured in both 12‑bit Bayer andbit‑depth‑matched sRGB, (ii) design a lightweight U‑Net that ingests thesingle‑channel Bayer tensor, and (iii) introduce InformationConservation Score (ICS}, a color‑invariant metric that aligns moreclosely with human opinion than PSNR or SSIM. On the test split ourraw‑domain model improves RGB results by up to +0.99 dB PSNR and +1.2 \% ICS, while running faster with half of the GFLOPs. The results advocate an \emph{ISP‑last}paradigm for low‑level vision and open the door to end‑to‑end learnablecamera pipelines.