Results 11 to 20 of about 2,877 (265)
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.
Vaswani, Namrata, Lu, Wei
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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
Huang, Li-Chi +5 more
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Data Collection Method in Clustering Sensing Network Based on Compressive Sensing [PDF]
Jisuanji gongcheng, 2018 In order to reduce the cluster sensing network transmissions and prolong network lifetime,this paper proposes a data collection method based on the hybrid Compressive Sensing(CS) technology for clustering Wireless Sensor Network(WSN).It divides the ...
LI Yulong,LIU Renren,ZHAO Jinfeng,ZANG Lang,CAO Bin
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Magnetic Resonance Materials in Physics, Biology and Medicine, 2023 Abstract Object Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements.
Siddharth S. Iyer +9 more
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A Two-Branch Convolution Residual Network for Image Compressive Sensing
IEEE Access, 2020 Deep learning has made great progress in image compressive sensing (CS) tasks recently, and several CS models based on it have achieved superior performance. In practice, sensing the entire image requires huge memory and computational effort.
Chenquan Gan +3 more
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Sensors, 2014 Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS) acquisition are very different from traditional image
Xiangwei Li +4 more
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Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal
Journal of Imaging, 2018 Hyperspectral (HS) imaging involves the sensing of a scene’s spectral properties, which are often redundant in nature. The redundancy of the information motivates our quest to implement Compressive Sensing (CS) theory for HS imaging.
Yaniv Oiknine +4 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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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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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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