AExperimental study for coding computational imaging and quality evaluation of reconstructed image

MA Yan-Peng; QI Hong-Xing; Han Gui-Cheng; WANG Yi-Kun; Wang Lei; SHU Rong

doi:10.11972/j.issn.1001-9014.2017.04.019

[1] Brady J D, Aristide D, Fiddy A M, et al. Computational optical sensing and imaging: introduction to the feature issue ［J］. Appl. Opt, 2008, 47(10): COSI1-COSI2.

[2] Takhar D, Laska J N, Wakin M B, et al. A New Compressive Imaging Camera Architecture Using Optical-Domain Compression ［J］. Computational Imaging IV at IS&T/SPIE Electronic Imaging. 2006: 43-52.

[3] Harwit M, Sloane-Neil J A．Hadamard transform optics［M］．New York: Academic Press, 1979 : 1- 104.

[5] Cand`es E, Romberg J, Tao T. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information［J］. IEEE Trans. Inf. Theory. 2006, 52: 489-509.

[6] Donoho D. Compressed sensing ［J］. IEEE Trans. Inf. Theory, 2006, 52:1289-1306.

[7] Tropp J, Gilbert A. Signal recovery from random measurements via orthogonal matching pursuit ［J］. IEEE Transactions on Information Theory, 2007,53(12):4655.

[8] Candès E.Compressive sampling［C］.International Congress of Mathematics, 2006: 1433-1452.

[9] Candès E, Wakin M.An introduction to compressive sampling［J］.IEEE Signal Process Magazine, 2008, 25(2): 21-30.

[10] DMD 0.7XGA 12°DDR DMD discovery TM［R］.America.Texas Instrument corporation.2005.

[13] Wax M, Kailath T. Detection of signals by information theoretic criteria ［J］. IEEE Trans. On ASSP, 1985, 33(2):387-392.