In many applications the noise that corrupts the signal is non-Gaussian and signal-dependent. There is a variaty of heuristic adaptive-neighborhood approaches for filtering signal and images corrupted by signal-dependent noise. The procedure is given for observations subject to the class of exponential distributions which includes the Poissonian model as an important special case.
The performance of the algorithm is illustrated for image denoising with data having Poissonian, Gaussian and Bernoulli observations.
The main algorithm is prepared as demo, so that it can be executed in a straightforward manner.
This demo reproduces figures and results from the paper:
Katkovnik V., and V. Spokoiny, “Spatially Adaptive Non-Gaussian Imaging via Fitted Local Likelihood Technique.”, Proc. Int. TICSP Workshop Spectral Meth. Multirate Signal Process., SMMSP 2006, Florence, 2006.
The provided demo is open-source, and may be modified and tuned to be exploited with other data. This DemoBox is available free-of-charge for educational and non-profit scientific research, enabling others researchers to understand and reproduce our work. Any unauthorized use of the LASIP routines for industrial or profit-oriented activities is expressively prohibited.
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