Tensor completion via tensor QR decomposition and L2, 1-norm minimization
Y Zheng, AB Xu - Signal Processing, 2021 - Elsevier
In this paper, we consider the tensor completion problem, which has been a concern for
many researchers studying signal processing and computer vision. Our fast and precise …
many researchers studying signal processing and computer vision. Our fast and precise …
Signal reconstruction of compressed sensing based on alternating direction method of multipliers
The sparse signal reconstruction of compressive sensing can be accomplished by l_1 l 1-
norm minimization, but in many existing algorithms, there are the problems of low success …
norm minimization, but in many existing algorithms, there are the problems of low success …
Accelerated alternating minimization algorithm for Poisson noisy image recovery
Restoring images corrupted by Poisson noise have attracted much attention in recent years
due to its significant applications in image processing. There are various regularization …
due to its significant applications in image processing. There are various regularization …
Projection neural networks with finite-time and fixed-time convergence for sparse signal reconstruction
This paper considers the L 1-minimization problem for sparse signal and image
reconstruction by using projection neural networks (PNNs). Firstly, a new finite-time …
reconstruction by using projection neural networks (PNNs). Firstly, a new finite-time …
Joint and direct optimization for dictionary learning in convolutional sparse representation
GJ Peng - IEEE transactions on neural networks and learning …, 2019 - ieeexplore.ieee.org
Convolutional sparse coding (CSC) is a useful tool in many image and audio applications.
Maximizing the performance of CSC requires that the dictionary used to store the features of …
Maximizing the performance of CSC requires that the dictionary used to store the features of …
Generalized self-adaptive algorithm for solving split common fixed point problem and its application to image restoration problem
R Suparatulatorn, A Khemphet… - … Journal of Computer …, 2020 - Taylor & Francis
In this work, we introduce a self-adaptive algorithm, based on viscosity approximation
methods, to solve split common fixed point problems of demicontractive operators in real …
methods, to solve split common fixed point problems of demicontractive operators in real …
Stabilized BB projection algorithm for large-scale convex constrained nonlinear monotone equations to signal and image processing problems
J Rao, C Yu, N Huang - Journal of Computational and Applied Mathematics, 2024 - Elsevier
Abstract The Barzilai–Borwein (BB) method is a popular and efficient gradient method for
solving large-scale unconstrained optimization problems. In general, it converges much …
solving large-scale unconstrained optimization problems. In general, it converges much …
Generalized covariance-assisted matching pursuit
H Li, J Wen - Signal Processing, 2019 - Elsevier
Sparse recovery aims to accurately recover the support of sparse vector from a very limited
number of noisy linear measurements. By utilizing the covariance and mean of the sparse …
number of noisy linear measurements. By utilizing the covariance and mean of the sparse …
Accelerated Proximal Iterative re-Weighted Alternating Minimization for Image Deblurring
The quadratic penalty alternating minimization (AM) method is widely used for solving the
convex $\ell_1 $ total variation (TV) image deblurring problem. However, quadratic penalty …
convex $\ell_1 $ total variation (TV) image deblurring problem. However, quadratic penalty …
A new multi-view learning machine with incomplete data
Multi-view learning with incomplete views (MVL-IV) is a reliable algorithm to process
incomplete datasets which consist of instances with missing views or features. In MVL-IV, it …
incomplete datasets which consist of instances with missing views or features. In MVL-IV, it …