Search Results for author:
Found 16 papers, 2 papers with code
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging
Inverse problems in multi-dimensional imaging, e. g., completion, denoising, and compressive sensing, are challenging owing to the big volume of the data and the inherent ill-posedness.
LAConv: Local Adaptive Convolution for Image Fusion
The convolution operation is a powerful tool for feature extraction and plays a prominent role in the field of computer vision.
Hyperspectral Image Super-Resolution
Image Super-Resolution
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Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery
Recently, transform-based tensor nuclear norm minimization methods are considered to capture low-rank tensor structures to recover third-order tensors in multi-dimensional image processing applications.
Dictionary Learning with Low-rank Coding Coefficients for Tensor Completion
In this paper, we propose a novel tensor learning and coding model for third-order data completion.
Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation
In this paper, we develop a new alternating projection method to compute nonnegative low rank matrix approximation for nonnegative matrices.
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior
Recently, convolutional neural network (CNN)-based methods are proposed for hyperspectral images (HSIs) denoising.
Nonnegative Low Rank Tensor Approximation and its Application to Multi-dimensional Images
The main aim of this paper is to develop a new algorithm for computing nonnegative low rank tensor approximation for nonnegative tensors that arise in many multi-dimensional imaging applications.
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).
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.
Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections
In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images.
Deep Plug-and-play Prior for Low-rank Tensor Completion
By integrating deterministic low-rankness prior to the data-driven deep prior, we suggest a novel regularized tensor completion model for multi-dimensional image completion.
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.
