Exploiting Occlusion in Non-Line-of-Sight Active Imaging

Active non-line-of-sight imaging systems are of growing interest for diverse applications. The most commonly proposed approaches to date rely on exploiting time-resolved measurements, i.e., measuring the time it takes for short-duration light pulses to transit the scene. This typically requires expensive, specialized, ultrafast lasers, and detectors that must be carefully calibrated. We develop an alternative approach that exploits the valuable role that natural occluders in a scene play in enabling accurate and practical image formation in such settings without such hardware complexity. In particular, we demonstrate that the presence of occluders in the hidden scene can obviate the need for collecting time-resolved measurements, and develop an accompanying analysis for such systems and their generalizations. Ultimately, the results suggest the potential to develop increasingly sophisticated future systems that are able to identify and exploit diverse structural features of the environment to reconstruct scenes hidden from view.

[1]  Ting Sun,et al.  Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..

[2]  Kannan Ramchandran,et al.  Low-complexity image denoising based on statistical modeling of wavelet coefficients , 1999, IEEE Signal Processing Letters.

[3]  Robert Henderson,et al.  Detection and tracking of moving objects hidden from view , 2015, Nature Photonics.

[4]  Antonio Torralba,et al.  Accidental pinhole and pinspeck cameras: Revealing the scene outside the picture , 2012, CVPR.

[5]  Lior Horesh,et al.  Experimental Design for Nonparametric Correction of Misspecified Dynamical Models , 2017, SIAM/ASA J. Uncertain. Quantification.

[6]  Frédo Durand,et al.  Turning Corners into Cameras: Principles and Methods , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[8]  Christos Thrampoulidis,et al.  Revealing hidden scenes by photon-efficient occlusion-based opportunistic active imaging. , 2018, Optics express.

[9]  Ramesh Raskar,et al.  Lensless Imaging With Compressive Ultrafast Sensing , 2016, IEEE Transactions on Computational Imaging.

[10]  Xiaobai Sun,et al.  Reference structure tomography. , 2004, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Wolfgang Heidrich,et al.  Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  藤重 悟 Submodular functions and optimization , 1991 .

[13]  Rob Fergus,et al.  Fast Image Deconvolution using Hyper-Laplacian Priors , 2009, NIPS.

[14]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, ACM Trans. Graph..

[15]  Gregory W. Wornell,et al.  Sensor Array Design Through Submodular Optimization , 2017, IEEE Transactions on Information Theory.

[16]  Adam L. Cohen Anti-pinhole Imaging , 1982 .

[17]  Marc Teboulle,et al.  Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.

[18]  T. M. Cannon,et al.  Coded aperture imaging with uniformly redundant arrays. , 1978, Applied optics.

[19]  J. Besag,et al.  Bayesian image restoration, with two applications in spatial statistics , 1991 .

[20]  R. Raskar,et al.  Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging , 2012, Nature Communications.

[21]  K. Eliceiri,et al.  Non-line-of-sight imaging using a time-gated single photon avalanche diode. , 2015, Optics express.

[22]  Ramesh Raskar,et al.  Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing , 2007, ACM Trans. Graph..

[23]  Aswin C. Sankaranarayanan,et al.  FlatCam: Thin, Lensless Cameras Using Coded Aperture and Computation , 2017, IEEE Transactions on Computational Imaging.

[24]  Aswin C. Sankaranarayanan,et al.  FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation , 2015, ArXiv.

[25]  Jaime Martín,et al.  Tracking objects outside the line of sight using 2D intensity images , 2016, Scientific Reports.

[26]  Ramesh Raskar,et al.  Looking Around the Corner using Ultrafast Transient Imaging , 2011, International Journal of Computer Vision.

[27]  Rebecca Willett,et al.  This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms—Theory and Practice , 2010, IEEE Transactions on Image Processing.

[28]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.