Clipped noisy images: Heteroskedastic modeling and practical denoising

We study the denoising of signals from clipped noisy observations, such as digital images of an under- or over-exposed scene. From a precise mathematical formulation and analysis of the problem, we derive a set of homomorphic transformations that enable the use of existing denoising algorithms for non-clipped data (including arbitrary denoising filters for additive independent and identically distributed, i.i.d., Gaussian noise). Our results have general applicability and can be “plugged” into current filtering implementations, to enable a more accurate and better processing of clipped data. Experiments with synthetic images and with real raw data from charge-coupled device (CCD) sensor show the feasibility and accuracy of the approach.