By Michael Elad
The box of sparse and redundant illustration modeling has passed through a tremendous revolution long ago 20 years. This began with a sequence of algorithms for approximating the sparsest ideas of linear structures of equations, later to be via unbelievable theoretical effects that warrantly those algorithms’ functionality. With those contributions in position, significant boundaries in making this version sensible and acceptable have been got rid of, and sparsity and redundancy turned vital, resulting in state of the art leads to quite a few disciplines. one of many major beneficiaries of this development is the sector of photograph processing, the place this version has been proven to guide to remarkable functionality in a variety of purposes.
This e-book presents a complete view of the subject of sparse and redundant illustration modeling, and its use in sign and picture processing. It bargains a scientific and ordered publicity to the theoretical foundations of this knowledge version, the numerical features of the concerned algorithms, and the sign and picture processing purposes that take advantage of those developments. The ebook is well-written, offering in actual fact the move of the information that introduced this box of study to its present achievements. It avoids a succession of theorems and proofs via supplying a casual description of the research pursuits and construction this manner the trail to the proofs. The purposes defined support the reader to raised comprehend complex and up to date options in sign and photo processing.
Written as a text-book for a graduate direction for engineering scholars, this e-book is additionally used as a simple access aspect for readers attracted to entering into this box, and for others already energetic during this zone which are drawn to increasing their figuring out and information.
The publication is followed by way of a Matlab software program package deal that reproduces many of the effects tested within the publication. A hyperlink to the unfastened software program is out there on springer.com.
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Additional info for Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Higher) lower bound. 18) uses the special structure A = [Ψ, Φ]. 1 we considered minors of size p × p extracted from the ˜ The positive-definiteness of all such miGram matrix of the normalized matrix A. nors imply that every p columns are linearly-independent. However, for simplification of the analysis we bounded all the off-diagonal entries of G by a single value µ(A), and thus, lost robustness to possibly few extreme entries in this matrix. 4. For a given matrix A ˜ Λ of p columns from A, and compute the sum of the absolute values of their inner product with a column outside this set.
More specifically, if we consider the 0 as a continuation of the p -norm for p → 0, then for checking its behavior we need to take its 0th root,7 and this is impossible. Alternatively, we can simply refer to the function x 0 as a candidate function for a norm. While this function satisfies the triangle inequality, u + v 0 ≤ u 0 + v 0 , the homogeneity property is not met: for t 0, tu 0 = u 0 t u 0 . This loss of sensitivity to scale will return to haunt us from time to time. 6 A more accurate notation would be x 00 , but we shall stick to the one described above, so as to be consistent with the literature.
7. S. Gurevich, R. Hadani, and N. Sochen, The finite harmonic oscillator and its associated sequences Proc. Natl. Acad. Sci. USA, 105(29):9869–9873, July, 2008. Further Reading 33 8. S. Gurevich, R. Hadani, and N. Sochen, On some deterministic dictionaries supporting sparsity, Journal of Fourier Analysis and Applications, 14(5-6):859– 876, December, 2008. 9. R. Gribonval and M. Nielsen, Sparse decompositions in unions of bases, IEEE Trans. on Information Theory, 49(12):3320–3325, 2003. 10. W. Heisenberg, The physical principles of the quantum theory, (C.