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Found 37 papers, 4 papers with code
Graph Sparsification for GCN Towards Optimal Crop Yield Predictions
Specifically, we first show that greedily removing an edge at a time that induces the minimal change in the second eigenvalue leads to a sparse graph with good GCN performance.
Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention
We study 3D point cloud attribute compression using a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \rightarrow \mathbb{R}$, we quantize and encode parameter vector $\theta$ that characterizes $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf{x})$ at known 3D points $\mathbf{x}$'s at the decoder.
Efficient Directed Graph Sampling via Gershgorin Disc Alignment
To minimize worst-case reconstruction error of the linear system solution $\mathbf{x}^* = \mathbf{C}^{-1} \mathbf{H}^\top \mathbf{y}$ with symmetric coefficient matrix $\mathbf{C} = \mathbf{H}^\top \mathbf{H} + \mu \mathbf{L}_{rw}^\top \mathbf{L}_{rw}$, the sampling objective is to choose $\mathbf{H}$ to maximize the smallest eigenvalue $\lambda_{\min}(\mathbf{C})$ of $\mathbf{C}$.
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment
Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on various datasets.
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction
Prediction of annual crop yields at a county granularity is important for national food production and price stability.
Manifold Graph Signal Restoration using Gradient Graph Laplacian Regularizer
In the graph signal processing (GSP) literature, graph Laplacian regularizer (GLR) was used for signal restoration to promote piecewise smooth / constant reconstruction with respect to an underlying graph.
Hybrid Model-based / Data-driven Graph Transform for Image Coding
Transform coding to sparsify signal representations remains crucial in an image compression pipeline.
Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks
Specifically, based on a spectral analysis of multilayer GCN output, we derive a spectrum prior for the graph Laplacian matrix $\mathbf{L}$ to robustify the model expressiveness against over-smoothing.
Fast Computation of Generalized Eigenvectors for Manifold Graph Embedding
Experiments show that our embedding is among the fastest in the literature, while producing the best clustering performance for manifold graphs.
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement
Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization.
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment
We prove that, after partitioning $\mathcal{G}$ into $Q$ sub-graphs $\{\mathcal{G}^q\}^Q_{q=1}$, the smallest Gershgorin circle theorem (GCT) lower bound of $Q$ corresponding coefficient matrices -- $\min_q \lambda^-_{\min}(\mathbf{B}^q)$ -- is a lower bound for $\lambda_{\min}(\mathbf{B})$.
Fast sensor placement by enlarging principle submatrix for large-scale linear inverse problems
Sensor placement for linear inverse problems is the selection of locations to assign sensors so that the entire physical signal can be well recovered from partial observations.
Unfolding Projection-free SDP Relaxation of Binary Graph Classifier via GDPA Linearization
Algorithm unfolding creates an interpretable and parsimonious neural network architecture by implementing each iteration of a model-based algorithm as a neural layer.
Projection-free Graph-based Classifier Learning using Gershgorin Disc Perfect Alignment
We repose the SDR dual for solution $\bar{\mathbf{H}}$, then replace the PSD cone constraint $\bar{\mathbf{H}} \succeq 0$ with linear constraints derived from GDPA -- sufficient conditions to ensure $\bar{\mathbf{H}}$ is PSD -- so that the optimization becomes an LP per iteration.
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment
Specifically, to articulate a sampling objective, we first assume a super-resolution (SR) method based on feature graph Laplacian regularization (FGLR) that reconstructs the original high-resolution PC, given 3D points chosen by a sampling matrix $\H$.
Pre-demosaic Graph-based Light Field Image Compression
Using a conventional Bayer pattern, data captured at each pixel is a single color component (R, G or B). The sensed data then undergoes demosaicking (interpolation of RGB components per pixel) and conversion to an array of sub-aperture images (SAIs).
Fast & Robust Image Interpolation using Gradient Graph Laplacian Regularizer
In the graph signal processing (GSP) literature, it has been shown that signal-dependent graph Laplacian regularizer (GLR) can efficiently promote piecewise constant (PWC) signal reconstruction for various image restoration tasks.
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph spectral signal compression and denoising.
Unrolling of Deep Graph Total Variation for Image Denoising
While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set.
Signed Graph Metric Learning via Gershgorin Disc Perfect Alignment
Given a convex and differentiable objective $Q(\M)$ for a real symmetric matrix $\M$ in the positive definite (PD) cone -- used to compute Mahalanobis distances -- we propose a fast general metric learning framework that is entirely projection-free.
Sampling Signals on Graphs: From Theory to Applications
In this article, we review current progress on sampling over graphs focusing on theory and potential applications.
Graph Metric Learning via Gershgorin Disc Alignment
We propose a fast general projection-free metric learning framework, where the minimization objective $\min_{\textbf{M} \in \mathcal{S}} Q(\textbf{M})$ is a convex differentiable function of the metric matrix $\textbf{M}$, and $\textbf{M}$ resides in the set $\mathcal{S}$ of generalized graph Laplacian matrices for connected graphs with positive edge weights and node degrees.
Robust Deep Graph Based Learning for Binary Classification
In this paper, we propose a robust binary classifier, based on CNNs, to learn deep metric functions, which are then used to construct an optimal underlying graph structure used to clean noisy labels via graph Laplacian regularization (GLR).
Feature Graph Learning for 3D Point Cloud Denoising
In this work, we assume instead the availability of a relevant feature vector $\mathbf{f}_i$ per node $i$, from which we compute an optimal feature graph via optimization of a feature metric.
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
In this work, we combine the robustness merit of model-based approaches and the learning power of data-driven approaches for real image denoising.
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination
Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable.
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model
Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise.
Graph-Based Blind Image Deblurring From a Single Photograph
We leverage the new graph spectral interpretation for RGTV to design an efficient algorithm that solves for the skeleton image and the blur kernel alternately.
Non-Local Graph-Based Prediction For Reversible Data Hiding In Images
If sufficiently smooth, we pose a maximum a posteriori (MAP) problem using either a quadratic Laplacian regularizer or a graph total variation (GTV) term as signal prior.
Blind Image Deblurring via Reweighted Graph Total Variation
The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given estimated blur kernel, de-convolve blurry input to restore the target image.
Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree
We first prove theoretically that in general a joint denoising / compression approach can outperform a separate two-stage approach that first denoises then encodes contours lossily.
Graph Fourier Transform with Negative Edges for Depth Image Coding
Recent advent in graph signal processing (GSP) has led to the development of new graph-based transforms and wavelets for image / video coding, where the underlying graph describes inter-pixel correlations.
Robust Semi-Supervised Graph Classifier Learning with Negative Edge Weights
In response, we derive an optimal perturbation matrix $\boldsymbol{\Delta}$ - based on a fast lower-bound computation of the minimum eigenvalue of $\mathbf{L}$ via a novel application of the Haynsworth inertia additivity formula---so that $\mathbf{L} + \boldsymbol{\Delta}$ is positive semi-definite, resulting in a stable signal prior.
Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images
In this paper, we combine three image priors---Laplacian prior for DCT coefficients, sparsity prior and graph-signal smoothness prior for image patches---to construct an efficient JPEG soft decoding algorithm.
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain
Focusing on image denoising, we derive an optimal metric space assuming non-local self-similarity of pixel patches, leading to an optimal graph Laplacian regularizer for denoising in the discrete domain.
Precision Enhancement of 3D Surfaces from Multiple Compressed Depth Maps
In texture-plus-depth representation of a 3D scene, depth maps from different camera viewpoints are typically lossily compressed via the classical transform coding / coefficient quantization paradigm.
Navigation domain representation for interactive multiview imaging
In this paper, we propose a novel multiview data representation that permits to satisfy bandwidth and storage constraints in an interactive multiview streaming system.
