Results 1 to 10 of about 9,171,861 (296)

Low-Light Image Enhancement Based on Constraint Low-Rank Approximation Retinex Model [PDF]

Sensors, 2022
Images captured in a low-light environment are strongly influenced by noise and low contrast, which is detrimental to tasks such as image recognition and object detection. Retinex-based approaches have been continuously explored for low-light enhancement.
Xuesong Li, Jianrun Shang, Wenhao Song, Jinyong Chen, Guisheng Zhang, Jinfeng Pan   +5 more
doaj   +2 more sources

Sparse Ultrasound Imaging via Manifold Low-Rank Approximation and Non-Convex Greedy Pursuit [PDF]

Sensors, 2018
Model-based image reconstruction has improved contrast and spatial resolution in imaging applications such as magnetic resonance imaging and emission computed tomography.
Thiago Alberto Rigo Passarin, Marcelo Victor Wüst Zibetti, Daniel Rodrigues Pipa   +2 more
doaj   +2 more sources

Clustering-based low-rank matrix approximation for multimodal medical image compression [PDF]

BioData Mining
Medical images are inherently high-resolution and contain locally varying structures that are crucial for diagnosis. Efficient compression of such data must therefore preserve diagnostic fidelity while minimizing redundancy. Low-rank matrix approximation
Sisipho Hamlomo, Marcellin Atemkeng
doaj   +2 more sources

Low-light Image Enhancement Model with Low Rank Approximation [PDF]

Jisuanji kexue, 2022
Due to the influence of low lightness,the images acquired at dim or backlight conditions tend to have poor visual quality.Retinex-based low-light enhancement models are effective in improving the scene lightness,but they are often limited in hand-ling ...
WANG Yi-han, HAO Shi-jie, HAN Xu, HONG Ri-chang
doaj   +1 more source

Mixed Alternating Projections with Application to Hankel Low-Rank Approximation

Algorithms, 2022
The method of alternating projections for extracting low-rank signals is considered. The problem of decreasing the computational costs while keeping the estimation accuracy is analyzed.
Nikita Zvonarev, Nina Golyandina
doaj   +1 more source

Toeplitz matrix completion via a low-rank approximation algorithm

Journal of Inequalities and Applications, 2020
In this paper, we propose a low-rank matrix approximation algorithm for solving the Toeplitz matrix completion (TMC) problem. The approximation matrix was obtained by the mean projection operator on the set of feasible Toeplitz matrices for every ...
Ruiping Wen, Yaru Fu
doaj   +1 more source

Fast and Robust Low-Rank Approximation for Five-Dimensional Seismic Data Reconstruction

IEEE Access, 2020
Five-dimensional (5D) seismic data reconstruction becomes more appealing in recent years because it takes advantage of five physical dimensions of the seismic data and can reconstruct data with large gap. The low-rank approximation approach is one of the
Juan Wu, Min Bai, Dong Zhang, Hang Wang, Guangtan Huang, Yangkang Chen   +5 more
doaj   +1 more source

PARS: Proxy-Based Automatic Rank Selection for Neural Network Compression via Low-Rank Weight Approximation

Mathematics, 2022
Low-rank matrix/tensor decompositions are promising methods for reducing the inference time, computation, and memory consumption of deep neural networks (DNNs).
Konstantin Sobolev, Dmitry Ermilov, Anh-Huy Phan, Andrzej Cichocki   +3 more
doaj   +1 more source

Subspace Reduction for Stochastic Planar Elasticity

Applied Mechanics, 2021
Stochastic eigenvalue problems are nonlinear and multiparametric. They require their own solution methods and remain one of the challenge problems in computational mechanics.
Harri Hakula, Mikael Laaksonen
doaj   +1 more source

Lower bounds for the low-rank matrix approximation

Journal of Inequalities and Applications, 2017
Low-rank matrix recovery is an active topic drawing the attention of many researchers. It addresses the problem of approximating the observed data matrix by an unknown low-rank matrix. Suppose that A is a low-rank matrix approximation of D, where D and A
Jicheng Li, Zisheng Liu, Guo Li
doaj   +1 more source