Undecimated Haar Thresholding for Poisson Intensity Estimation

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.


Published in:
Proceedings of the 2010 IEEE International Conference on Image Processing (ICIP'10), Hong Kong, People's Republic of China, 1697–1700
Year:
2010
Publisher:
IEEE
Laboratories:




 Record created 2015-09-18, last modified 2018-03-17

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