Titlebook: Compressed Sensing and its Applications; MATHEON Workshop 201 Holger Boche,Robert Calderbank,Jan Vybíral Book 2015 Springer International P

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Two Algorithms for Compressed Sensing of Sparse Tensors, a sparse signal from relatively few linear measurements via a suitable nonlinear minimization process. Conventional CS theory relies on vectorial data representation, which results in good compression ratios at the expense of increased computational complexity. In applications involving color image

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Structured Sparsity: Discrete and Convex Approaches,learning, and optimization. In fact, most natural data can be . represented, i.e., a small set of coefficients is sufficient to describe the data using an appropriate basis. Sparsity is also used to enhance interpretability in real-life applications, where the relevant information therein typically

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Explicit Matrices with the Restricted Isometry Property: Breaking the Square-Root Bottleneck,ucted using random processes, while explicit constructions are notorious for performing at the “square-root bottleneck,” i.e., they only accept sparsity levels on the order of the square root of the number of measurements. The only known explicit matrix which surpasses this bottleneck was constructe

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