\nYingming Lai, Yujia Xue, Christian\u2010Yves C\u00f4t\u00e9, Xianglei Liu, Antoine,Laram\u00e9e, Nicolas Jaouen, Fran\u00e7ois L\u00e9gar\u00e9, Lei Tian, Jinyang Liang
\nLaser & Photonics Reviews<\/strong><\/em> 2020, 14, 2000122.
\n\u2b51<\/strong> <\/em><\/strong><\/span>on the cover story<\/a><\/span><\/strong><\/span><\/span><\/p>\n
Compressed ultrafast photography (CUP) is an emerging potent technique that allows imaging a nonrepeatable or difficult\u2010to\u2010produce transient event in a single shot. Despite many recent advances, existing CUP techniques operate only at visible and near\u2010infrared wavelengths. In addition, spatial encoding via a digital micromirror device (DMD) in CUP systems often limits its field of view and imaging speeds. Finally, conventional reconstruction algorithms have limited control of the reconstruction process to further improve the image quality in the recovered datacubes of the scene. To overcome these limitations, this article reports a single\u2010shot UV\u2010CUP that exhibits a sequence depth of up to 1500 frames with a size of 1750 \u00d7 500 pixels at an imaging speed of 0.5 trillion frames per second. A patterned photocathode is integrated into a streak camera, which overcomes the previous restrictions in DMD\u2010based spatial encoding and improves the system’s compactness. Meanwhile, the plug\u2010and\u2010play alternating direction method of multipliers algorithm is implemented to CUP’s image reconstruction to enhance reconstructed image quality. UV\u2010CUP’s single\u2010shot ultrafast imaging ability is demonstrated by recording UV pulses transmitting through various spatial patterns. UV\u2010CUP is expected to find many applications in both fundamental and applied science.<\/p>\n
![\"\"](\"\/tianlab\/files\/2020\/10\/CUP-UV-485x636.png\")
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Compressive holographic video<\/strong><\/a> Compressed sensing has been discussed separately in spatial and temporal domains. Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image. Coded exposure is a temporal compressed sensing method for high speed video acquisition. In this work, we combine compressive holography and coded exposure techniques and extend the discussion to 4D reconstruction in space and time from one coded captured image. In our prototype, digital in-line holography was used for imaging macroscopic, fast moving objects. The pixel-wise temporal modulation was implemented by a digital micromirror device. In this paper we demonstrate 10\u00d7 temporal super resolution with multiple depths recovery from a single image. Two examples are presented for the purpose of recording subtle vibrations and tracking small particles within 5 ms.<\/span><\/p>\n <\/p>\n 3D imaging in volumetric scattering media using phase-space measurements<\/strong><\/a> We demonstrate the use of phase-space imaging for 3D localization of multiple point sources inside scattering material. The effect of scattering is to spread angular (spatial frequency) information, which can be measured by phase space imaging. We derive a multi-slice forward model for homogenous volumetric scattering, then develop a reconstruction algorithm that exploits sparsity in order to further constrain the problem. By using 4D measurements for 3D reconstruction, the dimensionality mismatch provides significant robustness to multiple scattering, with either static or dynamic diffusers. Experimentally, our high-resolution 4D phase-space data is collected by a spectrogram setup, with results successfully recovering the 3D positions of multiple LEDs embedded in turbid scattering media.<\/p>\n Empirical concentration bounds for compressive holographic bubble imaging based on a Mie scattering model We use compressive in\u2013line holography to image air bubbles in water and investigate the effect of bubble concentration on reconstruction performance by simulation. Our forward model treats bubbles as finite spheres and uses Mie scattering to compute the scattered field in a physically rigorous manner. Although no simple analytical bounds on maximum concentration can be derived within the classical compressed sensing framework due to the complexity of the forward model, the receiver operating characteristic (ROC) curves in our simulation provide an empirical concentration bound for accurate bubble detection by compressive holography at different noise levels, resulting in a maximum tolerable concentration much higher than the traditional back-propagation method.<\/p>\n Compressive holographic two-dimensional localization with 1\/302<\/sup> subpixel accuracy We propose the use of compressive holography for two\u2013dimensional (2D) subpixel motion localization. Our approach is based on computational implementation of edge\u2013extraction using a Fourier\u2013plane spiral phase mask, followed by compressive reconstruction of the edge of the object. Using this technique and relatively low\u2013cost computer and piezo motion stage to establish ground truth for the motion, we demonstrated localization within 1\/30th of a camera pixel in each linear dimension.<\/p>\n Compressive X-ray phase tomography based on the transport of intensity equation<\/strong><\/a> We develop and implement a compressive reconstruction method for tomographic recovery of refractive index distribution for weakly attenuating objects in a microfocus x-ray system. This is achieved through the development of a discretized operator modeling both the transport of intensity equation and the x-ray transform that is suitable for iterative reconstruction techniques.<\/p>\n Scanning-free compressive holography for object localization with subpixel accuracy We propose quantitative localization measurement of a known object with subpixel accuracy using compressive holography. We analyze the theoretical optimal solution in the compressive sampling framework and experimentally demonstrate localization accuracy of 1\/45 pixel, in good agreement with the analysis.<\/p>\n Experimental compressive phase space tomography Phase space tomography estimates correlation functions entirely from snapshots in the evolution of the wave function along a time or space variable. In contrast, traditional interferometric methods require measurement of multiple two-point correlations. However, as in every tomographic formulation, undersampling poses a severe limitation. Here we present the first, to our knowledge, experimental demonstration of compressive reconstruction of the classical optical correlation function, i.e. the mutual intensity function. Our compressive algorithm makes explicit use of the physically justifiable assumption of a low-entropy source (or state.) Since the source was directly accessible in our classical experiment, we were able to compare the compressive estimate of the mutual intensity to an independent ground-truth estimate from the van Cittert-Zernike theorem and verify substantial quantitative improvements in the reconstruction.<\/p>\n <\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Exploiting sparsity in vectors (e.g. L1-norm, Total variation) Single-Shot Ultraviolet Compressed Ultrafast Photography Yingming Lai, Yujia Xue, Christian\u2010Yves C\u00f4t\u00e9, Xianglei Liu, Antoine,Laram\u00e9e, Nicolas Jaouen, Fran\u00e7ois L\u00e9gar\u00e9, Lei Tian, Jinyang Liang Laser & Photonics Reviews 2020, 14, 2000122. \u2b51 on the cover story Compressed ultrafast photography (CUP) is an emerging potent technique that allows imaging a […]<\/p>\n","protected":false},"author":12228,"featured_media":0,"parent":133,"menu_order":12,"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\/285"}],"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=285"}],"version-history":[{"count":5,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/pages\/285\/revisions"}],"predecessor-version":[{"id":1844,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/pages\/285\/revisions\/1844"}],"up":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/pages\/133"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/media?parent=285"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Zihao Wang, Leonidas Spinoulas, Kuan He, Lei Tian, Oliver Cossairt, Aggelos K. Katsaggelos, and Huaijin Chen
Opt. Express\u00a025, 250-262 (2017)<\/span>.<\/p>\n![\"dhv\"](\"\/tianlab\/files\/2017\/01\/DHV.jpeg\")
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\nH. Liu, E. Jonas, L. Tian, J. Zhong, B. Recht, L. Waller
\nOpt. Express 23, 14461-14471 (2015).<\/p>\n![\"PhaseSpaceScattering\"](\"\/tianlab\/files\/2016\/08\/PhaseSpaceScattering1-636x393.png\")
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\n<\/a><\/strong>W. Chen, Lei Tian, S. Rehman, Z. Zhang, H. P. Lee, G. Barbastathis
\n<\/span>Opt. Express 23, (2015).<\/span><\/p>\n
\n<\/a><\/strong>Y. Liu, Lei Tian, C. Hsieh, G. Barbastathis
\n<\/span>Optics Express 22, 9774-9782 (2014).<\/span><\/p>\n![\"subpixel2D\"](\"\/tianlab\/files\/2016\/08\/subpixel2D-636x341.png\"){kind=tracking}
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\nLei Tian, J. C. Petruccelli, Q. Miao, H. Kudrolli, V. Nagarkar, G. Barbastathis
\nOptics Letters 38, 3418-3421 (2013).<\/p>\n![\"xrayphase\"](\"\/tianlab\/files\/2016\/08\/xrayphase-636x329.png\")
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\n<\/a><\/strong>Y. Liu, Lei Tian, J. W. Lee, H. Y. H. Huang, M. S. Triantafyllou, G. Barbastathis
\nOptics Letters 37, 3357-3359 (2012).<\/p>\n![\"subpixel1D\"](\"\/tianlab\/files\/2016\/08\/subpixel1D-621x636.png\"){kind=tracking}
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Exploiting sparsity (low-rankness) in matrices<\/h4>\n
\n<\/a><\/strong>Lei Tian, J. Lee, S. B. Oh, G. Barbastathis
\n<\/span>Optics Express 20, 8296-8308 (2012).
\n<\/span>*Highlighted in the OSA Spotlight on Optics<\/a><\/strong><\/p>\n![\"PST\"](\"\/tianlab\/files\/2016\/08\/PST-456x636.png\")
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