A study of the coherence parameter of the progressive compressive imager based on radon transform

We have recently introduced a progressive compressing sensing method based on an appropriate sampling scheme of optical Radon projections. By choosing the sampling steps to be of the size of the golden angle new information is optimally acquired with each angular sampling step thus permitting gradual improvement of the reconstructed image. A comparison between the progressive sampling scheme with the conventional one based on uniform sampling is given in terms of their coherence parameter.

[1]  Michael Elad,et al.  Optimized Projections for Compressed Sensing , 2007, IEEE Transactions on Signal Processing.

[2]  J. Romberg,et al.  Imaging via Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[3]  Jun Tanida,et al.  Generalized sampling using a compound-eye imaging system for multi-dimensional object acquisition. , 2010, Optics express.

[4]  Richard G. Baraniuk,et al.  A new compressive imaging camera architecture using optical-domain compression , 2006, Electronic Imaging.

[5]  Ofer Levi,et al.  Progressive compressive imaging from Radon projections. , 2012, Optics express.

[6]  Y. Rivenson,et al.  Practical compressive sensing of large images , 2009, 2009 16th International Conference on Digital Signal Processing.

[7]  Bahram Javidi,et al.  Single exposure super-resolution compressive imaging by double phase encoding. , 2010, Optics express.

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

[9]  A. Stern,et al.  Random Projections Imaging With Extended Space-Bandwidth Product , 2007, Journal of Display Technology.

[10]  Yonina C. Eldar,et al.  Super-resolution and reconstruction of sparse sub-wavelength images. , 2009, Optics express.

[11]  Michael Elad,et al.  On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations , 2008, IEEE Transactions on Information Theory.

[12]  Adrian Stern,et al.  Compressed imaging system with linear sensors. , 2007, Optics letters.

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

[14]  Ofer Levi,et al.  Optical compressive change and motion detection. , 2012, Applied optics.

[15]  Wai Lam Chan,et al.  A single-pixel terahertz imaging system based on compressed sensing , 2008 .

[16]  Yonina C. Eldar,et al.  Super-resolution and reconstruction of sparse images carried by incoherent light. , 2010, Optics letters.

[17]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[18]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[19]  R. M. Willett,et al.  Compressed sensing for practical optical imaging systems: A tutorial , 2011, IEEE Photonics Conference 2012.

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