Computational LADAR imaging.

Most LADAR (laser radar, LIDAR) imaging systems use pixel-basis sampling, where each azimuth and elevation resolution element is uniquely sampled and recorded. We demonstrate and examine alternative sampling and post-detection processing schemes where recorded measurements are made in alternative bases that are intended to reduce system power consumption and laser emissions. A prototype of such a sensor having the capability to generate arbitrary illumination beam patterns rather than spot, line scanning, or flash techniques is described along with computational imaging algorithms to reduce speckle and identify scene objects in a low-dimensional compressed basis rather than in the pixel basis. Such techniques yield considerable energy savings and prove valuable when used on platforms with severe limitations on sensor size, weight, and power, and in particular as part of autonomous systems where image output for human interpretation is unnecessary.

[1]  I. Daubechies,et al.  An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.

[2]  Rama Chellappa,et al.  Separated Component-Based Restoration of Speckled SAR Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Vivek K Goyal,et al.  Exploiting sparsity in time-of-flight range acquisition using a single time-resolved sensor. , 2011, Optics express.

[4]  R. Gerchberg.  A practical algorithm for the determination of phase from image and diffraction plane pictures , 1972 .

[5]  Brent Schwarz,et al.  LIDAR: Mapping the world in 3D , 2010 .

[6]  Michael Elad,et al.  Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .

[7]  Michael A. Powers,et al.  Brassboard development of a MEMS-scanned ladar sensor for small ground robots , 2011, Defense + Commercial Sensing.

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

[9]  Thomas Brox,et al.  On the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and SIDEs , 2004, SIAM J. Numer. Anal..

[10]  R. Collins,et al.  Long-range time-of-flight scanning sensor based on high-speed time-correlated single-photon counting. , 2009, Applied optics.

[11]  Mohamed-Jalal Fadili,et al.  Morphological Component Analysis: An Adaptive Thresholding Strategy , 2007, IEEE Transactions on Image Processing.

[12]  Michael Elad,et al.  L1-L2 Optimization in Signal and Image Processing , 2010, IEEE Signal Processing Magazine.

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

[14]  Mitchell Jones,et al.  Development, integration, testing, and evaluation of the U.S. Army Buckeye System to the NAVAIR Arrow UAV , 2008, SPIE Defense + Commercial Sensing.

[15]  J. Howell,et al.  Photon-counting compressive sensing laser radar for 3D imaging. , 2011, Applied optics.

[16]  Vivek K. Goyal,et al.  Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors , 2014, IEEE Transactions on Computational Imaging.

[17]  Michael P. Friedlander,et al.  Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..

[18]  L. Coldren,et al.  Fully integrated hybrid silicon two dimensional beam scanner. , 2015, Optics express.

[19]  J. Goodman.  Some fundamental properties of speckle , 1976 .

[20]  Robert Dean.  Common world model for unmanned systems , 2013, Defense, Security, and Sensing.