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Undecimated Haar Thresholding for Poisson Intensity Estimation

F. Luisier, T. Blu, M. Unser

Proceedings of the 2010 IEEE International Conference on Image Processing (ICIP'10), Hong Kong, People's Republic of China, September 26-29, 2010, pp. 1697-1700.



We propose a novel algorithm for denoising Poisson-corrupted images, that performs a signal-adaptive thresholding of the undecimated Haar wavelet coefficients. A Poisson's unbiased MSE estimate is devised and adapted to arbitrary transform-domain pointwise processing. This prior-free quadratic measure of quality is then used to globally optimize a linearly parameterized subband-adaptive thresholding, which accounts for the signal-dependent noise variance. We demonstrate the qualitative and computational competitiveness of the resulting denoising algorithm through comprehensive comparisons with some state-of-the-art multiscale techniques specifically designed for Poisson intensity estimation. We also show promising denoising results obtained on low-count fluorescence microscopy images.


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