Practical denoising of clipped or overexposed noisy images

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 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 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 CCD sensor show the feasibility and accuracy of the approach.