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Algorithm-driven Advances for Scientific CT Instruments: From Model-based to Deep Learning-based Approaches
In the first part, we will focus on model-based image reconstruction algorithms that formulate the inversion as solving a high-dimensional optimization problem involving a data-fidelity term and a regularization term.
Model-based Reconstruction for Single Particle Cryo-Electron Microscopy
In this paper we present a model-based image reconstruction technique that uses a regularized cost function to reconstruct the 3D density map by assuming known orientations for the particles.
Multi-resolution Data Fusion for Super-Resolution Electron Microscopy
Consequently, there is an enormous demand in the materials and biological sciences to image at greater speed and lower dosage, while maintaining resolution.
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
The power of the P&P approach is that it allows a wide array of modern denoising algorithms to be used as a "prior model" for tomography and image interpolation.
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