Joint super resolution and denoising: learning to recover sharp features in radiology images
Cole, Patrick Alexander
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https://hdl.handle.net/2142/108636
Description
Title
Joint super resolution and denoising: learning to recover sharp features in radiology images
Author(s)
Cole, Patrick Alexander
Issue Date
2020-07-22
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Oluwasanmi
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Machine Learning
Artificial Intelligence
Computer Vision
Radiology
X-ray Radiograph
Computed Tomography
Super Resolution
Denoise
Image Processing
Abstract
Radiology exams require exposing a patient to a variable dosage of radiation. The amount of radiation used during the exam directly corresponds to the level of noise in the resulting image. While large amounts of radiation can be dangerous for certain patients, radiologists need an uncorrupted image to make a diagnosis. In our work, we detail methods for simulating low-dose noise for two popular radiology exams: x-ray radiograph and computed tomography. We propose a methodology to recover the uncorrupted exam results given a noisy, or low-dose, sample. Using a two-part criterion that consists of a pixel-wise loss and an adversarial loss, we are able to recover the structure and fine detail of the normal-dose sample.
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