Mixed Poisson Gaussian noise reduction in fluorescence microscopy images using modified structure of wavelet transform

Abstract

Fluorescence microscopy is an important investigation tool of discoveries in the field of biological sciences where the imaging phenomena are limited by the noise. This paper introduces the integration of biorthogonal wavelet filters along with mixed Poisson-Gaussian unbiased risk estimate (MPGURE) based subband adaptive thresholding function for the restoration of low photon count microscopy images. The proposed algorithm consists of four steps. In the first step, variance stabilization transform along with a multi-scale Wiener filtering approach is used to filter out the noise and blurring effect. In the second step, deconvolved images are further decomposed by the biorthogonal wavelet filters. The modified wavelet subband structure is used for the identification of noisy and noise-free subbands. In the next stage, different noisy coefficients are thresholded using the MPGURE-based thresholding operation. The different thresholded images are combined along with different optimum coefficients. Finally, inverse variance stabilization transformation is applied to obtain the final restored output. Performance of the proposed algorithm is tested on 14 different benchmark image data sets with performance evaluation measures like signal-to-noise ratio, peak signal-to-noise ratio, mean structural similarity index measur, and correlation coefficient. Simulation results of the proposed algorithm claim better results than other state-of-the-art techniques.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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