Results 11 to 20 of about 528,580 (282)
Sparse linear representation [PDF]
2009 IEEE International Symposium on Information Theory, 2009 5 pages, to appear in proc.
Halyun Jeong, Young-Han Kim 0001
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Quantization of Sparse Representations [PDF]
2007 Data Compression Conference (DCC'07), 2007 Compressive sensing (CS) is a new signal acquisition technique for sparse and compressible signals. Rather than uniformly sampling the signal, CS computes inner products with randomized basis functions; the signal is then recovered by a convex optimization. Random CS measurements are universal in the sense that the same acquisition system is sufficient
Petros Boufounos, Richard G. Baraniuk
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Generating Images with Sparse Representations
CoRR, 2021 The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as inputs for likelihood-based models.
Charlie Nash +3 more
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Sparse image representation with epitomes [PDF]
CVPR 2011, 2011 Computer Vision and Pattern Recognition, Colorado Springs : United States (2011)
Benoît, Louise +3 more
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Spiralet Sparse Representation
CoRR, 2014 10 pages, Working Paper Number: WP-RFM-14 ...
Reza Farrahi Moghaddam, Mohamed Cheriet
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Joint Sparse Representation-based Single Image Super-Resolution for Remote Sensing Applications
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Sparse representation-based single image super-resolution (SISR) methods use a coupled overcomplete dictionary trained from high-resolution images/image patches.
Bhabesh Deka +3 more
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Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling [PDF]
, 2014 We frame the task of predicting a semantic labeling as a sparse reconstruction procedure that applies a target-specific learned transfer function to a generic deep sparse code representation of an image.
Maire, Michael +2 more
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Local Connectivity Enhanced Sparse Representation
IEEE Access, 2020 During the past two decades, the subspace clustering problem has attracted much attention. Since the data set in real-world problems usually contains a lot of categories, it seems that the large subspace number (LSN) subspace clustering has great ...
Kewei Tang +6 more
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Sparse signal representation for complex-valued imaging [PDF]
, 2009 We propose a sparse signal representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature.
Cetin, Mujdat +3 more
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Learning Sparse Representations of Depth [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2011 This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically obtained by laser range scanners or ...
Ivana Tosic +2 more
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