Evaggelia Tsiligianni

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Compressed sensing (CS) theory relies on sparse representations in order to recover signals from an undersampled set of measurements. The sensing mechanism is described by the projection matrix, which should possess certain properties to guarantee high quality signal recovery, using efficient algorithms. Although the major breakthrough in compressed sensing(More)
— Despite the important properties of unit norm tight frames (UNTFs) and equiangular tight frames (ETFs), their construction has been proven extremely difficult. The few known techniques produce only a small number of such frames while imposing certain restrictions on frame dimensions. Motivated by the application of incoherent tight frames in compressed(More)
In objectbased video representation, video scenes are composed of several arbitrarily shaped video objects (VOs), defined by their texture, shape and motion. In errorprone communications, packet loss results in missing information at the decoder. The impact of transmission errors is minimised through error concealment. In this paper, we propose a spatial(More)
—Performance guarantees for the algorithms deployed to solve underdetermined linear systems with sparse solutions are based on the assumption that the involved system matrix has the form of an incoherent unit norm tight frame. Learned dictionaries, which are popular in sparse representations, often do not meet the necessary conditions for signal recovery.(More)
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