  Confocal microscopy is essential for histopathologic cell visualization and
quantification. Despite its significant role in biology, fluorescence confocal
microscopy suffers from the presence of inherent noise during image
acquisition. Non-local patch-wise Bayesian mean filtering (NLB) was until
recently the state-of-the-art denoising approach. However, classic denoising
methods have been outperformed by neural networks in recent years. In this
work, we propose to exploit the strengths of NLB in the framework of Bayesian
deep learning. We do so by designing a convolutional neural network and
training it to learn parameters of a Gaussian model approximating the prior on
noise-free patches given their nearest, similar yet non-local, neighbors. We
then apply Bayesian reasoning to leverage the prior and information from the
noisy patch in the process of approximating the noise-free patch. Specifically,
we use the closed-form analytic \textit{maximum a posteriori} (MAP) estimate in
the NLB algorithm to obtain the noise-free patch that maximizes the posterior
distribution. The performance of our proposed method is evaluated on confocal
microscopy images with real noise Poisson-Gaussian noise. Our experiments
reveal the superiority of our approach against state-of-the-art unsupervised
denoising techniques.
