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Image compression-encryption method based on two-dimensional sparse recovery and chaotic system [PDF]
Scientific Reports, 2021 In this paper, we propose an image compression-encryption method based on two-dimensional (2D) sparse representation and chaotic system. In the first step of this method, the input image is extended in a transform domain to obtain a sparse representation.
Aboozar Ghaffari
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Lower Bounds for Sparse Recovery [PDF]
Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, 2010 We consider the following k-sparse recovery problem: design an m x n matrix A, such that for any signal x, given Ax we can efficiently recover x' satisfying ||x-x'||_1
Ba, Khanh Do +3 more
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Sparse signal recovery from modulo observations [PDF]
EURASIP Journal on Advances in Signal Processing, 2021 We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a relatively new imaging mechanism called modulo imaging, which can be used to extend the dynamic range ...
Viraj Shah, Chinmay Hegde
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Sparse Recovery with Very Sparse Compressed Counting [PDF]
, 2013 Compressed sensing (sparse signal recovery) often encounters nonnegative data (e.g., images). Recently we developed the methodology of using (dense) Compressed Counting for recovering nonnegative K-sparse signals.
Li, Ping, Zhang, Cun-Hui, Zhang, Tong
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Identification-While-Scanning of a Multi-Aircraft Formation Based on Sparse Recovery for Narrowband Radar [PDF]
Sensors, 2016 It is known that the identification performance of a multi-aircraft formation (MAF) of narrowband radar mainly depends on the time on target (TOT).
Yuan Jiang +5 more
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Sparse Signal Recovery under Poisson Statistics [PDF]
2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2013 We are motivated by problems that arise in a number of applications such as Online Marketing and explosives detection, where the observations are usually modeled using Poisson statistics.
Motamedvaziri, D. +2 more
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Nonuniform Sparse Recovery with Subgaussian Matrices [PDF]
Electronic Transactions on Numerical Analysis, 2011 Compressive sensing predicts that sufficiently sparse vectors can be recovered from highly incomplete information. Efficient recovery methods such as $\ell_1$-minimization find the sparsest solution to certain systems of equations.
Ayaz, Ulaş, Rauhut, Holger
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Group-sparse matrix recovery [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 Comment: ICASSP ...
Zeng, Xiangrong +1 more
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Sparse recovery with integrality constraints [PDF]
Discrete Applied Mathematics, 2020 We investigate conditions for the unique recoverability of sparse integer-valued signals from a small number of linear measurements. Both the objective of minimizing the number of nonzero components, the so-called $\ell_0$-norm, as well as its popular substitute, the $\ell_1$-norm, are covered.
Lange, J. +3 more
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Learning-based accelerated sparse signal recovery algorithms
ICT Express, 2021 In this paper, we propose an accelerated sparse recovery algorithm based on inexact alternating direction of multipliers. We formulate a sparse recovery problem with a concave regularizer and solve it with the relaxed and accelerated alternating method ...
Dohyun Kim, Daeyoung Park
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