Search Results for author:
Found 21 papers, 2 papers with code
SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective
To address this issue, we propose a novel TN paradigm, named SVD-inspired TN decomposition (SVDinsTN), which allows us to efficiently solve the TN-SS problem from a regularized modeling perspective, eliminating the repeated structure evaluations.
CD-GAN: a robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors
In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e. g., optical or radar).
A Triple-Double Convolutional Neural Network for Panchromatic Sharpening
Pansharpening refers to the fusion of a panchromatic image with a high spatial resolution and a multispectral image with a low spatial resolution, aiming to obtain a high spatial resolution multispectral image.
Nonlinear Transform Induced Tensor Nuclear Norm for Tensor Completion
The main idea of this type of methods is exploiting the low-rank structure of frontal slices of the targeted tensor under the linear transform along the third mode.
Fully-Connected Tensor Network Decomposition for Robust Tensor Completion Problem
In this paper, by leveraging the superior expression of the fully-connected tensor network (FCTN) decomposition, we propose a $\textbf{FCTN}$-based $\textbf{r}$obust $\textbf{c}$onvex optimization model (RC-FCTN) for the RTC problem.
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution
Hyperspectral image has become increasingly crucial due to its abundant spectral information.
Dynamic Cross Feature Fusion for Remote Sensing Pansharpening
In order to enhance the relationships of inter-branches, dynamic cross feature transfers are embedded into multiple branches to obtain high-resolution representations.
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling
This work employs an idea that models spectral images as tensors following the block-term decomposition model with multilinear rank-$(L_r, L_r, 1)$ terms (i. e., the LL1 model) and formulates the HSR problem as a coupled LL1 tensor decomposition problem.
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks
In order to alleviate this issue, in this work, we propose a simple and efficient architecture for deep convolutional neural networks to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution hyperspectral image (HR-HSI).
Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergence
Key words: nonconvex optimization, tensor ring rank, logdet function, tensor completion, alternating direction method of multipliers.
Tensor train rank minimization with nonlocal self-similarity for tensor completion
The TT rank minimization accompany with \emph{ket augmentation}, which transforms a lower-order tensor (e. g., visual data) into a higher-order tensor, suffers from serious block-artifacts.
Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion
In the literature, the tensor nuclear norm can be computed by using tensor singular value decomposition based on the discrete Fourier transform matrix, and tensor completion can be performed by the minimization of the tensor nuclear norm which is the relaxation of the sum of matrix ranks from all Fourier transformed matrix frontal slices.
Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery
Based on it, we define a novel tensor rank, the tensor $N$-tubal rank, as a vector whose elements contain the tubal rank of all mode-$k_1k_2$ unfolding tensors, to depict the correlations along different modes.
Rain Streak Removal for Single Image via Kernel Guided CNN
Then, our framework starts with learning the motion blur kernel, which is determined by two factors including angle and length, by a plain neural network, denoted as parameter net, from a patch of the texture component.
FastDeRain: A Novel Video Rain Streak Removal Method Using Directional Gradient Priors
In this paper, we propose a novel video rain streak removal approach FastDeRain, which fully considers the discriminative characteristics of rain streaks and the clean video in the gradient domain.
Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm
In this paper, we investigate tensor recovery problems within the tensor singular value decomposition (t-SVD) framework.
A Novel Tensor-Based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors
Rain streaks removal is an important issue of the outdoor vision system and has been recently investigated extensively.
Single image super-resolution by approximated Heaviside functions
We propose a new iterative model for single image super-resolution based on an observation: an image is consisted of smooth components and non-smooth components, and we use two classes of approximated Heaviside functions (AHFs) to represent them respectively.
New explicit thresholding/shrinkage formulas for one class of regularization problems with overlapping group sparsity and their applications
To solve this kind of ill-posed problems, a regularization term (i. e., regularizer) should be introduced, under the assumption that the solutions have some specific properties, such as sparsity and group sparsity.
Total variation with overlapping group sparsity for image deblurring under impulse noise
The total variation (TV) regularization method is an effective method for image deblurring in preserving edges.
Image Restoration using Total Variation with Overlapping Group Sparsity
Usually, the high-order total variation (HTV) regularizer is an good option except its over-smoothing property.
