High dynamic range compressive imaging: a programmable imaging system

Some scenes and objects have a wide range of brightness that cannot be captured with a conventional camera. This limitation, which degrades the dynamic range of an imaged scene or object, is addressed by use of high dynamic range (HDR) imaging techniques. With HDR ima- ging techniques, images of a very broad range of intensity can be obtained with conventional cameras. Another limitation of conventional cameras is the range of wavelength that they can capture. Outside the visible wave- lengths, the responsivity of conventional cameras drops; therefore, a con- ventional camera cannot capture images in nonvisible wavelengths. Compressive imaging is a solution for this problem. Compressive imaging reduces the number of pixels of a camera to one, so a camera can be replaced by a detector with one pixel. The range of wavelengths to which such detectors are responsive is much wider than that of a conven- tional camera. A combination of HDR imaging and compressive imaging is introduced and is benefitted from the advantages of both techniques. An algorithm that combines these two techniques is proposed, and results are presented. © 2012 Society of Photo-Optical Instrumentation Engineers

[1]  Jong Chul Ye,et al.  Performance evaluation of accelerated functional MRI acquisition using compressed sensing , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[2]  Richard G. Baraniuk,et al.  Democracy in Action: Quantization, Saturation, and Compressive Sensing , 2011 .

[3]  C. Jutten,et al.  Compressed sensing Block MAP-LMS adaptive filter for sparse channel estimation and a Bayesian Cramer-Rao bound , 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

[4]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.

[5]  Shree K. Nayar,et al.  High dynamic range imaging: spatially varying pixel exposures , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[6]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics) , 2005 .

[7]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH '08.

[8]  Yoram Bresler,et al.  Optimal sub-Nyquist nonuniform sampling and reconstruction for multiband signals , 2001, IEEE Trans. Signal Process..

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

[10]  S. Kirolos,et al.  Random Sampling for Analog-to-Information Conversion of Wideband Signals , 2006, 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software.

[11]  Compressive Quantum Ghost Imaging , 2011 .

[12]  Neil Barakat,et al.  Applications of digital micro-mirror devices to digital optical microscope dynamic range enhancement. , 2009, Optics express.

[13]  Adrian Stern,et al.  Compressed Imaging With a Separable Sensing Operator , 2009, IEEE Signal Processing Letters.

[14]  Vahid Tarokh,et al.  SPARLS: The Sparse RLS Algorithm , 2010, IEEE Transactions on Signal Processing.

[15]  Shree K. Nayar,et al.  Computational Cameras: Redefining the Image , 2006, Computer.

[16]  Faramarz Farahi,et al.  Active illumination single-pixel camera based on compressive sensing. , 2011, Applied optics.

[17]  Aswin C. Sankaranarayanan,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[18]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[19]  S. Kirolos,et al.  DepartmentofElectricalandComputerEngineering,RiceUniversity,Houston,TX DepartmentsofMathematicsandEECS,UniversityofMichigan,AnnArbor,MI , 2022 .

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

[21]  D. Donoho,et al.  Uncertainty principles and signal recovery , 1989 .

[22]  Richard G. Baraniuk,et al.  Terahertz imaging with compressed sensing and phase retrieval , 2008 .

[23]  Shengli Zhou,et al.  Sparse channel estimation for OFDM: Over-complete dictionaries and super-resolution , 2009, 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications.

[24]  Shree K. Nayar,et al.  Radiometric self calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[25]  R. Weissleder,et al.  Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging , 2002, European Radiology.

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

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

[28]  Kousuke Moriwaki.  Adaptive exposure image input system for obtaining high-quality color information , 1994, Systems and Computers in Japan.

[29]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[30]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting , 2010 .

[31]  Olgica Milenkovic,et al.  Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.

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

[33]  Fadil Santosa,et al.  INVERSION OF IMPEDANCE PROFILE FROM BAND-LIMITED DATA. , 1983 .

[34]  Trac D. Tran,et al.  Fast compressive imaging using scrambled block Hadamard ensemble , 2008, 2008 16th European Signal Processing Conference.

[35]  Richard G. Baraniuk,et al.  Compressive Sensing DNA Microarrays , 2008, EURASIP J. Bioinform. Syst. Biol..

[36]  Stephen P. Boyd,et al.  Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.

[37]  D. L. Donoho,et al.  Rapid MR Imaging with "Compressed Sensing" and Randomly Under-Sampled 3DFT Trajectories , 2004 .

[38]  Justin K. Romberg,et al.  Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.

[39]  Vahid Tarokh,et al.  Shannon-Theoretic Limits on Noisy Compressive Sampling , 2007, IEEE Transactions on Information Theory.

[40]  H. Rauhut.  Random Sampling of Sparse Trigonometric Polynomials , 2005, math/0512642.

[41]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Emmanuel J. Candès,et al.  NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..

[43]  Joel A. Tropp,et al.  Recovery of short, complex linear combinations via /spl lscr//sub 1/ minimization , 2005, IEEE Transactions on Information Theory.

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

[45]  Richard G. Baraniuk,et al.  An Architecture for Compressive Imaging , 2006, 2006 International Conference on Image Processing.

[46]  Yasushi Yagi,et al.  High Dynamic Range Camera using Reflective Liquid Crystal , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[47]  T. Nakano,et al.  Wide dynamic range vision sensor for vehicles , 1994, Proceedings of VNIS'94 - 1994 Vehicle Navigation and Information Systems Conference.

[48]  Baoxin Li,et al.  Compressive imaging of color images , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.