סמינר: Graduate Seminar
R3: Reconstruction, Raw, and Rain – Deraining Directly in the Bayer Domain
Lecturer:
Nathaniel Rothschild
Research Areas:
| Image reconstruction from corrupted images is crucial across many domains. Most reconstruction networks are trained on post-ISP sRGB images, even though the image-signal-processing pipeline irreversibly mixes colors, clips dynamic range and blurs fine detail. This paper uses the rain degradation problem as a ”use case” to show that these losses are avoidable and show that learning directly on raw Bayer mosaics yields superior reconstructions. To substantiate the claim we (i) evaluate post-ISP and Bayer reconstruction pipelines, (ii) curate RAW-RAIN, the first public benchmark of real rainy scenes captured in both 12-bit Bayer and bit-depth-matched sRGB, and (iii) introduce Information Conservation Score (ICS), a color-invariant metric that aligns more closely with human opinion than PSNR or SSIM. On the test split our raw-domain model improves sRGB results by up to +0.99dB PSNR and +1.2 % ICS, while running faster with half of the GFLOPs. The results advocate an ISP-last paradigm for low-level vision and open the door to end-to-end learnable camera pipelines.
M.Sc. student under the supervision of Prof. Avi Mendelson and Prof. Chaim Baskin.
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