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Bowley, James; Rebollo - Neira, Laura (2011)
Languages: English
Types: Article
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Theory
A property of sparse representations in relation to their capacity for information storage is discussed. It is shown that this feature can be used for an application that we term Encrypted Image Folding. The proposed procedure is realizable through any suitable transformation. In particular, in this paper we illustrate the approach by recourse to the Discrete Cosine Transform and a combination of redundant Cosine and Dirac dictionaries. The main advantage of the proposed technique is that both storage and encryption can be achieved simultaneously using simple processing steps.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] S. Mallat. A wavelet tour of signal processing Academic Press, London, 1998.
    • [2] S.S. Chen, D.L. Donoho, and M.A. Saunders. Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20:33-61, 1998.
    • [3] S. Mallat and Z. Zhang. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41:3397-3415, 1993.
    • [4] Y.C. Pati, R. Rezaiifar, and P.S. Krishnaprasad. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In Proceedings of the 27th Annual Asilomar Conference in Signals, System and Computers, volume 1, pages 40-44, 1993.
    • [5] L. Rebollo-Neira and D. Lowe. Optimized orthogonal matching pursuit approach. IEEE Signal Processing Letters, 9:137-140, 2002.
    • [6] M. Andrle and L. Rebollo-Neira. A swapping-based refinement of orthogonal matching pursuit strategies. Signal Processing, 86:480-495, 2006.
    • [7] S. Fischer, G. Cristobal, R. Redondo, R. Sparse overcomplete Gabor wavelet representation based on local competitions. IEEE Transactions on Image Processing 15: 265-272, 2006.
    • [8] R. Figueras i Ventura, P. Vandergheynst, P. Frossard Low-rate and flexible image coding with redundant representations IEEE Transactions on Image Processing, 15: 726-739, 2006.
    • [9] D. Donoho and X. Huo. Uncertainty principles and ideal atomic decomposition. IEEE Transactions on Information Theory, 47:2845-2862, 2001.
    • [10] M. Eldar and A.M. Bruckstein, A generalized uncertainty principle and sparse representations of pairs of bases. EEE Transactions on Information Theory, 48: 2558-2567, 2002.
    • [11] A. Feurer and A. Nemirosky, On sparse represeation in pairs of basis. EEE Transactions on Information Theory, 49: 1579-1581, 2003.
    • [12] R. Gribonval and M. Nielsen. Sparse representations in unions of bases. IEEE Transactions on Information Theory, 49:3320-3325, 2003.
    • [13] Greed is good: algorithmic results for sparse approximation IEEE Transactions on Information Theory, 50: 2231-2242, 2004.
    • [14] B.A. Olshausen and B.J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381:607-609, 1997.
    • [15] M. Elad M. Aharon and A.M. Bruckstein. K-svd: An algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans on Signal Processing, 54:4311- 4322, 2006.
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