{"doi":"10.1364/ol.397717","title":"Deep-learning-based image reconstruction for compressed ultrafast photography","abstract":"Compressed ultrafast photography (CUP) is a computational optical imaging technique that can capture transient dynamics at an unprecedented speed. Currently, the image reconstruction of CUP relies on iterative algorithms, which are time-consuming and often yield nonoptimal image quality. To solve this problem, we develop a deep-learning-based method for CUP reconstruction that substantially improves the image quality and reconstruction speed. A key innovation toward efficient deep learning reconstruction of a large three-dimensional (3D) event datacube ( <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"> <mml:mi>x</mml:mi> <mml:mo>,</mml:mo> <mml:mi>y</mml:mi> <mml:mo>,</mml:mo> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mi>t</mml:mi> </mml:mrow> </mml:math> ) ( <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"> <mml:mi>x</mml:mi> <mml:mo>,</mml:mo> <mml:mi>y</mml:mi> </mml:math> , spatial coordinate; <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"> <mml:mrow class=\"MJX-TeXAtom-ORD\"> <mml:mi>t</mml:mi> </mml:mrow> </mml:math> , time) is that we decompose the original datacube into massively parallel two-dimensional (2D) imaging subproblems, which are much simpler to solve by a deep neural network. We validated our approach on simulated and experimental data.","journal":"Optics Letters","year":2020,"id":101603,"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":27,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9541,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":482238,"name":"Xiaohua Feng","orcid":null,"position":1,"is_corresponding":false},{"id":227830,"name":"Liang Gao","orcid":"0000-0002-4296-5586","position":2,"is_corresponding":false},{"id":497303,"name":"Yayao Ma","orcid":"0000-0002-5078-9295","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":null,"created_at":"2026-07-18T22:40:22.678835Z","pmid":"32796968","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":[]}