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coherence tomography. Ophthalmology 121, 162–172 (2014)
53. C. Guillemot, F. Pereira, L. Torres, T. Ebrahimi, R. Leonardi, J. Ostermann, Distributed
monoview and multiview video coding. IEEE Signal Process. Mag. 24, 67–76 (2007)
54. J.Y. Lee, S.J. Chiu, P. Srinivasan, J.A. Izatt, C.A. Toth, S. Farsiu et al., Fully automatic software for quantification of retinal thickness and volume in eyes with diabetic macular edema
from images acquired by cirrus and spectralis spectral domain optical coherence tomography
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55. S. Jiao, R. Knighton, X. Huang, G. Gregori, C. Puliafito, Simultaneous acquisition of sectional and fundus ophthalmic images with spectral-domain optical coherence tomography.
Opt. Express 13, 444–452 (2005)
56. A. Cameron, D. Lui, A. Boroomand, J. Glaister, A. Wong, K. Bizheva, Stochastic speckle noise
compensation in optical coherence tomography using non-stationary spline-based speckle noise
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57. Y. Chen, N.M. Nasrabadi, T.D. Tran, Hyperspectral image classification using dictionary-based
sparse representation. IEEE Trans. Geosci. Remote Sens. 49, 3973–3985 (2011)
58. L. Fang, S. Li, X. Kang, J.A. Benediktsson, Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation. IEEE Trans. Geosci. Remote Sens. 52,
7738–7749 (2014)
59. K. Skretting, J.H. Husøy, S.O. Aase, Improved Huffman coding using recursive splitting. in
Proceedings of Norwegian Signal Processing, NORSIG, pp. 92–95 (1999)
60. A. Said, W.A. Pearlman, A new fast and efficient image codec based on set partitioning in
hierarchical trees. IEEE Trans. Circuits Syst. Video Technol. 6, 243–250 (1996)
61. Software was downloaded at: http://www.apple.com/quicktime/extending/
62. L. Zhang, L. Zhang, X. Mou, D. Zhang, FSIM: a feature similarity index for image quality
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103
48. J. Yang, Z. Wang, Z. Lin, S. Cohen, T. Huang, Coupled dictionary training for image superresolution. IEEE Trans. Image Process. 21, 3467–3478 (2012)
49. S. Wang, L. Zhang, Y. Liang, Q. Pan, Semi-coupled dictionary learning with applications
to image super-resolution and photo-sketch synthesis. in Proceedings of IEEE International
Conference on Computer Vision Pattern Recognition, pp. 2216–2223 (2012)
50. J.A. Tropp, A.C. Gilbert, M.J. Strauss, Algorithms for simultaneous sparse approximation. Part
I: greedy pursuit. Signal Process. 86, 572–588 (2006)
51. E. Candes, J. Romberg, Sparsity and incoherence in compressive sampling. Inverse Prob. 23,
969–985 (2007)
52. S. Farsiu, S.J. Chiu, R.V. O’Connell, F.A. Folgar, E. Yuan, J.A. Izatt et al., Quantitative classification of eyes with and without intermediate age-related aacular degeneration using optical
coherence tomography. Ophthalmology 121, 162–172 (2014)
53. C. Guillemot, F. Pereira, L. Torres, T. Ebrahimi, R. Leonardi, J. Ostermann, Distributed
monoview and multiview video coding. IEEE Signal Process. Mag. 24, 67–76 (2007)
54. J.Y. Lee, S.J. Chiu, P. Srinivasan, J.A. Izatt, C.A. Toth, S. Farsiu et al., Fully automatic software for quantification of retinal thickness and volume in eyes with diabetic macular edema
from images acquired by cirrus and spectralis spectral domain optical coherence tomography
machines. Invest. Ophthalmol. Vis. Sci. 54, 7595–7602 (2013)
55. S. Jiao, R. Knighton, X. Huang, G. Gregori, C. Puliafito, Simultaneous acquisition of sectional and fundus ophthalmic images with spectral-domain optical coherence tomography.
Opt. Express 13, 444–452 (2005)
56. A. Cameron, D. Lui, A. Boroomand, J. Glaister, A. Wong, K. Bizheva, Stochastic speckle noise
compensation in optical coherence tomography using non-stationary spline-based speckle noise
modelling. Biomed. Opt. Exp. 4, 1769–1785 (2013)
57. Y. Chen, N.M. Nasrabadi, T.D. Tran, Hyperspectral image classification using dictionary-based
sparse representation. IEEE Trans. Geosci. Remote Sens. 49, 3973–3985 (2011)
58. L. Fang, S. Li, X. Kang, J.A. Benediktsson, Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation. IEEE Trans. Geosci. Remote Sens. 52,
7738–7749 (2014)
59. K. Skretting, J.H. Husøy, S.O. Aase, Improved Huffman coding using recursive splitting. in
Proceedings of Norwegian Signal Processing, NORSIG, pp. 92–95 (1999)
60. A. Said, W.A. Pearlman, A new fast and efficient image codec based on set partitioning in
hierarchical trees. IEEE Trans. Circuits Syst. Video Technol. 6, 243–250 (1996)
61. Software was downloaded at: http://www.apple.com/quicktime/extending/
62. L. Zhang, L. Zhang, X. Mou, D. Zhang, FSIM: a feature similarity index for image quality
assessment. IEEE Trans. Image Process. 20, 2378–2386 (2011)