Image Denoising in Mixed Poisson–Gaussian Noise

By Florian Luisier, Thierry Blu, Michael A. Unser. IEEE Transactions on Image Processing, 2011

503 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5136c28493c6cc15a61c4a6aa45b834494935b39

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Benchmarking Denoising Algorithms with Real Photographs (2017, background): “Other works specifically aim to model intensity-dependent noise [21, 24].”
A Review Paper: Noise Models in Digital Image Processing (2015, methodology): “After all to get a noisy image ( , ) Z j k from the Poisson-Gaussian model is based on [22-23].”