Compressive line sensing underwater imaging system

Compressive sensing (CS) theory has drawn great interest and led to new imaging techniques in many different fields. In recent years, the FAU/HBOI OVOL has conducted extensive research to study the CS based active electro-optical imaging system in the scattering medium such as the underwater environment. The unique features of such system in comparison with the traditional underwater electro-optical imaging system are discussed. Building upon the knowledge from the previous work on a frame based CS underwater laser imager concept, more advantageous for hover-capable platforms such as the Hovering Autonomous Underwater Vehicle (HAUV), a compressive line sensing underwater imaging (CLSUI) system that is more compatible with the conventional underwater platforms where images are formed in whiskbroom fashion, is proposed in this paper. Simulation results are discussed.

[1]  Massimo Fornasier,et al.  Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.

[2]  Thomas E. Giddings,et al.  Numerical simulation of the incoherent electro-optical imaging process in plane-stratified media , 2009 .

[3]  Bing Ouyang,et al.  Compressive sensing underwater laser serial imaging system , 2013, J. Electronic Imaging.

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

[5]  Aaron D. Wyner,et al.  The rate-distortion function for source coding with side information at the decoder , 1976, IEEE Trans. Inf. Theory.

[6]  R. Baraniuk,et al.  Compressive Radar Imaging , 2007, 2007 IEEE Radar Conference.

[7]  B. C. Redman,et al.  Streak Tube Imaging LIDAR (STIL) for 3-D Imaging of Terrestrial Targets , 2000 .

[8]  Jahja I. Trisnadi,et al.  Overview and applications of Grating-Light-Valve-based optical write engines for high-speed digital imaging , 2004, SPIE MOEMS-MEMS.

[9]  Fraser Dalgleish,et al.  Synchronous Laser Line Scanners for Undersea Imaging Applications , 2011 .

[10]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[11]  E. Candès,et al.  Sparsity and incoherence in compressive sampling , 2006, math/0611957.

[12]  Walter M. Duncan,et al.  Emerging digital micromirror device (DMD) applications , 2003, SPIE MOEMS-MEMS.

[13]  R. A. McDonald,et al.  Noiseless Coding of Correlated Information Sources , 1973 .

[14]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[15]  Y. Rachlin,et al.  The secrecy of compressed sensing measurements , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[16]  Thomas J. Kulp,et al.  Results of the final tank test of the LLNL/NAVSEA synchronous-scanning underwater laser imaging system , 1992, Optics & Photonics.

[17]  Jianwei Ma,et al.  Single-Pixel Remote Sensing , 2009, IEEE Geoscience and Remote Sensing Letters.

[18]  J. L. Forand,et al.  Range-gated underwater laser imaging system , 1993 .

[19]  F. Dalgleish,et al.  Extended range distributed laser serial imaging in turbid estuarine and coastal conditions , 2012, 2012 Oceans.

[20]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[21]  Jules S. Jaffe,et al.  Computer modeling and the design of optimal underwater imaging systems , 1990 .

[22]  Trac D. Tran,et al.  Fast compressive sampling with structurally random matrices , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.