Results 41 to 50 of about 10,393,467 (307)

A weighted denoising method based on Bregman iterative regularization and gradient projection algorithms

Journal of Inequalities and Applications, 2017
A weighted Bregman-Gradient Projection denoising method, based on the Bregman iterative regularization (BIR) method and Chambolle’s Gradient Projection method (or dual denoising method) is established.
Beilei Tong
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Total-Variation Mode Decomposition [PDF]

, 2021
In this work we analyze the Total Variation (TV) flow applied to one dimensional signals. We formulate a relation between Dynamic Mode Decomposition (DMD), a dimensionality reduction method based on the Koopman operator, and the spectral TV decomposition. DMD is adapted by time rescaling to fit linearly decaying processes, such as the TV flow.
Ido Cohen 0001, Tom Berkov, Guy Gilboa
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Design of Compressed Sensing Algorithm for Coal Mine IoT Moving Measurement Data Based on a Multi-Hop Network and Total Variation

Sensors, 2018
As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased.
Gang Wang, Zhikai Zhao, Yongjie Ning
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A Directional Total Variation

, 2012
Publication in the conference proceedings of EUSIPCO, Bucharest, Romania ...
Ilker Bayram, Mustafa E. Kamasak
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An MBO method for modularity optimisation based on total variation and signless total variation

European Journal of Applied Mathematics
In network science, one of the significant and challenging subjects is the detection of communities. Modularity [1] is a measure of community structure that compares connectivity in the network with the expected connectivity in a graph sampled from a ...
Zijun Li, Yves van Gennip, Volker John
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Low Dimensional Manifold Regularization Based Blind Image Inpainting and Non-Uniform Impulse Noise Recovery

IEEE Access, 2020
Blind image inpainting is a challenging task in image processing. Motivated by the excellent performance of low dimensional manifold model (LDMM) in image inpainting for large-scale pixels missing, we introduce a novel blind inpainting model to repair ...
Mei Gao, Baosheng Kang, Xiangchu Feng, Lixia Cao, Wenjuan Zhang   +4 more
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Strongly Convex Divergences

Entropy, 2020
We consider a sub-class of the f-divergences satisfying a stronger convexity property, which we refer to as strongly convex, or κ-convex divergences. We derive new and old relationships, based on convexity arguments, between popular f-divergences.
James Melbourne
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Fast Total Variation Method Based on Iterative Reweighted Norm for Airborne Scanning Radar Super-Resolution Imaging

Remote Sensing, 2020
The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target.
Xingyu Tuo, Yin Zhang, Yulin Huang, Jianyu Yang   +3 more
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A Two-Staged Feature Extraction Method Based on Total Variation for Hyperspectral Images

Remote Sensing, 2022
Effective feature extraction (FE) has always been the focus of hyperspectral images (HSIs). For aerial remote-sensing HSIs processing and its land cover classification, in this article, an efficient two-staged hyperspectral FE method based on total ...
Chunchao Li, Xuebin Tang, Lulu Shi, Yuanxi Peng, Yuhua Tang   +4 more
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Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis

Remote Sensing, 2019
In this paper, we develop a hyperspectral feature extraction method called sparse and smooth low-rank analysis (SSLRA). First, we propose a new low-rank model for hyperspectral images (HSIs) where we decompose the HSI into smooth and sparse components ...
Behnood Rasti, Pedram Ghamisi, Magnus O. Ulfarsson   +2 more
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