Fluorescence microscopy image noise reduction using IEMD-based adaptive thresholding approach

Fluorescence microscopy is an important investigation tool for discoveries in the field of biological sciences. In this paper, we propose an adaptive thresholding technique-based improved empirical mode decomposition (IEMD) for denoising of heavily degraded images labeled with Fluorescent proteins. These images are widely used by a computational biologists to analyze the biological functions of different species. A variance stabilization transformation is applied as preprocessing step. The multi-scale Wiener filtering approach is used as the first step for accurate image deconvolution. In the subsequent steps, IEMD is performed to obtain different series of intrinsic mode functions (IMFs) which are further separated into noise and signal-significant IMFs based on Cosine similarity index. The IMF adaptive thresholding technique is used which filter-out the unwanted frequency coefficients related to mixed Poisson-Gaussian noise (MPG). The thresholded output IMFs are combined with signal significant IMFs in the third step. Finally, the mean square deviation (MSD) is minimized using mixed Poisson-Gaussian unbiased risk estimate (MPGURE). To evaluate the effectiveness of the proposed scheme, we have compared the results of the proposed scheme with those of the five state-of-the-art techniques. The simulation results validate, the effectiveness of the proposed method. The proposed algorithm achieves better performance in terms of four quantitative evaluation measures by reducing the effect of noise.