Results 41 to 50 of about 2,932,031 (355)
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based
Jian Zhang, Bernard Ghanem
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Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis [PDF]
IEEE Transactions on Wireless Communications, 2019 Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS).
Jiajia Guo +3 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). The question addressed in this paper is whether an even smaller number of samples is sufficient when there exists prior knowledge about the distribution of the ...
Reeves, Galen, Gastpar, Michael
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Optimization-Inspired Compact Deep Compressive Sensing
IEEE Journal on Selected Topics in Signal Processing, 2020 In order to improve CS performance of natural images, in this paper, we propose a novel framework to design an OPtimization-INspired Explicable deep Network, dubbed OPINE-Net, for adaptive sampling and recovery.
Jian Zhang, Chen Zhao, Wen Gao
semanticscholar +1 more source
Compressed wavefront sensing [PDF]
Optics Letters, 2014 We report on an algorithm for fast wavefront sensing that incorporates sparse representation for the first time in practice. The partial derivatives of optical wavefronts were sampled sparsely with a Shack-Hartman wavefront sensor (SHWFS) by randomly subsampling the original SHWFS data to as little as 5%.
Ryan P. McNabb +3 more
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Jurnal Elkomika, 2020 ABSTRAK Watermarking pada citra medis dilakukan untuk melindungi hak kepemilikan dan keaslian sebuah citra medis. Proses embedding dan extraction dirancang menggunakan metode Stationary Wavelet Transform (SWT) dan Statistical Mean Manipulation (SMM ...
YASQI HAFIZHANA +3 more
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International Journal of Engineering Technologies and Management Research, 2020 Compressive sensing is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples. It works on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality.
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SPECTRUM SENSING OF WIDE BAND SIGNALS BASED ON ENERGY DETECTION WITH COMPRESSIVE SENSING
Journal of Engineering and Sustainable Development, 2020 Compressive sensing (CS) technique is used to solve the problem of high sampling rate with wide band signal spectrum sensing where high speed analogue to digital converter is needed to do that. This leads to difficult hardware implementation, large time
Ali Mohammad A. AL-Hussain +1 more
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ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2022 Compressive sensing of 2D signals involves three fundamental steps: sparse representation, linear measurement matrix, and recovery of the signal. This paper focuses on analyzing the efficiency of various measurement matrices for compressive sensing of ...
Hepzibah Christinal A +4 more
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Sequential Compressed Sensing [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2010 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 by bounding the number of samples required for a given level of signal sparsity. However, using these
Malioutov, Dmitry M. +2 more
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