{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105275"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105275","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Compression artifact suppression for color images with dual-domain SE-ARResNet","abstract":"JPEG compression has been a popular lossy image compression technique and is widely used in digital imaging. Restoring high-quality images from their compressed JPEG counterparts, however, is an ill-posed inverse problem but could be of great use in improving the visual quality of images. With the representational power that convolutional neural networks (CNNs) demon- strate, we show that it is possible to suppress JPEG compression artifacts and recover visually pleasing images. To recover original high-quality and high-resolution images from JPEG compressed images, we leverage prior knowledge of JPEG compression into consideration by exploiting frequency redundancies with the CNN in discrete cosine domain and constrain the quantization loss, in addition to exploiting spatial redundancies in the pixel domain. This data-driven approach tar- gets removing compression artifacts, including blocking, blurring, ringing and banding artifacts, and recovering high-frequency information for reconstruction. We design a deep CNN in each domain and fuse the outputs with an aggregation network to produce the output image. To improve the model performance, we leverage the robustness and ability to tackle vanishing gradient problems of ResNet to build a deep network, and utilize squeeze-and- excitation block, a technique typically found beneficial in classification tasks, to this regression problem to exploit global information in a larger scale. We refer to the module proposed in this work as squeeze-and-excitation artifact removal ResNet (SE-ARResNet). Prior work in this field mainly focuses on reconstructing a grayscale image or the luminance channel of the image. We demonstrate that we can reconstruct color images effectively and robustly with the dual-domain CNN approach.","abstract_html":"JPEG compression has been a popular lossy image compression technique and is widely used in digital imaging. Restoring high-quality images from their compressed JPEG counterparts, however, is an ill-posed inverse problem but could be of great use in improving the visual quality of images. With the representational power that convolutional neural networks (CNNs) demon- strate, we show that it is possible to suppress JPEG compression artifacts and recover visually pleasing images. To recover original high-quality and high-resolution images from JPEG compressed images, we leverage prior knowledge of JPEG compression into consideration by exploiting frequency redundancies with the CNN in discrete cosine domain and constrain the quantization loss, in addition to exploiting spatial redundancies in the pixel domain. This data-driven approach tar- gets removing compression artifacts, including blocking, blurring, ringing and banding artifacts, and recovering high-frequency information for reconstruction. We design a deep CNN in each domain and fuse the outputs with an aggregation network to produce the output image. To improve the model performance, we leverage the robustness and ability to tackle vanishing gradient problems of ResNet to build a deep network, and utilize squeeze-and- excitation block, a technique typically found beneficial in classification tasks, to this regression problem to exploit global information in a larger scale. We refer to the module proposed in this work as squeeze-and-excitation artifact removal ResNet (SE-ARResNet). Prior work in this field mainly focuses on reconstructing a grayscale image or the luminance channel of the image. We demonstrate that we can reconstruct color images effectively and robustly with the dual-domain CNN approach.","abstract_has_math":false,"creators":["Nie, Jiaxi"],"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":["Do, Minh N."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:48:28Z","date_published":"2019-08-23T20:48:28Z","updated_at":"2026-07-22T22:24:44Z","subjects":["JPEG compression","artifact removal","CNN","DCT"],"languages":["en"],"rights":["Copyright 2019 Jiaxi Jason Nie"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105275","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh N."]},{"key":"dc:creator","label":"Author","values":["Nie, Jiaxi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:48:28Z","2021-08-24T09:15:16Z","2019-04-26","2019-05"]},{"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":["JPEG compression","artifact removal","CNN","DCT"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Jiaxi Jason Nie"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105275"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["JPEG compression has been a popular lossy image compression technique and is widely used in digital imaging. Restoring high-quality images from their compressed JPEG counterparts, however, is an ill-posed inverse problem but could be of great use in improving the visual quality of images. With the representational power that convolutional neural networks (CNNs) demon- strate, we show that it is possible to suppress JPEG compression artifacts and recover visually pleasing images. To recover original high-quality and high-resolution images from JPEG compressed images, we leverage prior knowledge of JPEG compression into consideration by exploiting frequency redundancies with the CNN in discrete cosine domain and constrain the quantization loss, in addition to exploiting spatial redundancies in the pixel domain. This data-driven approach tar- gets removing compression artifacts, including blocking, blurring, ringing and banding artifacts, and recovering high-frequency information for reconstruction. We design a deep CNN in each domain and fuse the outputs with an aggregation network to produce the output image. To improve the model performance, we leverage the robustness and ability to tackle vanishing gradient problems of ResNet to build a deep network, and utilize squeeze-and- excitation block, a technique typically found beneficial in classification tasks, to this regression problem to exploit global information in a larger scale. We refer to the module proposed in this work as squeeze-and-excitation artifact removal ResNet (SE-ARResNet). Prior work in this field mainly focuses on reconstructing a grayscale image or the luminance channel of the image. We demonstrate that we can reconstruct color images effectively and robustly with the dual-domain CNN approach.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Jiaxi Nie, accepted the attached license on 2019-04-26 at 12:31.","The student, Jiaxi Nie, submitted this Thesis for approval on 2019-04-26 at 16:00.","This Thesis was approved for publication on 2019-04-26 at 16:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13948 on 2019-08-22 at 16:24:02","Made available in DSpace on 2019-08-23T20:48:28Z (GMT). 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Restoring high-quality images from their compressed JPEG counterparts, however, is an ill-posed inverse problem but could be of great use in improving the visual quality of images. With the representational power that convolutional neural networks (CNNs) demon- strate, we show that it is possible to suppress JPEG compression artifacts and recover visually pleasing images. To recover original high-quality and high-resolution images from JPEG compressed images, we leverage prior knowledge of JPEG compression into consideration by exploiting frequency redundancies with the CNN in discrete cosine domain and constrain the quantization loss, in addition to exploiting spatial redundancies in the pixel domain. This data-driven approach tar- gets removing compression artifacts, including blocking, blurring, ringing and banding artifacts, and recovering high-frequency information for reconstruction. We design a deep CNN in each domain and fuse the outputs with an aggregation network to produce the output image. To improve the model performance, we leverage the robustness and ability to tackle vanishing gradient problems of ResNet to build a deep network, and utilize squeeze-and- excitation block, a technique typically found beneficial in classification tasks, to this regression problem to exploit global information in a larger scale. We refer to the module proposed in this work as squeeze-and-excitation artifact removal ResNet (SE-ARResNet). Prior work in this field mainly focuses on reconstructing a grayscale image or the luminance channel of the image. We demonstrate that we can reconstruct color images effectively and robustly with the dual-domain CNN approach.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Jiaxi Nie, accepted the attached license on 2019-04-26 at 12:31.","The student, Jiaxi Nie, submitted this Thesis for approval on 2019-04-26 at 16:00.","This Thesis was approved for publication on 2019-04-26 at 16:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13948 on 2019-08-22 at 16:24:02","Made available in DSpace on 2019-08-23T20:48:28Z (GMT). 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