Why Low-Light Cameras Go Color Blind: Removing Color Bias in Raw Denoising
Raw images inherently suffer from noise due to the stochastic nature of light and sensor hardware imperfections. As real photon counts fall, the ratio of this noise to the signal degrades; consequently, for low-light conditions, robust denoising is especially vital for high-quality results. While recent data-driven methods achieve strong performance, they typically rely on large-scale noisy-clean image pairs that are costly and difficult to collect. Alternatively, parametric noise models can generate synthetic training data, but this necessitates precise camera calibration, which is often impractical for unknown devices. In this work, we propose a camera-agnostic, calibration-free paradig...
| Authors | Mohammad Mohammadi, Sina Honari, Stavros Tsogkas et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
Models and datasets referencing this paper
- MhdMohammadi/SIDD-CCDataset
- MhdMohammadi/SIDD-CC-DNGDataset
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