{"id":"https://openalex.org/W3035632580","doi":"https://doi.org/10.1109/icme46284.2020.9102835","title":"High Accuracy Compressive Chromo-Tomography Reconstruction via Convolutional Sparse Coding","display_name":"High Accuracy Compressive Chromo-Tomography Reconstruction via Convolutional Sparse Coding","publication_year":2020,"publication_date":"2020-06-09","ids":{"openalex":"https://openalex.org/W3035632580","doi":"https://doi.org/10.1109/icme46284.2020.9102835","mag":"3035632580"},"language":"en","primary_location":{"id":"doi:10.1109/icme46284.2020.9102835","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102835","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029990373","display_name":"Baoping Li","orcid":"https://orcid.org/0000-0001-7951-1740"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoping Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100429428","display_name":"Xuesong Zhang","orcid":"https://orcid.org/0000-0003-3185-5100"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuesong Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071183299","display_name":"Jing Jiang","orcid":"https://orcid.org/0000-0003-3242-912X"},"institutions":[{"id":"https://openalex.org/I114234892","display_name":"Beijing Union University","ror":"https://ror.org/01hg31662","country_code":"CN","type":"education","lineage":["https://openalex.org/I114234892"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Jiang","raw_affiliation_strings":["Department of Communication Engineering, Beijing Union University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Communication Engineering, Beijing Union University, Beijing, China","institution_ids":["https://openalex.org/I114234892"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100707846","display_name":"Yuzhong Chen","orcid":"https://orcid.org/0000-0001-7408-2684"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuzhong Chen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100360194","display_name":"Qi Zhang","orcid":"https://orcid.org/0000-0001-5303-9804"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082092902","display_name":"Anlong Ming","orcid":"https://orcid.org/0000-0003-2952-7757"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anlong Ming","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"35","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.919531524181366},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6871525049209595},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.6764304041862488},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.6261591911315918},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.6127731800079346},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.5965334177017212},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5480098128318787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5011579990386963},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.49860644340515137},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4955178499221802},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4948808550834656},{"id":"https://openalex.org/keywords/tomographic-reconstruction","display_name":"Tomographic reconstruction","score":0.46732985973358154},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.46572989225387573},{"id":"https://openalex.org/keywords/tomography","display_name":"Tomography","score":0.46308690309524536},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3899800181388855},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3463357388973236},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2393462359905243},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.09789398312568665},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08935791254043579}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.919531524181366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6871525049209595},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.6764304041862488},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.6261591911315918},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.6127731800079346},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.5965334177017212},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5480098128318787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5011579990386963},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.49860644340515137},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4955178499221802},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4948808550834656},{"id":"https://openalex.org/C97742081","wikidata":"https://www.wikidata.org/wiki/Q7820109","display_name":"Tomographic reconstruction","level":3,"score":0.46732985973358154},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.46572989225387573},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.46308690309524536},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3899800181388855},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3463357388973236},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2393462359905243},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.09789398312568665},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08935791254043579},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme46284.2020.9102835","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme46284.2020.9102835","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1946953458","https://openalex.org/W1975622988","https://openalex.org/W1986931325","https://openalex.org/W2012946078","https://openalex.org/W2030270830","https://openalex.org/W2050790932","https://openalex.org/W2056672982","https://openalex.org/W2079319869","https://openalex.org/W2084591647","https://openalex.org/W2092663520","https://openalex.org/W2100109944","https://openalex.org/W2117259536","https://openalex.org/W2145096794","https://openalex.org/W2148791483","https://openalex.org/W2170608472","https://openalex.org/W2202656999","https://openalex.org/W2304846792","https://openalex.org/W2520430674","https://openalex.org/W2700340246","https://openalex.org/W2770113520","https://openalex.org/W2773809991","https://openalex.org/W2921862546","https://openalex.org/W2937472985","https://openalex.org/W2964984565","https://openalex.org/W2990175810","https://openalex.org/W3121742466","https://openalex.org/W6653409537","https://openalex.org/W6727116172","https://openalex.org/W6761155283"],"related_works":["https://openalex.org/W2890544631","https://openalex.org/W2067062989","https://openalex.org/W2998105788","https://openalex.org/W4205656132","https://openalex.org/W2111634407","https://openalex.org/W3004790527","https://openalex.org/W2157785665","https://openalex.org/W2008821896","https://openalex.org/W2048023787","https://openalex.org/W2068941797"],"abstract_inverted_index":{"Over":[0],"the":[1,39,46,97,129],"last":[2],"decade":[3],"various":[4],"compressive":[5,34],"snapshot":[6],"hyperspectral":[7,102,124],"imaging":[8],"methods":[9],"have":[10],"been":[11,23],"proposed.":[12],"The":[13],"limited":[14],"reconstruction":[15,98],"quality":[16],"from":[17],"severely":[18],"compressed":[19],"measurements,":[20],"however,":[21],"has":[22],"a":[24,33,58,82,121],"practical":[25],"barrier":[26],"to":[27],"real":[28],"applications.":[29],"This":[30],"paper":[31],"proposes":[32],"chromo-tomography":[35],"framework":[36],"that":[37,112,128],"incorporates":[38],"convolutional":[40,103],"sparse":[41],"coding":[42],"(CSC)":[43],"prior":[44],"into":[45],"classical":[47],"total":[48],"variation":[49],"and":[50,92,100,127],"L":[51],"<sub":[52],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[53],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[54],"regularization":[55],"functionals.":[56],"Such":[57],"combination":[59],"allows":[60],"excellent":[61],"high-frequency":[62],"recovery":[63],"capabilities":[64],"of":[65,87,134],"CSC,":[66],"while":[67],"effectively":[68],"suppressing":[69],"ghost":[70],"artifacts":[71],"in":[72,108],"tomographic":[73],"reconstructions.":[74,137],"Since":[75],"nondifferentiable":[76],"regularizers":[77],"are":[78],"employed,":[79],"we":[80],"propose":[81],"preconditioned":[83],"alternating":[84],"direction":[85],"method":[86,131],"multipliers":[88],"(ADMM)":[89],"for":[90,96,101],"flexible":[91],"efficient":[93],"solutions,":[94],"both":[95],"task":[99],"dictionary":[104],"learning.":[105],"We":[106],"demonstrate":[107],"our":[109],"numerical":[110],"experiments":[111],"just":[113],"25":[114],"learned":[115],"3D":[116],"CSC":[117],"filters":[118],"can":[119],"fulfill":[120],"rather":[122],"effective":[123],"imagery":[125],"representation":[126],"proposed":[130],"is":[132],"capable":[133],"high":[135],"accuracy":[136]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
