{"id":"https://openalex.org/W2150551556","doi":"https://doi.org/10.1109/icip.2009.5414411","title":"Enhancing sparsity using gradients for compressive sensing","display_name":"Enhancing sparsity using gradients for compressive sensing","publication_year":2009,"publication_date":"2009-11-01","ids":{"openalex":"https://openalex.org/W2150551556","doi":"https://doi.org/10.1109/icip.2009.5414411","mag":"2150551556"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2009.5414411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2009.5414411","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 16th IEEE International Conference on Image Processing (ICIP)","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/A5004716468","display_name":"Vishal M. Patel","orcid":"https://orcid.org/0000-0002-5239-692X"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vishal M. Patel","raw_affiliation_strings":["University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088245220","display_name":"Glenn R. Easley","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Glenn R. Easley","raw_affiliation_strings":["University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102762707","display_name":"Rama Chellappa","orcid":"https://orcid.org/0000-0002-7638-1650"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rama Chellappa","raw_affiliation_strings":["University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110162620","display_name":"Dennis M. Healy","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dennis M. Healy","raw_affiliation_strings":["University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3566,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67073741,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"14","issue":null,"first_page":"3033","last_page":"3036"},"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9997000098228455,"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":0.9983999729156494,"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/compressed-sensing","display_name":"Compressed sensing","score":0.7102261185646057},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6752555966377258},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.6062455177307129},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5930673480033875},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.529022216796875},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.517284631729126},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4970584213733673},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.493673175573349},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4863146245479584},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4800244867801666},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.437539279460907},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.4168474078178406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35432177782058716},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22232991456985474},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.21379736065864563},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11038130521774292}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7102261185646057},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6752555966377258},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.6062455177307129},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5930673480033875},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.529022216796875},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.517284631729126},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4970584213733673},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.493673175573349},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4863146245479584},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4800244867801666},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.437539279460907},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.4168474078178406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35432177782058716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22232991456985474},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.21379736065864563},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11038130521774292},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icip.2009.5414411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2009.5414411","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 16th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.190.5125","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.190.5125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cfar.umd.edu/%7Erama/Publications/Patel_ICIP_2009b.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.369.3578","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.369.3578","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.umiacs.umd.edu/users/pvishalm/Conference_pub/ICIP_Gradients.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W95698601","https://openalex.org/W1676212501","https://openalex.org/W1981558336","https://openalex.org/W2004544971","https://openalex.org/W2101675075","https://openalex.org/W2107861471","https://openalex.org/W2109449402","https://openalex.org/W2112668648","https://openalex.org/W2145051549","https://openalex.org/W2145096794","https://openalex.org/W2153686064","https://openalex.org/W2164452299","https://openalex.org/W2296616510","https://openalex.org/W4250955649","https://openalex.org/W6637147058"],"related_works":["https://openalex.org/W2158224665","https://openalex.org/W2379589510","https://openalex.org/W4300044672","https://openalex.org/W2810730439","https://openalex.org/W1881631164","https://openalex.org/W2358292267","https://openalex.org/W2186864281","https://openalex.org/W2378166785","https://openalex.org/W1964277756","https://openalex.org/W2465351041"],"abstract_inverted_index":{"In":[0],"this":[1,37,68],"paper,":[2],"we":[3],"propose":[4],"a":[5,14,18,45,55],"reconstruction":[6],"method":[7],"that":[8,26,40,50,67],"recovers":[9],"images":[10],"assumed":[11],"to":[12,74],"have":[13],"sparse":[15],"representation":[16],"in":[17,29,53],"gradient":[19],"domain":[20],"by":[21],"using":[22],"partial":[23],"measurement":[24],"samples":[25],"are":[27],"collected":[28],"the":[30],"Fourier":[31],"domain.":[32],"A":[33],"key":[34],"improvement":[35],"of":[36,44],"technique":[38,70],"is":[39,71],"it":[41],"makes":[42],"use":[43],"robust":[46],"generalized":[47],"Poisson":[48],"solver":[49],"greatly":[51],"aids":[52],"achieving":[54],"significantly":[56],"improved":[57],"performance":[58],"over":[59],"similar":[60],"proposed":[61],"methods.":[62],"Experiments":[63],"provided":[64],"also":[65],"demonstrate":[66],"new":[69],"more":[72],"flexible":[73],"work":[75],"with":[76],"either":[77],"random":[78],"or":[79],"restricted":[80],"sampling":[81],"scenarios":[82],"better":[83],"than":[84],"its":[85],"competitors.":[86]},"counts_by_year":[{"year":2017,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}