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Low illumination environment in Fluorescence microscopy, create arbitrary variations in the photon emission and detection process that manifest as Poisson noise in the captured images. Therefore study the effect of Standard denoising algorithms wherein the noise is either transformed to Gaussian or the denoising is done on the Poisson noise itself. In the first strategy the noise is Gaussianized by applying the Anscombe root transformation to the data, to produce a signal in which the noise can be treated as additive Gaussian and then the consequential image is denoised using conservative denoising algorithms for additive white Gaussian noise such as BLS_GSM and OWT_SURELET and finally the inverse transformation is done on the denoised image. The choice of the proper inverse transformation is vital for fluorescence images in order to reduce the bias error which arises when the nonlinear forward transformation is applied. The Latter strategy considers PURELET technique where the denoising process is a Linear Expansion of Thresholds (LET) that optimize results by depending 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). Experimental results are compared with exisitng work on how the ISNR changes with the change in algorithms for fluorescence images.

Fluoresence Image Denoising using Diverese Strategies and their Performance Evaluation