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© Research
Publication : IEEE Transactions on Image Processing

Denoising of Microscopy Images: A Review of the State-of-the-Art, and a New Sparsity-Based Method

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in IEEE Transactions on Image Processing - 01 Aug 2018

William Meiniel, Jean-Christophe Olivo-Marin, Elsa D. Angelini

IEEE Transactions on Image Processing, 2018, vol. 27, no 8, p. 3842-3856

This paper reviews the state-of-the-art in denoising methods for biological microscopy images and introduces a new and original sparsity-based algorithm. The proposed method combines total variation (TV) spatial regularization, enhancement of low-frequency information, and aggregation of sparse estimators and is able to handle simple and complex types of noise (Gaussian, Poisson, and mixed), without any a priori model and with a single set of parameter values. An extended comparison is also presented, that evaluates the denoising performance of the thirteen (including ours) state-of-the-art denoising methods specifically designed to handle the different types of noises found in bioimaging. Quantitative and qualitative results on synthetic and real images show that the proposed method outperforms the other ones on the majority of the tested scenarios.

DOI: 10.1109/TIP.2018.2819821

https://ieeexplore.ieee.org/abstract/document/8327626/