## From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images (2007)

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Citations: | 226 - 31 self |

### BibTeX

@MISC{Bruckstein07fromsparse,

author = {Alfred M. Bruckstein and David L. Donoho and Michael Elad},

title = {From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images },

year = {2007}

}

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### Abstract

A full-rank matrix A ∈ IR n×m with n < m generates an underdetermined system of linear equations Ax = b having infinitely many solutions. Suppose we seek the sparsest solution, i.e., the one with the fewest nonzero entries: can it ever be unique? If so, when? As optimization of sparsity is combinatorial in nature, are there efficient methods for finding the sparsest solution? These questions have been answered positively and constructively in recent years, exposing a wide variety of surprising phenomena; in particular, the existence of easily-verifiable conditions under which optimally-sparse solutions can be found by concrete, effective computational methods. Such theoretical results inspire a bold perspective on some important practical problems in signal and image processing. Several well-known signal and image processing problems can be cast as demanding solutions of undetermined systems of equations. Such problems have previously seemed, to many, intractable. There is considerable evidence that these problems often have sparse solutions. Hence, advances in finding sparse solutions to underdetermined systems energizes research on such signal and image processing problems – to striking effect. In this paper we review the theoretical results on sparse solutions of linear systems, empirical

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Citation Context ... 0% success. As the problem size increases, the transition from typicality of success to typicality of failure becomes increasingly sharp—in the large-n limit, perfectly sharp. A rigorous result from =-=[44, 56, 57]-=- explains the meaning of the curve in panel (a). Theorem 11. Fix a (δ, ρ) pair.At problem size n, set mn = ⌊n/δ⌋ and kn = ⌊nρ⌋.Draw a problem instance y = Ax at random with A an n × mn matrix from the... |

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Citation Context ...t for equivalence for a certain explicit function r [18]. These qualitative results opened the way to asking for the precise quantitative behavior, i.e., for ρW above. • Tropp, Gilbert, and coworkers =-=[158]-=- studied running OMP over the problem suite consisting of the Gaussian matrix ensemble and k-sparse coefficients solution; they showed that the sparsest solution is found with high probability provide... |

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Citation Context ...ld in such cases seek a better prior. Careful empirical modeling of wavelet coefficients of images with edges has shown that, in many cases, the prior model p(y) ∝ exp(−λ‖Ty‖1) can indeed be improved =-=[35, 144, 10]-=-. The Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.SPARSE MODELING OF SIGNALS AND IMAGES 63 general form p(y) ∝ exp(−λ‖Ty‖r r) with 0 <r<1 has been studied, and values... |

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Citation Context ...apes and X has sparse columns. The matrix factorization viewpoint connects this problem with related problems of nonnegative matrix factorization [104, 55] and sparse nonnegative matrix factorization =-=[92, 1]-=-. Clearly, there is no general practical algorithm for solving problem (52) or (53), for the same reasons that there is no general practical algorithm for solving (P0), only more so! However, just as ... |

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Citation Context ...of (P1) and (P0) and in [158] in studying OMP. Other fundamental ideas include Kashin’s results on n-widths of the octahedron [98], Milman’s quotient of a subspace theorem, and Szarek’s volume bounds =-=[132]-=-, all reflecting the miraculous properties of ℓ1 norms when restricted to random subspaces, which lie at the heart of the (P0)–(P1) equivalence. Rudelson and Vershynin [140] have made very effective u... |

318 | Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise
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Citation Context ...esponds to the OMP algorithm as described in Exhibit 1, and the other for BP (i.e., solving (P1) in place of (P0)). 2.3.1. The GA Solves (P0) in Sufficiently Sparse Cases. Theorem 6 (equivalence: OGA =-=[156, 48]-=-). For a system of linear equations Ax = b (A ∈ Rn×m full-rank with n<m), if a solution x exists obeying (9) ‖x‖0 < 1 ( 1+ 2 1 ) , µ(A) an OGA run with threshold parameter ɛ0 =0is guaranteed to find i... |

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Citation Context ... coworkers [148, 147], where the image Barbara is decomposed into piecewise smooth (cartoon) and texture, using MCA as described above. They used a dictionary combining two representations: curvelets =-=[146, 12, 13]-=- for representing the cartoon part, and local overlapped DCT for the texture. The second row in this figure, taken from [68], presents inpainting results, where missing values (the text) are recovered... |

310 |
Image denoising via sparse and redundant representations over learned dictionaries
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Citation Context ...f results from JPEG, JPEG2000, PCA, and sparse coding with K-SVD dictionary training. The values below each result show the PSNR. 6.2.2. Methodology and Algorithms. The denoising methods described in =-=[63, 64]-=- take a different approach: by training a dictionary on the image content directly. One option is to use a standard library of clean images, e.g., the Corel library of 60,000 images, and develop a sta... |

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Citation Context ...nformation theory, constructing error-correcting codes using a collection of orthogonal bases with minimal coherence, obtaining similar bounds on the mutual coherence for amalgams of orthogonal bases =-=[11]-=-. Mutual coherence, relatively easy to compute, allows us to lower bound the spark, which is often hard to compute. Lemma 4 (see [46]). For any matrix A ∈ R n×m , the following relationship holds: (7)... |

274 | New tight frames of curvelets and optimal representations of objects with C2 singularities
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Citation Context ...ery powerful sparsity constraint. The weak ℓp norm is a popular measure of sparsity in the mathematical analysis community; models of cartoon images have sparse representations as measured in weak ℓp =-=[26, 13]-=-. Almost equivalent are the usual ℓ p norms, defined by ( ∑ ‖x‖p = |xi| p i ) 1/p These will seem more familiar objects than the weak ℓp norms, in the range 1 ≤ p ≤∞; however, for measuring sparsity, ... |

272 | Minimax estimation via wavelet shrinkage
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Citation Context ...n is key to widely used techniques of transformbased image compression. Transform sparsity is also a driving factor for other important signal and image processing problems, including image denoising =-=[50, 51, 27, 43, 53, 52, 144, 124, 96]-=- and image deblurring [76, 75, 74, 41]. Repeatedly, it has been shown that a better representation technique—one that leads to more sparsity—can be the basis for a practically better solution to such ... |

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Citation Context ...resent edges than do Fourier and wavelet methods. By shrinkage of transform coefficients followed by reconstruction, some reduction in image noise is observed, while edges are approximately preserved =-=[103, 19, 20, 21, 146, 136, 70, 71, 88, 89]-=-. Copyright © by SIAM. Unauthorized reproduction of this article is prohibited.SPARSE MODELING OF SIGNALS AND IMAGES 73 Original JPEG (26.59dB) JPEG-2000 (27.81dB) PCA (29.27dB) K-SVD (33.26dB) Origi... |

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248 |
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Citation Context ...ures 5 and 6 two worked out large-scale applications. Figure 5 presents compressed sensing of dynamic MRI—real-time acquisition of heart motion—by Michael Lustig and coworkers at the Stanford MRI lab =-=[112, 111]-=-. They obtain a successful reconstruction of moving imagery of the beating heart from raw pseudorandom samples of the k-t space, with a factor of 7 undersampling, i.e., they solve a system of equation... |

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Citation Context ...k, as it calls for a combinatorial search over all possible subsets of columns from A. The importance of this property of matrices for the study of the uniqueness of sparse solutions was unraveled in =-=[84]-=-. Interestingly, this property previously appeared in the literature of psychometrics (termed Kruskal rank), used in the context of studying uniqueness of tensor decomposition [102, 110]. The spark is... |

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207 | Image compression via joint statistical characterization in the wavelet domain
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207 | Noise removal via Bayesian wavelet coring
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203 | A Wavelet Tour of - Mallat - 1998 |

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