{"id":"https://openalex.org/W2791903222","doi":"https://doi.org/10.1109/vcip.2017.8305055","title":"Depth super-resolution via fully edge-augmented guidance","display_name":"Depth super-resolution via fully edge-augmented guidance","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2791903222","doi":"https://doi.org/10.1109/vcip.2017.8305055","mag":"2791903222"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2017.8305055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305055","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"type":"article","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/A5063288559","display_name":"Jingyu Yang","orcid":"https://orcid.org/0000-0002-7521-7920"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingyu Yang","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091681416","display_name":"Hao Lan","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Lan","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089261744","display_name":"Xiaolin Song","orcid":"https://orcid.org/0000-0001-6376-5068"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolin Song","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100377578","display_name":"Kun Li","orcid":"https://orcid.org/0000-0003-2326-0166"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kun Li","raw_affiliation_strings":["School of Computer Science and Technology, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tianjin University, Tianjin, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063288559"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.2731,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66271262,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998000264167786,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9994000196456909,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7896629571914673},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7744078636169434},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.7278724908828735},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7099329829216003},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6683281064033508},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.597781240940094},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5788646340370178},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.560343325138092},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5241644382476807},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5194684863090515},{"id":"https://openalex.org/keywords/superresolution","display_name":"Superresolution","score":0.5056419372558594},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4866276681423187},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4210437834262848},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4150092899799347},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11456871032714844},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.05537453293800354},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.05271410942077637}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7896629571914673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7744078636169434},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.7278724908828735},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7099329829216003},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6683281064033508},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.597781240940094},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5788646340370178},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.560343325138092},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5241644382476807},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5194684863090515},{"id":"https://openalex.org/C141239990","wikidata":"https://www.wikidata.org/wiki/Q957423","display_name":"Superresolution","level":3,"score":0.5056419372558594},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4866276681423187},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4210437834262848},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4150092899799347},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11456871032714844},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.05537453293800354},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.05271410942077637},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip.2017.8305055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2017.8305055","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Visual Communications and Image Processing (VCIP)","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":22,"referenced_works":["https://openalex.org/W1513100184","https://openalex.org/W1665214252","https://openalex.org/W1956571368","https://openalex.org/W1981705310","https://openalex.org/W2099712288","https://openalex.org/W2104620097","https://openalex.org/W2118273112","https://openalex.org/W2153388956","https://openalex.org/W2155893237","https://openalex.org/W2194775991","https://openalex.org/W2242218935","https://openalex.org/W2259424905","https://openalex.org/W2494890720","https://openalex.org/W2503186844","https://openalex.org/W2503339013","https://openalex.org/W2520322935","https://openalex.org/W3100388886","https://openalex.org/W6630726011","https://openalex.org/W6637242042","https://openalex.org/W6641256678","https://openalex.org/W6690498163","https://openalex.org/W6723408449"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W4293226380","https://openalex.org/W2788972299","https://openalex.org/W2521347458","https://openalex.org/W2498789492","https://openalex.org/W4389989350","https://openalex.org/W3004000015","https://openalex.org/W2559771220","https://openalex.org/W4387300017"],"abstract_inverted_index":{"Recently,":[0],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs)":[4],"have":[5],"been":[6],"widely":[7],"used":[8],"for":[9],"image":[10],"processing":[11],"problems.":[12],"In":[13],"this":[14],"work,":[15],"we":[16,71],"present":[17],"an":[18,73],"end-to-end":[19],"depth":[20,44,69,95],"map":[21,45,75,96],"super-resolution":[22,97],"method":[23],"based":[24],"on":[25,28,65],"CNN.":[26],"Standing":[27],"a":[29,41,47,56,83],"residual":[30],"learning":[31],"architecture,":[32],"the":[33,52,66,77,89,93],"proposed":[34,90],"network":[35,91],"learns":[36],"joint":[37],"features":[38],"to":[39,63],"get":[40],"high-resolution":[42],"(HR)":[43],"from":[46,76],"low-resolution":[48],"(LR)":[49],"one":[50],"with":[51],"multi-layers":[53],"guidance":[54],"of":[55,68],"HR":[57,79],"color":[58,80],"image.":[59],"Furthermore,":[60],"in":[61],"order":[62],"focus":[64],"boundaries":[67],"map,":[70],"generate":[72],"edge-attention":[74],"associated":[78],"images":[81],"as":[82],"guidance.":[84],"Experimental":[85],"results":[86],"show":[87],"that":[88],"outperforms":[92],"state-of-the-art":[94],"methods.":[98]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}