{"id":"https://openalex.org/W3033744692","doi":"https://doi.org/10.48550/arxiv.2006.04368","title":"Photoacoustic Microscopy with Sparse Data Enabled by Convolutional Neural Networks for Fast Imaging","display_name":"Photoacoustic Microscopy with Sparse Data Enabled by Convolutional Neural Networks for Fast Imaging","publication_year":2020,"publication_date":"2020-06-08","ids":{"openalex":"https://openalex.org/W3033744692","doi":"https://doi.org/10.48550/arxiv.2006.04368","mag":"3033744692"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2006.04368","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.04368","pdf_url":"https://arxiv.org/pdf/2006.04368","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2006.04368","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038250840","display_name":"Jiasheng Zhou","orcid":"https://orcid.org/0000-0001-6165-1294","observed_orcids":["https://orcid.org/0000-0001-6165-1294"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Jiasheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"He, Da","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Da","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108981611","display_name":"Xiaoyu Shang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shang, Xiaoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101651845","display_name":"Zhendong Guo","orcid":"https://orcid.org/0000-0001-6361-9802","observed_orcids":["https://orcid.org/0000-0001-6361-9802"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Zhendong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008342821","display_name":"Sung\u2010Liang Chen","orcid":"https://orcid.org/0000-0002-0572-5110","observed_orcids":["https://orcid.org/0000-0002-0572-5110"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Sung-liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075638298","display_name":"Jiajia Luo","orcid":"https://orcid.org/0000-0002-9965-7664","observed_orcids":["https://orcid.org/0000-0002-9965-7664"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Jiajia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9848999977111816,"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"}},"topics":[{"id":"https://openalex.org/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9848999977111816,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.005900000222027302,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.00139999995008111,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/photoacoustic-microscopy","display_name":"photoacoustic microscopy","score":0.9990000128746033},{"id":"https://openalex.org/keywords/sparse-reconstruction","display_name":"sparse reconstruction","score":0.5789999961853027},{"id":"https://openalex.org/keywords/image-super-resolution","display_name":"image super-resolution","score":0.6430000066757202},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9110000133514404},{"id":"https://openalex.org/keywords/squeeze-and-excitation-blocks","display_name":"squeeze-and-excitation blocks","score":0.9440000057220459},{"id":"https://openalex.org/keywords/residual-blocks","display_name":"residual blocks","score":0.9890000224113464},{"id":"https://openalex.org/keywords/perceptual-loss","display_name":"perceptual loss","score":0.9259999990463257},{"id":"https://openalex.org/keywords/rapid-imaging","display_name":"fast imaging","score":0.5669999718666077}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7314796447753906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7277101874351501},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7106541395187378},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.6134977340698242},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5447081923484802},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4454784095287323},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4364534020423889},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42900604009628296}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2006.04368","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.04368","pdf_url":"https://arxiv.org/pdf/2006.04368","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2006.04368","doi":"https://doi.org/10.48550/arxiv.2006.04368","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2006.04368","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2006.04368","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.04368","pdf_url":"https://arxiv.org/pdf/2006.04368","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4300000071525574}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1885185971","https://openalex.org/W1901129140","https://openalex.org/W1998412854","https://openalex.org/W2019189459","https://openalex.org/W2030964111","https://openalex.org/W2059441307","https://openalex.org/W2099471712","https://openalex.org/W2108598243","https://openalex.org/W2111399082","https://openalex.org/W2133665775","https://openalex.org/W2147342803","https://openalex.org/W2157048271","https://openalex.org/W2196707239","https://openalex.org/W2331128040","https://openalex.org/W2500522403","https://openalex.org/W2605740169","https://openalex.org/W2621047161","https://openalex.org/W2735224642","https://openalex.org/W2789273102","https://openalex.org/W2791197315","https://openalex.org/W2795434209","https://openalex.org/W2799460839","https://openalex.org/W2802116844","https://openalex.org/W2884630383","https://openalex.org/W2923654191","https://openalex.org/W2963125871","https://openalex.org/W2963174698","https://openalex.org/W2963420686","https://openalex.org/W2963470893","https://openalex.org/W2964193438","https://openalex.org/W2967115092"],"related_works":["https://openalex.org/W2757389719","https://openalex.org/W2951714568","https://openalex.org/W1963814553","https://openalex.org/W2037595954","https://openalex.org/W1988158806","https://openalex.org/W2507293823","https://openalex.org/W2386146599","https://openalex.org/W2018015402","https://openalex.org/W2144778520","https://openalex.org/W2801815023"],"abstract_inverted_index":{"Photoacoustic":[0],"microscopy":[1],"(PAM)":[2],"has":[3],"been":[4],"a":[5,50,91,94,103],"promising":[6],"biomedical":[7],"imaging":[8],"technology":[9],"in":[10,19,155],"recent":[11],"years.":[12],"However,":[13],"the":[14,24,40,59,87,115,135,171,175,179],"point-by-point":[15],"scanning":[16],"mechanism":[17],"results":[18,168],"low-speed":[20],"imaging,":[21],"which":[22,37,89,141,189],"limits":[23],"application":[25],"of":[26,42,61,117,129,137,159,162,178,183],"PAM.":[27],"Reducing":[28],"sampling":[29],"density":[30],"can":[31,173],"naturally":[32],"shorten":[33],"image":[34,43,68,73,101,176,182],"acquisition":[35,69],"time,":[36],"is":[38,90,111,121,151],"at":[39],"cost":[41],"quality.":[44,74],"In":[45],"this":[46],"work,":[47],"we":[48],"propose":[49],"method":[51],"using":[52,154],"convolutional":[53],"neural":[54],"networks":[55],"(CNNs)":[56],"to":[57,85,102,113],"improve":[58],"quality":[60,177],"sparse":[62,99,180],"PAM":[63,100,127,157,181,193],"images,":[64],"thereby":[65],"speeding":[66],"up":[67],"while":[70],"keeping":[71],"good":[72],"The":[75,107,119,132,167],"CNN":[76],"model":[77,120,150,172],"utilizes":[78],"both":[79],"squeeze-and-excitation":[80],"blocks":[81,84],"and":[82,124,147,165,194],"residual":[83],"achieve":[86],"enhancement,":[88],"mapping":[92],"from":[93,186],"1/4":[95],"or":[96],"1/16":[97],"low-sampling":[98],"latent":[104],"fully-sampled":[105],"image.":[106],"perceptual":[108],"loss":[109],"function":[110],"applied":[112],"keep":[114],"fidelity":[116],"images.":[118],"mainly":[122],"trained":[123],"validated":[125],"on":[126],"images":[128,158],"leaf":[130],"veins.":[131],"experiments":[133],"show":[134,169],"effectiveness":[136],"our":[138],"proposed":[139],"method,":[140],"significantly":[142],"outperforms":[143],"existing":[144],"methods":[145],"quantitatively":[146],"qualitatively.":[148],"Our":[149],"also":[152],"tested":[153],"vivo":[156],"blood":[160,184],"vessels":[161,185],"mouse":[163],"ears":[164],"eyes.":[166],"that":[170],"enhance":[174],"several":[187],"aspects,":[188],"may":[190],"help":[191],"fast":[192],"facilitate":[195],"its":[196],"clinical":[197],"applications.":[198]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}
