{"id":241,"date":"2016-08-13T11:03:56","date_gmt":"2016-08-13T15:03:56","guid":{"rendered":"https:\/\/sites.bu.edu\/tianlab\/?page_id=241"},"modified":"2026-04-01T20:34:59","modified_gmt":"2026-04-02T00:34:59","slug":"research","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/tianlab\/research\/","title":{"rendered":"Research"},"content":{"rendered":"<p>Our lab develops\u00a0<strong>computational imaging methods<\/strong>,\u00a0which<em> jointly design optics, devices, signal processing and algorithms<\/em>, and enable novel capabilities that each one alone cannot. Our research is inherently interdisciplinary, combining expertise in <strong>optical engineering<\/strong>, <strong>physics\u00a0<\/strong>and<strong>\u00a0computation<\/strong>. We work on imaging technologies for biomedical, neuroscience, and semiconductor-related applications.<\/p>\n<hr \/>\n<h3><strong>Deep Learning for Computational Imaging\u00a0<\/strong><\/h3>\n<h4><span style=\"color: #800000;\"><img loading=\"lazy\" src=\"\/tianlab\/files\/2025\/11\/WSD-636x450.png\" alt=\"\" width=\"300\" height=\"212\" class=\"wp-image-2501 alignleft\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2025\/11\/WSD-636x450.png 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2025\/11\/WSD-1024x725.png 1024w, https:\/\/sites.bu.edu\/tianlab\/files\/2025\/11\/WSD-768x544.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2025\/11\/WSD-1536x1088.png 1536w, https:\/\/sites.bu.edu\/tianlab\/files\/2025\/11\/WSD.png 1870w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>Diffusion models<\/span><\/h4>\n<p>We work on diffusion models for solving inverse problems.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>Alido, et al, <a href=\"https:\/\/arxiv.org\/abs\/2505.10311\"><strong>Whitened Score Diffusion: A Structured Prior for Imaging Inverse Problems<\/strong><\/a>, NeurIPS, 2025.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2022\/09\/NF-IDT-636x363.png\" alt=\"\" width=\"300\" height=\"171\" class=\"alignleft wp-image-2008\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2022\/09\/NF-IDT-636x363.png 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/09\/NF-IDT-1024x585.png 1024w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/09\/NF-IDT-768x439.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/09\/NF-IDT-1536x877.png 1536w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/09\/NF-IDT-2048x1169.png 2048w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4><span style=\"color: #752929;\"><strong>Implicit neural representation<\/strong><\/span><\/h4>\n<p>We develop computational imaging techniques that leverage implicit neural representations.<span style=\"text-decoration: underline;\"><em><\/em><\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>Wang, et al, <strong><a href=\"https:\/\/doi.org\/10.1117\/1.APN.3.5.056005\">NeuPh: scalable and generalizable neural phase retrieval with local conditional neural fields<\/a><\/strong>, <em><strong>Advanced Photonics Nexus<\/strong><\/em> 3, 056005 (2024).<\/li>\n<li>Liu, et al, <a href=\"https:\/\/www.nature.com\/articles\/s42256-022-00530-3\"><strong>Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields<\/strong><\/a>, <em><strong>N<\/strong><strong>ature Machine Intelligence<\/strong><\/em><span>\u00a04<\/span><span>,\u00a0<\/span><span>781\u2013791\u00a0<\/span><span>(2022).<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2022\/03\/Uncertainty-584x636.png\" alt=\"\" width=\"300\" height=\"327\" class=\"alignleft wp-image-1828\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Uncertainty-584x636.png 584w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Uncertainty-940x1024.png 940w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Uncertainty-768x837.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Uncertainty-1410x1536.png 1410w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Uncertainty.png 1640w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4><span style=\"color: #752929;\"><strong>Reliable deep learning with Uncertainty Quantification<\/strong><\/span><\/h4>\n<p>We develop uncertainty quantification techniques to provide more reliable deep learning predictions for quantitative bio-imaging.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span>Xue, et al, <\/span><a href=\"https:\/\/www.osapublishing.org\/optica\/abstract.cfm?uri=optica-6-5-618\"><strong>Reliable deep learning-based phase imaging with uncertainty quantification<\/strong><\/a><span style=\"text-decoration: underline;\">,<\/span><a href=\"https:\/\/www.osapublishing.org\/optica\/abstract.cfm?uri=optica-6-5-618\"> <\/a><span><strong><em>Optica<\/em><\/strong>\u00a0<\/span><span>6<\/span><span>, 618-629 (2019)<\/span><span>.<\/span><\/li>\n<li>Liu,, et al. <strong><a href=\"https:\/\/www.nature.com\/articles\/s41377-019-0216-0\">Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification<\/a><\/strong>. <em><strong>Light: Science &amp; Applications<\/strong><\/em> 8.1 (2019): 102.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2022\/03\/DSN-636x441.png\" alt=\"\" width=\"300\" height=\"208\" class=\"wp-image-1834 alignleft\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/DSN-636x441.png 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/DSN-1024x710.png 1024w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/DSN-768x532.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/DSN-1536x1064.png 1536w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/DSN-2048x1419.png 2048w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4><span style=\"color: #800000;\">Adaptive deep learning<\/span><\/h4>\n<p>We work on adaptive deep learning framework to achieve robust imaging across a variety of conditions.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span>Tahir, et al, <\/span><a href=\"https:\/\/www.nature.com\/articles\/s41377-022-00730-x\"><strong>Adaptive 3D descattering with a dynamic synthesis network<\/strong><\/a>, <em><strong>Light: Science &amp; Applications<\/strong><\/em><span> 11, 42, 2022.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<hr \/>\n<h3><strong>Computational Microscopy<\/strong><\/h3>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2022\/03\/CM2-150x150.jpg\" alt=\"\" width=\"300\" height=\"235\" class=\"alignleft wp-image-1822\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/CM2-636x497.jpg 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/CM2-1024x801.jpg 1024w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/CM2-768x600.jpg 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/CM2.jpg 1219w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4 style=\"text-align: left;\"><span style=\"color: #752929;\"><strong>Computational Miniature Mesoscope (CM<sup>2<\/sup>)<\/strong><\/span><\/h4>\n<p style=\"text-align: left;\">We develop &#8220;wearable&#8221; computational fluorescence microscope that achieves cm-scale FOV and \u00b5m-scale resolution with single-shot 3D imaging capability. A complete publication list is <a href=\"https:\/\/sites.bu.edu\/tianlab\/publications\/comp-fluo-imaging\/\">here<\/a>.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span>Xue, et al, <\/span><a href=\"https:\/\/advances.sciencemag.org\/content\/6\/43\/eabb7508\"><strong>Single-Shot 3D Widefield Fluorescence Imaging with a Computational Miniature Mesoscope, <\/strong><\/a><em><strong>Science Advances<\/strong><\/em><span> 21 2020: EABB7508<\/span><\/li>\n<li><span>Xue, et al<\/span>, <strong><a href=\"https:\/\/opg.optica.org\/optica\/fulltext.cfm?uri=optica-9-9-1009&amp;id=497528\">Deep-learning-augmented computational miniature mesoscope<\/a><\/strong>, <strong><em>Optica<\/em><\/strong> 9, 1009-1021 (2022).<\/li>\n<li>Yang, et al, <a href=\"https:\/\/opg.optica.org\/optica\/fulltext.cfm?uri=optica-11-6-860&amp;id=552177\"><strong>Wide-field, high-resolution reconstruction in computational multi-aperture miniscope using a Fourier neural network<\/strong><\/a>, <em><strong>Optica<\/strong><\/em><span> 11, 860-871 (2024).<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4><\/h4>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2024\/06\/EventLFM-636x588.png\" alt=\"\" width=\"300\" height=\"277\" class=\"alignleft wp-image-2315\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/EventLFM-636x588.png 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/EventLFM-1024x947.png 1024w, https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/EventLFM-768x710.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/EventLFM-1536x1421.png 1536w, https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/EventLFM-2048x1894.png 2048w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>&nbsp;<\/p>\n<h4 style=\"text-align: left;\"><span style=\"color: #752929;\">Event-driven dynamic microscopy<\/span><\/h4>\n<p>We develop computational &#8220;event-driven&#8221; microscopy techniques to address challenges in dynamic imaging.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span>Guo, Ruipeng, et al. <a href=\"https:\/\/www.nature.com\/articles\/s41377-024-01502-5\"><em><strong>EventLFM: Event Camera integrated Fourier Light Field Microscopy for Ultrafast 3D imaging<\/strong><\/em><\/a>. <\/span><em><strong>Light: Science &amp; Applications<\/strong><\/em><span> 2024.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<h3><strong>Computational Metrology<br \/>\n<\/strong><\/h3>\n<p>We developed computational imaging techniques for semiconductor metrology and inspection applications.<\/p>\n<h4><span style=\"color: #752929;\"><strong><img loading=\"lazy\" src=\"\/tianlab\/files\/2026\/03\/mbs-636x489.jpeg\" alt=\"\" width=\"300\" height=\"231\" class=\"alignleft wp-image-2542\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2026\/03\/mbs-636x489.jpeg 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2026\/03\/mbs-1024x788.jpeg 1024w, https:\/\/sites.bu.edu\/tianlab\/files\/2026\/03\/mbs-768x591.jpeg 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2026\/03\/mbs-1536x1182.jpeg 1536w, https:\/\/sites.bu.edu\/tianlab\/files\/2026\/03\/mbs-2048x1575.jpeg 2048w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>Substrate-Enhanced Diffraction Tomography<\/strong><\/span><\/h4>\n<p><span>We develop a reflection-mode diffraction tomography technique that enables the simultaneous recovery of forward- and backward-scattering information for high-resolution 3D refractive index reconstruction.\u00a0<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>T. Li, et. al. <a href=\"https:\/\/opg.optica.org\/optica\/fulltext.cfm?uri=optica-13-4-661\"><strong>Transfer-function approach to substrate-enhanced diffraction tomography<\/strong><\/a>, <em><strong>Optica<\/strong><\/em>, 2026.<\/li>\n<li>J. Zhu, et. al. <a href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/11417435\"><strong>Reflection-mode Multi-slice Fourier Ptychographic Tomography<\/strong><\/a>, <strong><em>IEEE Transactions on Computational Imaging<\/em><\/strong><span>, 2026.<\/span><\/li>\n<li>T. Li, et. al. <a href=\"https:\/\/opg.optica.org\/optica\/fulltext.cfm?uri=optica-12-3-406&amp;id=569159\"><strong>Reflection-mode diffraction tomography of multiple-scattering samples on a reflective substrate from intensity images<\/strong><\/a>, <em><strong>Optica<\/strong><\/em>, 2025.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4><span style=\"color: #752929;\"><strong><img loading=\"lazy\" src=\"\/tianlab\/files\/2023\/11\/FPT-494x636.png\" alt=\"\" width=\"300\" height=\"386\" class=\"alignleft wp-image-2233\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2023\/11\/FPT-494x636.png 494w, https:\/\/sites.bu.edu\/tianlab\/files\/2023\/11\/FPT-795x1024.png 795w, https:\/\/sites.bu.edu\/tianlab\/files\/2023\/11\/FPT-768x989.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2023\/11\/FPT-1193x1536.png 1193w, https:\/\/sites.bu.edu\/tianlab\/files\/2023\/11\/FPT.png 1246w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/strong><\/span><\/h4>\n<h4><\/h4>\n<h4><\/h4>\n<h4><span style=\"color: #752929;\"><strong>Fourier Ptychograhpic Topography<\/strong><\/span><\/h4>\n<p>We develop novel topography techniques based on the reflection-mode Fourier ptychographic microscopy, termed Fourier ptychograhpic topography (FPT). FPT provides both a wide FOV and high resolution, and achieves nanoscale height reconstruction accuracy.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>H. Wang, et. al. <a href=\"https:\/\/opg.optica.org\/oe\/fulltext.cfm?uri=oe-31-7-11007&amp;id=528271\"><strong>Fourier ptychographic topography<\/strong><\/a>, <em><strong>Optics Express<\/strong><\/em> 31, 11007-11018 (2023).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<h3>Computational Phase Imaging<\/h3>\n<p>We work in the following major directions. A complete publication list is <a href=\"https:\/\/sites.bu.edu\/tianlab\/publications\/computational-phase-microscopy\/\">here<\/a>.<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2022\/03\/IDT-636x490.png\" alt=\"\" width=\"300\" height=\"231\" class=\"wp-image-1825 alignleft\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/IDT-636x490.png 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/IDT-1024x789.png 1024w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/IDT-768x592.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/IDT.png 1200w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4><span style=\"color: #752929;\"><strong>Intensity Diffraction Tomography<\/strong><\/span><\/h4>\n<p>We are developing high-speed computational 3D phase microscopy techniques by leveraging simple optical setups and advanced algorithms.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span>Li, et al, <\/span><a href=\"https:\/\/doi.org\/10.1117\/1.AP.1.6.066004\"><strong>High-speed in vitro intensity diffraction tomography<\/strong><\/a>, <strong><em>Advanced Photonics<\/em><\/strong><span>, 1(6)<\/span><span>, 066004 (2019).<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2022\/03\/FPM-636x481.png\" alt=\"\" width=\"300\" height=\"227\" class=\"wp-image-1864 alignleft\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/FPM-636x481.png 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/FPM-1024x775.png 1024w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/FPM-768x581.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/FPM-1536x1162.png 1536w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/FPM-2048x1550.png 2048w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4><span style=\"color: #800000;\">Fourier Ptychography\u00a0<\/span><\/h4>\n<p>We are working computational microscopy techniques to achieve gigapixel phase imaging.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>Tian, et al, <strong><a href=\"https:\/\/www.osapublishing.org\/optica\/abstract.cfm?uri=optica-2-10-904\" target=\"_blank\" rel=\"noopener noreferrer\">Computational illumination for high-speed in vitro Fourier ptychographic microscopy<\/a><\/strong>, <span><strong><em>Optica<\/em><\/strong>\u00a02(10), 904-911 (2015).<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2024\/06\/DPC-627x636.png\" alt=\"\" width=\"300\" height=\"304\" class=\"wp-image-2299 alignleft\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/DPC-627x636.png 627w, https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/DPC-1010x1024.png 1010w, https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/DPC-768x779.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/DPC-1514x1536.png 1514w, https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/DPC-2019x2048.png 2019w, https:\/\/sites.bu.edu\/tianlab\/files\/2024\/06\/DPC-100x100.png 100w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4><span style=\"color: #800000;\">Differential phase contrast microscopy<\/span><\/h4>\n<p>We are working computational microscopy techniques based on the principle of transfer function analysis.<span style=\"text-decoration: underline;\"><em><\/em><\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span>Tian and Waller. <a href=\"https:\/\/opg.optica.org\/oe\/fulltext.cfm?uri=oe-23-9-11394&amp;id=315599\"><strong>Quantitative differential phase contrast imaging in an LED array microscope<\/strong><\/a>.\u00a0<\/span><em><strong>Optics Express<\/strong><\/em><span>\u00a023.9 (2015): 11394-11403.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2022\/04\/BS-IDT-1-636x513.png\" alt=\"\" width=\"300\" height=\"242\" class=\"wp-image-1881 alignleft\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2022\/04\/BS-IDT-1-636x513.png 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/04\/BS-IDT-1-1024x826.png 1024w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/04\/BS-IDT-1-768x619.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/04\/BS-IDT-1-1536x1239.png 1536w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/04\/BS-IDT-1-2048x1652.png 2048w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4><span style=\"color: #800000;\">Computational label-free chemical microscopy<\/span><\/h4>\n<p>We are working computational label-free microscopy techniques with chemically specific information.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>Zhao, et al, <a href=\"https:\/\/www.nature.com\/articles\/s41467-022-35329-8\"><strong>Bond-Selective Intensity Diffraction Tomography<\/strong><\/a>, <strong><i>Nat Commun<\/i><\/strong><span>\u00a0<\/span>13<span>, 7767 (2022).<\/span><strong><br \/>\n<\/strong><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<h3>Deep learning for Quantitative Bio-imaging<\/h3>\n<p>We work in the following major directions.\u00a0 A complete publication list is <a href=\"https:\/\/sites.bu.edu\/tianlab\/publications\/deep-learning-for-biomedical-imaging\/\">here<\/a>.<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2022\/03\/Neural-signal-630x636.png\" alt=\"\" width=\"300\" height=\"303\" class=\"wp-image-1824 alignleft\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Neural-signal-630x636.png 630w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Neural-signal-1015x1024.png 1015w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Neural-signal-768x775.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Neural-signal-100x100.png 100w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/Neural-signal.png 1485w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><strong><\/strong><\/p>\n<h4><span style=\"color: #752929;\"><strong>Deep learning for in-vivo neural signal modeling &amp; extraction<\/strong><\/span><\/h4>\n<p>We develop various deep learning techniques to enhance and analyze in-vivo neural signals. A complete publication list is <a href=\"https:\/\/sites.bu.edu\/tianlab\/publications\/computational-neurophotonics\/\">here<\/a>.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>Chang, et al, <a href=\"https:\/\/www.spiedigitallibrary.org\/journals\/neurophotonics\/volume-11\/issue-04\/045007\/DeepVID-v2--self-supervised-denoising-with-decoupled-spatiotemporal-enhancement\/10.1117\/1.NPh.11.4.045007.full\"><strong>DeepVID v2: self-supervised denoising with decoupled spatiotemporal enhancement for low-photon voltage imaging<\/strong><\/a>, <em><strong>Neurophotonics<\/strong><\/em>, 2024.<\/li>\n<li><span>Platisa, et al, <a href=\"https:\/\/doi.org\/10.1038\/s41592-023-01820-3\"><strong>High-speed low-light in vivo two-photon voltage imaging of large neuronal populations<\/strong><\/a><\/span>, <em><strong>Nature Methods<\/strong><\/em> 20<span>,\u00a0<\/span><span class=\"u-visually-hidden\">pages<\/span><span>1095\u20131103 (<\/span><span data-test=\"article-publication-year\">2023<\/span><span>)<\/span><span class=\"highwire-cite-metadata-pages highwire-cite-metadata\">.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2022\/03\/digital_stain.png\" alt=\"\" width=\"300\" height=\"301\" class=\"wp-image-1826 alignleft\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/digital_stain.png 568w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/digital_stain-150x150.png 150w, https:\/\/sites.bu.edu\/tianlab\/files\/2022\/03\/digital_stain-100x100.png 100w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4><strong><span style=\"color: #752929;\">Cross-modality information transfer<\/span><\/strong><\/h4>\n<p>We work on deep learning technique that allows knowledge transfer across different bio-imaging modalities.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>Cheng, et al, <strong><a href=\"https:\/\/advances.sciencemag.org\/content\/7\/3\/eabe0431\">Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy<\/a>, <\/strong><cite><strong>Science Advances<\/strong>\u00a0<\/cite><span>\u00a015 Jan 2021:\u00a0<\/span><span>Vol. 7, no. 3, eabe0431.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<h3>Computational Imaging in Complex Media<\/h3>\n<p>We work in the following major directions.\u00a0 A complete publication list is <a href=\"https:\/\/sites.bu.edu\/tianlab\/publications\/imaging-through-scattering\/\">here<\/a>.<\/p>\n<p><img loading=\"lazy\" src=\"\/tianlab\/files\/2021\/04\/ExvS1SpWgAE6idk-627x636.jpeg\" alt=\"\" width=\"300\" height=\"304\" class=\"alignleft wp-image-1619\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2021\/04\/ExvS1SpWgAE6idk-627x636.jpeg 627w, https:\/\/sites.bu.edu\/tianlab\/files\/2021\/04\/ExvS1SpWgAE6idk-1010x1024.jpeg 1010w, https:\/\/sites.bu.edu\/tianlab\/files\/2021\/04\/ExvS1SpWgAE6idk-768x779.jpeg 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2021\/04\/ExvS1SpWgAE6idk-100x100.jpeg 100w, https:\/\/sites.bu.edu\/tianlab\/files\/2021\/04\/ExvS1SpWgAE6idk.jpeg 1144w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p>\n<h4><span style=\"color: #752929;\"><strong>Multiple-scattering modeling &amp; Physics-based deep learning<\/strong><\/span><\/h4>\n<p>We develop efficient and accurate multiple-scattering models to enable large-scale simulation of multiple-scattering in biological samples. These multiple scattering models form the foundation to develop physics-based deep learning models to achieve accurate 3D phase reconstructions. A complete publication list is <a href=\"https:\/\/sites.bu.edu\/tianlab\/publications\/physics-embedded-deep-learning\/\">here<\/a>.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>Matlock, Zhu, Tian, <a href=\"https:\/\/opg.optica.org\/oe\/fulltext.cfm?uri=oe-31-3-4094&amp;id=525403\"><strong>Multiple-scattering simulator-trained neural network for intensity diffraction tomography<\/strong><\/a>, <strong><em>Optics Express<\/em><\/strong> 31, 4094-4107 (2023).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<h3>Computational imaging with non-conventional optics<\/h3>\n<h4><img loading=\"lazy\" src=\"\/tianlab\/files\/2020\/04\/ASP-1024x701-1-636x435.png\" alt=\"\" width=\"300\" height=\"205\" class=\"wp-image-1386 alignleft\" srcset=\"https:\/\/sites.bu.edu\/tianlab\/files\/2020\/04\/ASP-1024x701-1-636x435.png 636w, https:\/\/sites.bu.edu\/tianlab\/files\/2020\/04\/ASP-1024x701-1-768x526.png 768w, https:\/\/sites.bu.edu\/tianlab\/files\/2020\/04\/ASP-1024x701-1.png 1024w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><span style=\"color: #800000;\">Computational Imaging with Metasurface Photodetectors<\/span><\/h4>\n<p>We develop computational imaging techniques for metasurface photodetectors to achieve non-conventional imaging capabilities. A complete publication list is <a href=\"https:\/\/sites.bu.edu\/tianlab\/publications\/computational-imaging-with-metasurface\/\">here<\/a>.<\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>Kogos, et al, <a href=\"https:\/\/www.nature.com\/articles\/s41467-020-15460-0\"><strong>Plasmonic ommatidia for lensless compound-eye vision<\/strong><\/a>, <strong><em>Nat. Communications<\/em><\/strong><span>\u00a011: 1637 (2020).<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h4><span style=\"color: #800000;\">\u00a0<\/span><\/h4>\n","protected":false},"excerpt":{"rendered":"<p>Our lab develops\u00a0computational imaging methods,\u00a0which jointly design optics, devices, signal processing and algorithms, and enable novel capabilities that each one alone cannot. Our research is inherently interdisciplinary, combining expertise in optical engineering, physics\u00a0and\u00a0computation. We work on imaging technologies for biomedical, neuroscience, and semiconductor-related applications. Deep Learning for Computational Imaging\u00a0 Diffusion models We work on diffusion [&hellip;]<\/p>\n","protected":false},"author":12228,"featured_media":0,"parent":0,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"page-templates\/no-sidebars.php","meta":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/pages\/241"}],"collection":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/users\/12228"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/comments?post=241"}],"version-history":[{"count":50,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/pages\/241\/revisions"}],"predecessor-version":[{"id":2553,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/pages\/241\/revisions\/2553"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/media?parent=241"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}