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Found 21 papers, 3 papers with code
Introducing Graph Learning over Polytopic Uncertain Graph
This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i. e., the graph is not exactly known, but its parameters or properties vary within a known range.
Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping
However, since SSTV refers only to adjacent pixels/bands, semi-local spatial structures are not preserved during denoising process.
Towards Robust Hyperspectral Anomaly Detection: Decomposing Background, Anomaly, and Mixed Noise via Convex Optimization
We propose a novel hyperspectral (HS) anomaly detection method that is robust to various types of noise.
Robust Spatiotemporal Fusion of Satellite Images: A Constrained Convex Optimization Approach
This paper proposes a novel spatiotemporal (ST) fusion framework for satellite images, named Robust Optimization-based Spatiotemporal Fusion (ROSTF).
Sparse Index Tracking: Simultaneous Asset Selection and Capital Allocation via $\ell_0$-Constrained Portfolio
In this paper, we propose a new problem formulation of sparse index tracking using an $\ell_0$-norm constraint that enables easy control of the upper bound on the number of assets in the portfolio.
Graph Signal Sampling Under Smoothness Priors: A Difference-of-Convex Approach
This paper proposes a method for properly sampling graph signals under smoothness priors.
Towards Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization
Second, existing methods do not explicitly account for the effects of stripe noise, which is common in HS measurements, in their formulations, resulting in significant degradation of unmixing performance when such noise is present in the input HS image.
Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications
To overcome these limitations, we establish an Operator norm-based design method of Variable-wise Diagonal Preconditioning (OVDP).
Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization
Our method simultaneously estimates an HR-HS image and a noiseless guide image, so the method can utilize spatial information in a guide image even if it is contaminated by heavy noise.
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising
The spatio-spectral total variation (SSTV) model has been widely used as an effective regularization of hyperspectral images (HSI) for various applications such as mixed noise removal.
Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise
The proposed method can avoid the difficulty of sensor selection with strongly correlated measurement noise, in which the possible sensor locations must be known in advance for calculating the precision matrix for selecting sensor locations.
Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data
We propose a time-varying graph signal recovery method for estimating the true time-varying graph signal from observations that are corrupted by missing values, unknown position outliers, and some random noise.
Guided Facial Skin Color Correction
In portrait photographs, skin color is often distorted due to the lighting environment (e. g., light reflected from a colored background wall and over-exposure by a camera strobe), and if the photo is artificially combined with another background color, this color change is emphasized, resulting in an unnatural synthesized result.
A General Destriping Framework for Remote Sensing Images Using Flatness Constraint
To resolve this, two requirements need to be considered: a general framework that can handle a variety of image regularizations in destriping, and a strong stripe noise characterization that can consistently capture the nature of stripe noise, regardless of the choice of image regularization.
On The Synergy Between Nonconvex Extensions of The Tensor Nuclear Norm for Tensor Recovery
Since the Tucker rank is nonconvex and discontinuous, many relaxations of the Tucker rank have been proposed, e. g., the tensor nuclear norm, weighted tensor nuclear norm, and weighted tensor Schatten-$p$ norm.
Epigraphical Relaxation for Minimizing Layered Mixed Norms
Mixed norm regularization methods play a central role in signal reconstruction and processing, where their optimization relies on the fact that the proximity operators of the mixed norms can be computed efficiently.
A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization
The methods have to handle a regularization term(s) and a data-fidelity term(s) simultaneously in one objective function, and so we need to carefully control the hyperparameter(s) that balances these terms.
TrendNets: Mapping Emerging Research Trends From Dynamic Co-Word Networks via Sparse Representation
Mapping the knowledge structure from word co-occurrences in a collection of academic papers has been widely used to provide insight into the topic evolution in an arbitrary research field.
Fast Singular Value Shrinkage with Chebyshev Polynomial Approximation Based on Signal Sparsity
We propose an approximation method for thresholding of singular values using Chebyshev polynomial approximation (CPA).
Decorrelated Vectorial Total Variation
This paper proposes a new vectorial total variation prior (VTV) for color images.
A Convex Regularizer for Reducing Color Artifact in Color Image Recovery
We propose a new convex regularizer, named the local color nuclear norm (LCNN), for color image recovery.
