{"id":"https://openalex.org/W3195985888","doi":"https://doi.org/10.1109/icip42928.2021.9506316","title":"Perception Inspired Deep Neural Networks For Spectral Snapshot Compressive Imaging","display_name":"Perception Inspired Deep Neural Networks For Spectral Snapshot Compressive Imaging","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3195985888","doi":"https://doi.org/10.1109/icip42928.2021.9506316","mag":"3195985888"},"language":"en","primary_location":{"id":"doi:10.1109/icip42928.2021.9506316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","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/A5053959526","display_name":"Ziyi Meng","orcid":"https://orcid.org/0000-0001-8294-8847"},"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":"Ziyi Meng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China,100876"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015431603","display_name":"Xin Yuan","orcid":"https://orcid.org/0000-0002-8311-7524"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Yuan","raw_affiliation_strings":["Westlake University,Hangzhong,China,310024"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Westlake University,Hangzhong,China,310024","institution_ids":["https://openalex.org/I3133055985"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9932,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.95688834,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2813","last_page":"2817"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9991999864578247,"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":0.9060324430465698},{"id":"https://openalex.org/keywords/snapshot","display_name":"Snapshot (computer storage)","score":0.7690494060516357},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.764123260974884},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.732241153717041},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6218719482421875},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.6138690114021301},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5185118913650513},{"id":"https://openalex.org/keywords/spectral-imaging","display_name":"Spectral imaging","score":0.46813496947288513},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43159541487693787},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.42352789640426636},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41296079754829407},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16407009959220886},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.116659015417099}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9060324430465698},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.7690494060516357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.764123260974884},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.732241153717041},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6218719482421875},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.6138690114021301},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5185118913650513},{"id":"https://openalex.org/C3232514","wikidata":"https://www.wikidata.org/wiki/Q7575196","display_name":"Spectral imaging","level":2,"score":0.46813496947288513},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43159541487693787},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.42352789640426636},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41296079754829407},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16407009959220886},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.116659015417099},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip42928.2021.9506316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1986213393","https://openalex.org/W1986701690","https://openalex.org/W2002498099","https://openalex.org/W2005361584","https://openalex.org/W2084591647","https://openalex.org/W2097273723","https://openalex.org/W2163753106","https://openalex.org/W2212988997","https://openalex.org/W2331128040","https://openalex.org/W2520430674","https://openalex.org/W2521489947","https://openalex.org/W2738753231","https://openalex.org/W2770113520","https://openalex.org/W2806340326","https://openalex.org/W2963764784","https://openalex.org/W2964984565","https://openalex.org/W2983736948","https://openalex.org/W3006196464","https://openalex.org/W3009338239","https://openalex.org/W3035466729","https://openalex.org/W3041490661","https://openalex.org/W3096654432","https://openalex.org/W3108567638","https://openalex.org/W3112283630","https://openalex.org/W3134510327","https://openalex.org/W3135630532","https://openalex.org/W3156692515","https://openalex.org/W3173125503","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6639824700","https://openalex.org/W6702130928","https://openalex.org/W6727116172","https://openalex.org/W6787380701"],"related_works":["https://openalex.org/W3047444742","https://openalex.org/W2054836752","https://openalex.org/W4386427838","https://openalex.org/W2330930555","https://openalex.org/W2477458110","https://openalex.org/W2951714568","https://openalex.org/W1973220951","https://openalex.org/W3178760882","https://openalex.org/W2002001014","https://openalex.org/W2049189005"],"abstract_inverted_index":{"We":[0],"consider":[1],"the":[2,14,24,37,41,58,67,88,98,103,107,128,137],"inverse":[3],"problem":[4],"of":[5,32,40],"coded":[6],"aperture":[7],"snapshot":[8,19],"spectral":[9],"imaging":[10],"(CASSI),":[11],"which":[12],"captures":[13],"spatio-spectral":[15],"data-cube":[16],"using":[17,28,136],"a":[18,83],"2D":[20],"measurement":[21],"and":[22,46,54,70,77,119],"reconstructs":[23],"3D":[25],"hyperspectral":[26,43,93],"images":[27,44,94],"algorithms.":[29],"Recent":[30],"advances":[31],"deep":[33,108],"learning":[34],"have":[35],"boosted":[36],"image":[38,130],"quality":[39,131],"reconstructed":[42,129],"significantly,":[45],"this":[47,80,125],"leads":[48],"to":[49],"an":[50,72],"end-to-end":[51],"real-time":[52],"capture":[53],"reconstruction":[55,63],"system.":[56],"However,":[57],"network":[59,74,110],"design":[60],"for":[61,111],"CASSI":[62,112],"is":[64,75],"still":[65,96],"at":[66],"incubation":[68],"stage":[69],"usually":[71],"off-the-shelf":[73],"employed":[76],"re-purposed.":[78],"In":[79],"work,":[81],"from":[82],"different":[84],"perspective,":[85],"inspired":[86],"by":[87],"fact":[89],"that":[90,123],"most":[91],"existing":[92],"are":[95],"in":[97],"visible":[99],"bandwidth,":[100],"we":[101],"introduce":[102],"perceptual":[104],"loss":[105],"into":[106],"neural":[109],"reconstruction.":[113],"Extensive":[114],"results":[115],"on":[116],"both":[117],"simulation":[118],"real":[120],"data":[121],"demonstrate":[122],"with":[124],"small":[126],"change,":[127],"can":[132],"be":[133],"improved":[134],"dramatically":[135],"same":[138],"network.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}
