{"doi":"10.1364/ao.415059","title":"Needle-based deep-neural-network camera","abstract":"We experimentally demonstrate a camera whose primary optic is a cannula/needle ( <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mi mathvariant=\"normal\">d</mml:mi> <mml:mi mathvariant=\"normal\">i</mml:mi> <mml:mi mathvariant=\"normal\">a</mml:mi> <mml:mi mathvariant=\"normal\">m</mml:mi> <mml:mi mathvariant=\"normal\">e</mml:mi> <mml:mi mathvariant=\"normal\">t</mml:mi> <mml:mi mathvariant=\"normal\">e</mml:mi> <mml:mi mathvariant=\"normal\">r</mml:mi> </mml:mrow> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mn>0.22</mml:mn> </mml:mrow> <mml:mspace width=\"thickmathspace\"/> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mi mathvariant=\"normal\">m</mml:mi> <mml:mi mathvariant=\"normal\">m</mml:mi> </mml:mrow> </mml:mrow> </mml:math> and <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mi mathvariant=\"normal\">l</mml:mi> <mml:mi mathvariant=\"normal\">e</mml:mi> <mml:mi mathvariant=\"normal\">n</mml:mi> <mml:mi mathvariant=\"normal\">g</mml:mi> <mml:mi mathvariant=\"normal\">t</mml:mi> <mml:mi mathvariant=\"normal\">h</mml:mi> </mml:mrow> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mn>12.5</mml:mn> </mml:mrow> <mml:mspace width=\"thickmathspace\"/> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mi mathvariant=\"normal\">m</mml:mi> <mml:mi mathvariant=\"normal\">m</mml:mi> </mml:mrow> </mml:mrow> </mml:math> ) that acts as a light pipe transporting light intensity from an object plane (35 cm away) to its opposite end. Deep neural networks (DNNs) are used to reconstruct color and grayscale images with a field of view of 18° and angular resolution of <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mo>∼</mml:mo> </mml:mrow> <mml:msup> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mn>0.4</mml:mn> </mml:mrow> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> . We showed a large effective demagnification of <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mn>127</mml:mn> </mml:mrow> </mml:mrow> <mml:mo>×</mml:mo> </mml:math> . Most interestingly, we showed that such a camera could achieve close to diffraction-limited performance with an effective numerical aperture of 0.045, depth of focus <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mo>∼</mml:mo> </mml:mrow> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mn>16</mml:mn> </mml:mrow> </mml:mrow> <mml:mspace width=\"thickmathspace\"/> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mtext>µ</mml:mtext> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mi mathvariant=\"normal\">m</mml:mi> </mml:mrow> </mml:mrow> </mml:mrow> </mml:math> , and resolution close to the sensor pixel size (3.2 µm). When trained on images with depth information, the DNN can create depth maps. Finally, we show DNN-based classification of the EMNIST dataset before and after image reconstructions. The former could be useful for imaging with enhanced privacy.","journal":"Applied Optics","year":2021,"id":206419,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.96,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":519771,"name":"Soren Nelson","orcid":null,"position":1,"is_corresponding":false},{"id":518138,"name":"Rajesh Menon","orcid":"0000-0002-4777-376X","position":2,"is_corresponding":false},{"id":518137,"name":"Ruipeng Guo","orcid":"0000-0003-1963-0080","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-18T23:51:41.571653Z","pmid":"33798147","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}