Adaptive suppression of CCD signal-dependent noise in light space

Abstract

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.

True Images: A Calibration Technique to Reproduce Images as Recorded

“…Thus, filtering operations will benefit from operations in a linear domain (or at least be closer to the physical counterpart). This is shown quite clearly in [3].…”

Image informative maps for component-wise estimating parameters of signal-dependent noise

“…16 and references therein) or signal-dependent Poisson noise [typical for charge-coupled device (CCD) sensors]. [17][18][19][20][21][22][23][24][25][26][27][28] A threshold that is a function of noise standard deviation can be used with an edge detector. 13 In Refs.…”