R3: 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 ISP-lastparadigm for low-level vision and open the door to end-to-end learnablecamera pipelines.