Compressive Holographic Video

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× 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.

[1]  Lu Gan Block Compressed Sensing of Natural Images , 2007, 2007 15th International Conference on Digital Signal Processing.

[2]  José M. Bioucas-Dias,et al.  A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.

[3]  Hong Qiao,et al.  GNCCP—Graduated NonConvexityand Concavity Procedure , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Ashok Veeraraghavan,et al.  Flexible Voxels for Motion-Aware Videography , 2010, ECCV.

[5]  Lei Tian,et al.  Empirical concentration bounds for compressive holographic bubble imaging based on a Mie scattering model. , 2015, Optics express.

[6]  Peter Kohl,et al.  Temporal Pixel Multiplexing for simultaneous high-speed high-resolution imaging , 2010, Nature Methods.

[7]  B. Javidi,et al.  Compressive Fresnel Holography , 2010, Journal of Display Technology.

[8]  Baozhen Ge,et al.  Trajectory and velocity measurement of a particle in spray by digital holography. , 2009, Applied optics.

[9]  Daniel L Marks,et al.  Compressive holography. , 2009, Optics express.

[10]  Lei Tian,et al.  Compressive holographic two-dimensional localization with 1/30(2) subpixel accuracy. , 2014, Optics express.

[11]  J. Katz,et al.  Applications of Holography in Fluid Mechanics and Particle Dynamics , 2010 .

[12]  Joseph N Mait,et al.  Millimeter-wave compressive holography. , 2010, Applied optics.

[13]  Bahram Javidi,et al.  Depth-independent segmentation of macroscopic three-dimensional objects encoded in single perspectives of digital holograms. , 2007, Optics letters.

[14]  David J. Brady,et al.  Sampling and processing for compressive holography [Invited]. , 2011, Applied optics.

[15]  L. Tian,et al.  Scanning-free compressive holography for object localization with subpixel accuracy. , 2012, Optics letters.

[16]  Ramesh Raskar,et al.  Coded exposure photography: motion deblurring using fluttered shutter , 2006, SIGGRAPH '06.

[17]  D. Gabor A New Microscopic Principle , 1948, Nature.

[18]  Pasquale Memmolo,et al.  Recent advances in holographic 3D particle tracking , 2015 .

[19]  Hakho Lee,et al.  Sparsity-Based Pixel Super Resolution for Lens-Free Digital In-line Holography , 2016, Scientific Reports.

[20]  Guido Schuster,et al.  High spatio-temporal resolution video with compressed sensing. , 2015, Optics express.

[21]  M H Jericho,et al.  Tracking particles in four dimensions with in-line holographic microscopy. , 2003, Optics letters.

[22]  L. Tian,et al.  Relaxation of mask design for single-shot phase imaging with a coded aperture. , 2016, Applied optics.

[23]  Shree K. Nayar,et al.  Efficient Space-Time Sampling with Pixel-Wise Coded Exposure for High-Speed Imaging , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  L. Tian,et al.  Quantitative measurement of size and three-dimensional position of fast-moving bubbles in air-water mixture flows using digital holography. , 2010, Applied optics.

[25]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[26]  A. Asundi,et al.  Studies of digital microscopic holography with applications to microstructure testing. , 2001, Applied optics.

[27]  Myung K. Kim,et al.  Review of digital holographic microscopy for three-dimensional profiling and tracking , 2014 .

[28]  D. Brady,et al.  Video-rate compressive holographic microscopic tomography. , 2011, Optics express.

[29]  Peter Klages,et al.  Digital in-line holographic microscopy. , 2006, Applied optics.

[30]  W Xu,et al.  Digital in-line holography for biological applications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Fook Chiong Cheong,et al.  Holographic deconvolution microscopy for high-resolution particle tracking. , 2011, Optics express.

[32]  M. Lustig,et al.  Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.

[33]  Guillermo Sapiro,et al.  Coded aperture compressive temporal imaging , 2013, Optics express.

[34]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[35]  Aydogan Ozcan,et al.  High-throughput lensfree 3D tracking of human sperms reveals rare statistics of helical trajectories , 2012, Proceedings of the National Academy of Sciences.

[36]  Elsa D. Angelini,et al.  Compressed Sensing with off-axis frequency-shifting holography , 2010, Optics letters.

[37]  J. Tanida,et al.  Single-shot phase imaging with a coded aperture. , 2014, Optics letters.

[38]  Torgny E. Carlsson,et al.  Simultaneous measurement of shape and deformation using digital light-in-flight recording by holography , 2000 .

[39]  K. Jacobson,et al.  Single-particle tracking: applications to membrane dynamics. , 1997, Annual review of biophysics and biomolecular structure.

[40]  Rama Chellappa,et al.  P2C2: Programmable pixel compressive camera for high speed imaging , 2011, CVPR 2011.

[41]  D. Allano,et al.  Application of multiple exposure digital in-line holography to particle tracking in a Bénard–von Kármán vortex flow , 2008 .

[42]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.