A Monte Carlo Framework for Denoising and Missing Wedge Reconstruction in Cryo-electron Tomography
We propose a statistical method to address an important issue in cryo electron tomography image analysis: reduction of a high amount of noise and artifacts due to the presence of a missing wedge (MW) in the spectral domain. The method takes as an input a 3D tomogram derived from limited-angle tomography, and gives as an output a 3D denoised and artifact compensated tomogram. The artifact compensation is achieved by filling up the MW with meaningful information. The method can be used to enhance visualization or as a pre-processing step for image analysis, including segmentation and classification. Results are presented for both synthetic and experimental data.
KeywordsCryo electron tomography Patch-based denoising Missing wedge restoration Stochastic models Monte Carlo simulation
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