Results 11 to 20 of about 1,448,495 (301)
CS-MUVI: Video compressive sensing for spatial-multiplexing cameras [PDF]
2012 IEEE International Conference on Computational Photography (ICCP), 2012 Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. SMC architectures are particularly useful when imaging at wavelengths for which full-frame sensors are too cumbersome or expensive.
Sankaranarayanan, Aswin C. +2 more
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Compressive Sensing for High-Resolution Direction-of-Arrival Estimation via Iterative Optimization on Sensing Matrix [PDF]
International Journal of Antennas and Propagation, 2015 A novel compressive sensing- (CS-) based direction-of-arrival (DOA) estimation algorithm is proposed to solve the performance degradation of the CS-based DOA estimation in the presence of sensing matrix mismatching. Firstly, a DOA sparse sensing model is
Hongtao Li, Chaoyu Wang, Xiaohua Zhu
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Iterative detection for compressive sensing: Turbo CS [PDF]
2014 IEEE International Conference on Communications (ICC), 2014 We consider compressive sensing as a source coding method for signal transmission. We concatenate a convolutional coding system with 1-bit compressive sensing to obtain a serial concatenated system model for sparse signal transmission over an AWGN channel.
Amin Movahed, Mark C. Reed
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Modified-CS: Modifying Compressive Sensing for Problems With Partially Known Support [PDF]
IEEE Transactions on Signal Processing, 2009 We study the problem of reconstructing a sparse signal from a limited number of its linear projections when a part of its support is known, although the known part may contain some errors. The ``known" part of the support, denoted T, may be available from prior knowledge.
Namrata Vaswani, Wei Lu 0024
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CS-ROMER: a novel compressed sensing framework for Faraday depth reconstruction
Monthly Notices of the Royal Astronomical Society, 2022 ABSTRACT The reconstruction of Faraday depth structure from incomplete spectral polarization radio measurements using the RM synthesis technique is an underconstrained problem requiring additional regularization. In this paper, we present cs-romer: a novel object-oriented compressed sensing framework to reconstruct Faraday depth signals ...
Miguel Cárcamo +3 more
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LS-CS-Residual (LS-CS): Compressive Sensing on Least Squares Residual [PDF]
IEEE Transactions on Signal Processing, 2010 Accepted (with mandatory minor revisions) to IEEE Trans. Signal Processing.
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CS-VQA: Visual Question Answering with Compressively Sensed Images [PDF]
2018 25th IEEE International Conference on Image Processing (ICIP), 2018 Visual Question Answering (VQA) is a complex semantic task requiring both natural language processing and visual recognition. In this paper, we explore whether VQA is solvable when images are captured in a sub-Nyquist compressive paradigm. We develop a series of deep-network architectures that exploit available compressive data to increasing degrees of
Li-Chi Huang +5 more
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CS-MCNet: A Video Compressive Sensing Reconstruction Network with Interpretable Motion Compensation [PDF]
, 2021 In this paper, a deep neural network with interpretable motion compensation called CS-MCNet is proposed to realize high-quality and real-time decoding of video compressive sensing. Firstly, explicit multi-hypothesis motion compensation is applied in our network to extract correlation information of adjacent frames(as shown in Fig.
Bowen Huang +5 more
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A Novel Strategy for Radar Imaging Based on Compressive Sensing [PDF]
, 2010 Radar data have already proven to be compressible with no significant losses for most of the applications in which it is used. In the framework of information theory, the compressibility of a signal implies that it can be decomposed onto a reduced set of
López-Dekker, Paco +8 more
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A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications
IEEE Access, 2018 Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain.
Meenu Rani, S. B. Dhok, R. B. Deshmukh
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