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An Evaluation and Improved Matching Cost of Stereo Matching Method
2016
International Journal of Image Graphics and Signal Processing
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In this paper, we propose a novel technique to create such images at ground-based data processing system. ...
Objective of the paper is to create a synthetic LISS III image at 23.5 m spatial and 5-day temporal resolutions. ...
We propose a novel approach to minimize these spurious spatial discontinuities in the spatiotemporal data fusion. ...
doi:10.5815/ijigsp.2015.10.06
fatcat:zah2kjwcjrgurffeo7ypv6pxea
Sparse modeling of neural network posterior probabilities for exemplar-based speech recognition
2016
Speech Communication
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This formulation leads to a posterior-based sparse modeling approach to speech recognition. ...
Dictionary learning has been found to be a principled way to alleviate the need of having huge collection of exemplars as required in conventional exemplar-based approaches, while still improving the performance ...
We also would like to acknowledge the anonymous reviewers for the insightful comments and remarks to improve the quality and clarity of the manuscript. ...
doi:10.1016/j.specom.2015.06.002
fatcat:om3t6ijz6vcojbhkinmrekrgw4
Signal Processing on Static and Dynamic 3D Meshes: Sparse Representations and Applications
2019
IEEE Access
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Finally, the identified limitations together with the potential open research directions are also presented for future research efforts toward modeling and optimization for static and dynamic 3D models ...
Moreover, the impact of sparse modeling and optimization tools to several 3D mesh processing tasks, such as completion of missing data, feature preserving noise removal, and rejection of outliers, is illustrated ...
Since, features are indeed sparse in a model, l 0 -norm minimization can be used for identifying them [46] . ...
doi:10.1109/access.2019.2894533
fatcat:pistdxwe3zenrievtcktrco33i
Sparse and redundant signal representations for x-ray computed tomography
[article]
2019
arXiv
pre-print
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Image models are central to all image processing tasks. ...
We review the central concepts in patch-based image processing and explain some of the state-of-the-art algorithms, with a focus on aspects that are more relevant to CT. ...
Obviously, the basic 0 and 1 norms are not capable of modelling these interactions between dictionary atoms. ...
arXiv:1912.03379v1
fatcat:44xkielnmjbmnkf73dntlvtyge
2020 Index IEEE Signal Processing Letters Vol. 27
2020
IEEE Signal Processing Letters
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., +, LSP 2020 610-614 Convex programming Convexifying Sparse Interpolation With Infinitely Wide Neural Networks: An Atomic Norm Approach. ...
Xu, S., +, LSP 2020 216-220
Range-Angle Decoupling and Estimation for FDA-MIMO Radar via Atomic
Norm Minimization and Accelerated Proximal Gradient. ...
doi:10.1109/lsp.2021.3055468
fatcat:wfdtkv6fmngihjdqultujzv4by
Image Segmentation Using Subspace Representation and Sparse Decomposition
[article]
2018
arXiv
pre-print
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We first propose a sparse decomposition framework, which models the background by a subspace containing smooth basis vectors, and foreground as a sparse and connected component. ...
In this dissertation, we focus on the extraction of text and graphics in mixed-content images, and design novel approaches for various aspects of this problem. ...
" background, while minimizing coefficient 0 norm to avoid overfitting of the smooth model on the foreground pixels. ...
arXiv:1804.02419v1
fatcat:q5k3k777krfjfcmnwytdutdalm
Object Tracking in Occlusion and Contrast Conditions using Patch-wise Sparse Method
2020
International Journal of Intelligent Engineering and Systems
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A sparse dictionary using the patches from the previous frames is used to track object in the current frame under such a clutter environment. ...
Contrast modeling, patch resizing and likelihood measurement in the proposed sparse framework allows the selection of key patches to tackle long-term occlusion and extreme illumination variation. ...
[19] used the detection confidence to form the templates set and identify the targets. It is mainly utilized for sparse representation of the appearance model. Babenko et al. ...
doi:10.22266/ijies2020.1031.27
fatcat:3mb6iumxe5dndh25xuyxc2dt6i
Compressed Sensing for Wireless Communications : Useful Tips and Tricks
[article]
2016
arXiv
pre-print
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As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. ...
In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. ...
As an approach to overcome the computational bottleneck of ℓ 0 -norm minimization, ℓ 1 -norm minimization has been used. ...
arXiv:1511.08746v3
fatcat:zvp56kulezdxngaf433w6royiu
2020 Index IEEE Transactions on Signal Processing Vol. 68
2020
IEEE Transactions on Signal Processing
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A Abdelaziz, M., see Brihuega, A., TSP 2020 3603-3618 Abdolee, R., see Ahmadi, M.J., TSP 2020 3808-3823 Abolhasani, M., and Rahmani, M., One-Step Prediction for Discrete Time-Varying Nonlinear Systems ...
Antman, A., +,
TSP 2020 5648-5663
Robust Adaptive Beamforming Based on Linearly Modified Atomic-Norm
Minimization With Target Contaminated Data. ...
., +, TSP 2020 3033-3048 A New Atomic Norm for DOA Estimation With Gain-Phase Errors. ...
doi:10.1109/tsp.2021.3055469
fatcat:6uswtuxm5ba6zahdwh5atxhcsy
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1808.05403v2 fatcat:wxjzwd67cvc2jig5feczrlmuqaOther Versions
To achieve sparsity and/or low-rankness inducing, the ℓ_1 norm and nuclear norm are of the most popular regularization penalties due to their convexity. ...
While the ℓ_1 and nuclear norm are convenient as the related convex optimization problems are usually tractable, it has been shown in many applications that a nonconvex penalty can yield significantly ...
only a few atoms of the learned dictionary. ...
arXiv:1808.05403v3
fatcat:lfq3t5gvgngmllu27ml7xnehtm
Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16)
[article]
2016
arXiv
pre-print
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(Discrete-valued signals; Union of low-dimensional spaces, Cosparsity, mixed/group norm, model-based, low-complexity models, ...); Matrix/manifold sensing/processing (graph, low-rank approximation, ... ...
models in non-convex/non-linear inverse problems (e.g., phase retrieval, blind deconvolution, self calibration); Approximate probabilistic inference for sparse problems; Sparse machine learning and inference ...
Acknowledgements We thank A. Steffens and C. Riofrío for helpful discussions. ...
arXiv:1609.04167v1
fatcat:cral5owpqremninl43bksxvenu
Image De-noising with Machine Learning: A Review
2021
IEEE Access
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This paper explores the numerous state-of-the-art machine-learning-based image de-noisers like dictionary learning models, convolutional neural networks and generative adversarial networks for a range ...
It is used to attenuate the noises and accentuate the specific image information stored within. ...
The data-fidelity term uses -norm fidelity to fit image patches and -norm regularizar for the sparse coding. ...
doi:10.1109/access.2021.3092425
fatcat:xirq6soukzchvaeiugcpgxnlqi
Table of Contents
2021
IEEE Transactions on Signal Processing
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Shim Mathematical Theory of Atomic Norm Denoising in Blind Two-Dimensional Super-Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Tekin Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tsp.2021.3136800
fatcat:zhf46mb3rbdlnnh3u2xizgxof4
PSSNet—An Accurate Super-Secondary Structure for Protein Segmentation
2022
International Journal of Molecular Sciences
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A super-secondary structure (SSS) is a spatially unique ensemble of secondary structural elements that determine the three-dimensional shape of a protein and its function, rendering SSSs attractive as ...
SSS segmentation, this method uses key characteristics of SSS geometry, including the lengths of secondary structural elements and the distances between them, torsion angles, spatial positions of Cα atoms ...
Acknowledgments: The authors are grateful to A.V. Efimov for the helpful discussions. ...
doi:10.3390/ijms232314813
pmid:36499138
pmcid:PMC9740782
fatcat:hwehk42u65fhfadmjvknhjvx4e
A Survey of Signal Processing Problems and Tools in Holographic Three-Dimensional Television
2007
IEEE transactions on circuits and systems for video technology (Print)
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Atomic decompositions, multiresolution techniques, Gabor functions, and Wigner distributions are among the signal processing techniques which have or may be applied to problems in optics. ...
The literature on computer-generated holography provides a good resource for holographic 3DTV related issues. ...
In general, atoms are organized in overcomplete dictionaries and the task is to obtain a sparse or super-resolving representation with preferably or number of computations [260] . ...
doi:10.1109/tcsvt.2007.909973
fatcat:lzfeaoennjatpk75cz6ad5kkt4
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