Adaptive suppression of CCD signal-dependent noise in light space

We propose a denoising scheme to restore images degraded by CCD noise. Typically, restoration algorithms assume a linear mapping between the incident light space and image space. However, in practice a camera response function performs a non-linear mapping on the sensor output and as a result the sensor noise model becomes more complex in the image space. In this paper, we correct for non-linearity by mapping the corrupted image into "light space", where the relationship between the incident light and light space values is linear. To reduce the sensor noise we accurately model the CCD sensor noise by using the Photon Transfer Curve. We then develop a combination of adaptive filters based on the estimated noise model in light space. Our adaptive system demonstrates efficient noise removal performance in uniform regions, while preserving edges and fine details.