[HTML][HTML] A non-convex tensor rank approximation for tensor completion
Low-rankness has been widely exploited for the tensor completion problem. Recent
advances have suggested that the tensor nuclear norm often leads to a promising …
advances have suggested that the tensor nuclear norm often leads to a promising …
Cauchy noise removal by nonconvex ADMM with convergence guarantees
Image restoration is one of the essential tasks in image processing. In order to restore
images from blurs and noise while also preserving their edges, one often applies total …
images from blurs and noise while also preserving their edges, one often applies total …
A denoising algorithm for partial discharge measurement based on the combination of wavelet threshold and total variation theory
J Tang, S Zhou, C Pan - IEEE Transactions on Instrumentation …, 2019 - ieeexplore.ieee.org
In electrical engineering, partial discharge (PD) measurement is frequently employed to
detect insulation defects and judge insulation conditions of high-voltage electrical …
detect insulation defects and judge insulation conditions of high-voltage electrical …
Auto-weighted robust low-rank tensor completion via tensor-train
Nowadays, multi-dimensional data (tensor data) have shown their capability of preserving
multilinear structures. Due to the measuring error or other non-human factors, these data …
multilinear structures. Due to the measuring error or other non-human factors, these data …
Compressive deconvolution in medical ultrasound imaging
The interest of compressive sampling in ultrasound imaging has been recently extensively
evaluated by several research teams. Following the different application setups, it has been …
evaluated by several research teams. Following the different application setups, it has been …
Image segmentation based on an active contour model of partial image restoration with local cosine fitting energy
In this paper, we use the cosine function to express the data energy fitting of a traditional
active contours model and propose a model based on sectional image recovery local cosine …
active contours model and propose a model based on sectional image recovery local cosine …
Tensor completion via convolutional sparse coding with small samples-based training
Tensor data often suffer from missing value problems due to the complex high-dimensional
structure while acquiring them. To complete the missing information, lots of Low-Rank …
structure while acquiring them. To complete the missing information, lots of Low-Rank …
Double reweighted sparse regression and graph regularization for hyperspectral unmixing
Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral
signatures in each mixed pixel, has attracted considerable attention in analyzing …
signatures in each mixed pixel, has attracted considerable attention in analyzing …
[HTML][HTML] Simultaneous image fusion and denoising by using fractional-order gradient information
JJ Mei, Y Dong, TZ Huang - Journal of Computational and Applied …, 2019 - Elsevier
Image fusion and denoising are significant in image processing because of the availability of
multi-sensor and the presence of the noise. The first-order and second-order gradient …
multi-sensor and the presence of the noise. The first-order and second-order gradient …
Effective blind image deblurring using matrix-variable optimization
L Huang, Y Xia, T Ye - IEEE Transactions on Image Processing, 2021 - ieeexplore.ieee.org
Blind image deblurring has been a challenging issue due to the unknown blur and
computation problem. Recently, the matrix-variable optimization method successfully …
computation problem. Recently, the matrix-variable optimization method successfully …