{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120586"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120586","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Noise reduction for ultrasound images using deep interpolation","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Li, Xiaobai"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Song, Pengfei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Ultrasound","Noise Reduction","Deep Interpolation"],"languages":["en","eng"],"rights":["Copyright 2023 Xiaobai Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120586","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Song, Pengfei"]},{"key":"dc:creator","label":"Author","values":["Li, Xiaobai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-03"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Ultrasound","Noise Reduction","Deep Interpolation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Xiaobai Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120586"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Xiaobai Li, accepted the attached license on 2023-05-03 at 08:37.","The student, Xiaobai Li, submitted this Thesis for approval on 2023-05-03 at 09:01.","This Thesis was approved for publication on 2023-05-03 at 15:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19310 on 2023-09-01 at 17:22:20","Ultrasound imaging has been proven to be a safe and effective method of detecting signals in the body related to physiological parameters such as anatomy, blood flow, and stiffness, aiding in the diagnosis of various diseases. However, the quality of these images can be compromised by the low signal-to-noise ratio (SNR) caused by electronic interference noise. Traditional methods like frame averaging can increase SNR, but they require a large number of input frames to produce a single frame of high-SNR output ultrasound images and are ineffective when motion is present as the output image might be blurry. Deep-learning-based denoising techniques have been developed throughout the years, but they either require ground truth images or identical signals in all time frames, which are unachievable in in-vivo ultrasound imaging. To address these limitations, we utilized a U Net-based encoder-decoder network called Deep Interpolation, which uses 40 input frames to reconstruct a high-quality noise-reduced output ultrasound image without ground truth and can be used for both static and in-vivo ultrasound images."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Noise reduction for ultrasound images using deep interpolation"]}]}],"canonical_facts":{"dc:contributor":["Song, Pengfei"],"dc:creator":["Li, Xiaobai"],"dc:date":["2023-05","2023-05-03"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Xiaobai Li, accepted the attached license on 2023-05-03 at 08:37.","The student, Xiaobai Li, submitted this Thesis for approval on 2023-05-03 at 09:01.","This Thesis was approved for publication on 2023-05-03 at 15:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19310 on 2023-09-01 at 17:22:20","Ultrasound imaging has been proven to be a safe and effective method of detecting signals in the body related to physiological parameters such as anatomy, blood flow, and stiffness, aiding in the diagnosis of various diseases. However, the quality of these images can be compromised by the low signal-to-noise ratio (SNR) caused by electronic interference noise. Traditional methods like frame averaging can increase SNR, but they require a large number of input frames to produce a single frame of high-SNR output ultrasound images and are ineffective when motion is present as the output image might be blurry. Deep-learning-based denoising techniques have been developed throughout the years, but they either require ground truth images or identical signals in all time frames, which are unachievable in in-vivo ultrasound imaging. To address these limitations, we utilized a U Net-based encoder-decoder network called Deep Interpolation, which uses 40 input frames to reconstruct a high-quality noise-reduced output ultrasound image without ground truth and can be used for both static and in-vivo ultrasound images."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120586"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Xiaobai Li"],"dc:subject":["Ultrasound","Noise Reduction","Deep Interpolation"],"dc:title":["Noise reduction for ultrasound images using deep interpolation"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}