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Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks
Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy.
Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction
The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods.
Background subtraction using the factored 3-way restricted Boltzmann machines
Hence, there needs to be a method that can extract useful information from such sensory inputs.
Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue.
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