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AssigneesGE Precision Healthcare LLC, Purdue Research Foundation, University of Notre Dame du Lac
InventorsRoman Melnyk, Madhuri Mahendra Nagare, Jie Tang, Obaidullah Rahman, Brian E Nett, Ken Sauer, Charles Addison Bouman, Jr.
Estimating the term…
Noise preserving models and methods for resolution recovery of x-ray computed tomography (e.g., using a computerized tool) are enabled. For example, a system can comprise: a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a pair generation component that generates a pair of images, the pair of images comprising an input image and a ground truth image, a training component that trains a machine learning based sharpening algorithm by approximately minimizing a loss function that determines an error between a sharpened image and the ground truth image, and a sharpening component that, using the sharpening algorithm, sharpens the input image to generate the sharpened image, wherein the sharpened image comprises a second noise that is similar in intensity to a first noise of the input image.
A system, comprising:
A method, comprising:
A non-transitory machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
Dependencies are inferred from the claim text.