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Image Denoising in Mixed Poisson-Gaussian Noise

F. Luisier, T. Blu, M. Unser

IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 696-708, March 2011.

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate). We provide a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques that are specifically tailored to the estimation of Poisson intensities. We also present denoising results obtained on real images of low-count fluorescence microscopy.

AUTHOR="Luisier, F. and Blu, T. and Unser, M.",
TITLE="Image Denoising in Mixed {P}oisson-{G}aussian Noise",
JOURNAL="{IEEE} Transactions on Image Processing",

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