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Compression-Based Compressed Sensing [PDF]
IEEE Transactions on Information Theory, 2016 Modern compression algorithms exploit complex structures that are present in signals to describe them very efficiently. On the other hand, the field of compressed sensing is built upon the observation that "structured" signals can be recovered from their
Erkip, Elza +3 more
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Universal Compressed Sensing [PDF]
2016 IEEE International Symposium on Information Theory (ISIT), 2016 In this paper, the problem of developing universal algorithms for compressed sensing of stochastic processes is studied. First, R\'enyi's notion of information dimension (ID) is generalized to analog stationary processes.
Jalali, Shirin, Poor, H. Vincent
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Sequential Compressed Sensing [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2009 Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements. Existing results in compressed sensing literature have focused on characterizing the achievable performance ...
Malioutov, Dmitry +2 more
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"Compressed" Compressed Sensing
2010 IEEE International Symposium on Information Theory, 2010 The field of compressed sensing has shown that a sparse but otherwise arbitrary vector can be recovered exactly from a small number of randomly constructed linear projections (or samples).
Gastpar, Michael, Reeves, Galen
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A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging [PDF]
Scientific Reports, 2017 Single-pixel imaging is an alternate imaging technique particularly well-suited to imaging modalities such as hyper-spectral imaging, depth mapping, 3D profiling.
Ming-Jie Sun +4 more
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Compressed Sensing for Biomedical Photoacoustic Imaging: A Review. [PDF]
Sensors (Basel)Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging.
Wang Y, Chen Y, Zhao Y, Liu S.
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Algebraic compressed sensing [PDF]
Applied and Computational Harmonic Analysis, 2023 We introduce the broad subclass of algebraic compressed sensing problems, where structured signals are modeled either explicitly or implicitly via polynomials. This includes, for instance, low-rank matrix and tensor recovery. We employ powerful techniques from algebraic geometry to study well-posedness of sufficiently general compressed sensing ...
Breiding, Paul +3 more
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Single-Shot Compressed Imaging via Random Phase Modulation
Applied Sciences, 2022 Compressed sensing (CS) provides an innovative framework for signal sampling, which enables accurate recovery of the sparse or compressible signal from a small set of linear measurements far fewer than the Nyquist rate in traditional signal processing ...
Cheng Zhang +5 more
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Distributed Compressed Sensing of Hyperspectral Images According to Spectral Library Matching
IEEE Access, 2021 The ever-increasing resolution puts tremendous pressure to the onboard hyperspectral imaging system. Compressed sensing technology is one of the important ways to solve this problem.
Hua Xiao, Zhongliang Wang, Xueying Cui
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From compression to compressed sensing [PDF]
2013 IEEE International Symposium on Information Theory, 2013 Can compression algorithms be employed for recovering signals from their underdetermined set of linear measurements? Addressing this question is the first step towards applying compression algorithms for compressed sensing (CS). In this paper, we consider a family of compression algorithms $\mathcal{C}_r$, parametrized by rate $r$, for a compact class ...
Arian Maleki, Shirin Jalali
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