3DGAT uses 3D Gaussian kernels plus the FLFM optical model to self-supervisedly reconstruct volumetric fluorescence, beating Richardson-Lucy deconvolution in resolution on simulated and real data.
An iterative technique for the rectification of observed distributions
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3D Gaussian Adaptive Reconstruction for Fourier Light-Field Microscopy
3DGAT uses 3D Gaussian kernels plus the FLFM optical model to self-supervisedly reconstruct volumetric fluorescence, beating Richardson-Lucy deconvolution in resolution on simulated and real data.