{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108636"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108636","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Joint super resolution and denoising: learning to recover sharp features in radiology images","abstract":"Embargo set by: Seth Robbins for item 116263 Lift date: 2022-10-07T22:44:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","abstract_html":"Embargo set by: Seth Robbins for item 116263 Lift date: 2022-10-07T22:44:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","abstract_has_math":false,"creators":["Cole, Patrick Alexander"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Koyejo, Oluwasanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-07T22:44:43Z","date_published":"2020-10-07T22:44:43Z","updated_at":"2026-07-22T22:24:48Z","subjects":["Machine Learning","Artificial Intelligence","Computer Vision","Radiology","X-ray Radiograph","Computed Tomography","Super Resolution","Denoise","Image Processing"],"languages":["en"],"rights":["Copyright 2020 Patrick Cole"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108636","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Oluwasanmi"]},{"key":"dc:creator","label":"Author","values":["Cole, Patrick Alexander"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-10-07T22:44:43Z","2022-10-07T22:44:53Z","2020-07-22","2020-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Artificial Intelligence","Computer Vision","Radiology","X-ray Radiograph","Computed Tomography","Super Resolution","Denoise","Image Processing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Patrick Cole"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108636"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Embargo set by: Seth Robbins for item 116263 Lift date: 2022-10-07T22:44:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-08-01","The student, Patrick Cole, accepted the attached license on 2020-07-21 at 15:48.","The student, Patrick Cole, submitted this Thesis for approval on 2020-07-21 at 16:16.","This Thesis was approved for publication on 2020-07-22 at 11:47.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15715 on 2020-10-02 at 15:34:01","Made available in DSpace on 2020-10-07T22:44:43Z (GMT). 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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. 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