Kirmiziay, CagatayAydeniz, BurhanTurkan, Mehmet2023-06-162023-06-162022978-1-6654-5432-2https://doi.org/10.1109/TIPTEKNO56568.2022.9960175https://hdl.handle.net/20.500.14365/1994Medical Technologies Congress (TIPTEKNO) -- OCT 31-NOV 02, 2022 -- Antalya, TURKEYAs 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.eninfo:eu-repo/semantics/closedAccessFluorescence microscopydenoisingneighborembeddinglinear-embeddingConference Object10.1109/TIPTEKNO56568.2022.99601752-s2.0-85144050599