NLED: Neighbor Linear-Embedding Denoising for Fluorescence Microscopy Images

Abstract

As noise corruption is an inevitable issue for all imaging technologies, this problem causes serious difficulties in analyzing the biological fine-details of fluorescence microscopy images. While Gaussian only, Poisson only and mixture of Poisson-Gaussian can generally be observed, the mixed-noise is more prominent in fluorescence microscopy. In this paper, a novel patch-based denoiser-learning approach is proposed for the images captured by fluorescence microscopy. The developed algorithm mainly builds upon linear-embeddings of neighboring image patches, and it learns a linear transformation between noisy and clean intrinsic geometric properties of patch-spaces. Experimental results demonstrate that the proposed Neighbor Linear-Embedding Denoising (NLED) has competitive performance both visually and statistically when compared to other algorithms in literature, for noise corrupted fluorescence microscopy images.