Compressed Sensing : Theory and Applications by Yonina C. Eldar, Gitta Kutyniok

By Yonina C. Eldar, Gitta Kutyniok

Compressed sensing is an exhilarating, quickly becoming box, attracting massive awareness in electric engineering, utilized arithmetic, information and computing device technology. This publication offers the 1st unique creation to the topic, highlighting fresh theoretical advances and a number of functions, in addition to outlining quite a few last examine demanding situations. After an intensive evaluation of the elemental conception, many state-of-the-art concepts are provided, together with complex sign modeling, sub-Nyquist sampling of analog signs, non-asymptotic research of random matrices, adaptive sensing, grasping algorithms and use of graphical types. All chapters are written by way of top researchers within the box, and constant variety and notation are applied all through. Key history details and transparent definitions make this a terrific source for researchers, graduate scholars and practitioners desirous to sign up for this fascinating learn region. it may possibly additionally function a supplementary textbook for classes on computing device imaginative and prescient, coding thought, sign processing, photograph processing and algorithms for effective information processing.

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But this contradicts our assumption that there exists at most one signal x ∈ Σk such that y = Ax. Therefore, we must have that spark(A) > 2k. Now suppose that spark(A) > 2k. Assume that for some y there exist x, x ∈ Σk such that y = Ax = Ax . We therefore have that A(x − x ) = 0. Letting h = x − x , we can write this as Ah = 0. Since spark(A) > 2k, all sets of up to 2k columns of A are linearly independent, and therefore h = 0. This in turn implies x = x , proving the theorem. It is easy to see that spark(A) ∈ [2, m + 1].

Perhaps somewhat surprisingly, while it is typically not quite as easy as in the fully random case, one can prove that many of these constructions also satisfy the RIP and/or have low coherence. Furthermore, one can analyze the effect of inaccuracies in the matrix A implemented by the system [54, 149]; in the simplest cases, such sensing matrix errors can be addressed through system calibration. 6, we begin by considering a natural first approach to the problem of sparse recovery. 10) where B(y) ensures that x is consistent with the measurements y.

Duarte, Y. C. Eldar, and G. , for very large problem sizes there are sharp thresholds dictating that the fraction of k-faces preserved will tend to either one or zero with very high probability, depending on ρ and γ [95]. For the precise values of ρ and γ which will enable successful recovery and for further discussion of similar results, see Chapters 7 and 9. These results provide sharp bounds on the minimum number of measurements required in the noiseless case. In general, these bounds are significantly stronger than the corresponding measurement bounds obtained within the RIP-based framework, which tend to be extremely loose in terms of the constants involved.

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