271 citations · 1.3k across the 60 of their papers we have counts for
64 papers
Mixed Graph Signal Analysis of Joint Image Denoising / Interpolation
Niruhan Viswarupan, Gene Cheung, Fengbo Lan +1
A noise-corrupted image often requires interpolation. Given a linear denoiser and a linear interpolator, when should the operations be independently executed in separate steps, and…
Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer
Yeganeh Gharedaghi, Gene Cheung, Xianming Liu
Images captured in poorly lit conditions are often corrupted by acquisition noise. Leveraging recent advances in graph-based regularization, we propose a fast Retinex-based restora…
Complex Graph Laplacian Regularizer for Inferencing Grid States
Chinthaka Dinesh, Junfei Wang, Gene Cheung +1
In order to maintain stable grid operations, system monitoring and control processes require the computation of grid states (e.g. voltage magnitude and angles) at high granularity.…
Graph Sparsification for GCN Towards Optimal Crop Yield Predictions
Saghar Bagheri, Gene Cheung, Tim Eadie
In agronomics, predicting crop yield at a per field/county granularity is important for farmers to minimize uncertainty and plan seeding for the next crop cycle. While state-of-the…
Modeling Viral Information Spreading via Directed Acyclic Graph Diffusion
Chinthaka Dinesh, Gene Cheung, Fei Chen +2
Viral information like rumors or fake news is spread over a communication network like a virus infection in a unidirectional manner: entity conveys information to a neighbor $j…
Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention
Tam Thuc Do, Philip A. Chou, Gene Cheung
We study 3D point cloud attribute compression using a volumetric approach: given a target volumetric attribute function , we quantize and e…
Efficient Directed Graph Sampling via Gershgorin Disc Alignment
Yuejiang Li, Hong Vicky Zhao, Gene Cheung
Graph sampling is the problem of choosing a node subset via sampling matrix to collect samples $\mathbf{y} = \mathbf{H} \mathbf{x} \in \mathbb…
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction
Saghar Bagheri, Chinthaka Dinesh, Gene Cheung +1
Prediction of annual crop yields at a county granularity is important for national food production and price stability. In this paper, towards the goal of better crop yield predict…
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment
Chinthaka Dinesh, Gene Cheung, Saghar Bagheri +1
A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. Howe…
Manifold Graph Signal Restoration using Gradient Graph Laplacian Regularizer
Fei Chen, Gene Cheung, Xue Zhang
In the graph signal processing (GSP) literature, graph Laplacian regularizer (GLR) was used for signal restoration to promote piecewise smooth / constant reconstruction with respec…
Hybrid Model-based / Data-driven Graph Transform for Image Coding
Saghar Bagheri, Tam Thuc Do, Gene Cheung +1
Transform coding to sparsify signal representations remains crucial in an image compression pipeline. While the Karhunen-Loève transform (KLT) computed from an empirical covariance…
Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks
Jin Zeng, Yang Liu, Gene Cheung +1
A graph convolutional network (GCN) employs a graph filtering kernel tailored for data with irregular structures. However, simply stacking more GCN layers does not improve performa…
Fast Computation of Generalized Eigenvectors for Manifold Graph Embedding
Fei Chen, Gene Cheung, Xue Zhang
Our goal is to efficiently compute low-dimensional latent coordinates for nodes in an input graph -- known as graph embedding -- for subsequent data processing such as clustering.…
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement
Xue Zhang, Gene Cheung, Jiahao Pang +3
A 3D point cloud is typically constructed from depth measurements acquired by sensors at one or more viewpoints. The measurements suffer from both quantization and noise corruption…
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment
Sadid Sahami, Gene Cheung, Chia-Wen Lin
We study the problem of efficiently summarizing a short video into several keyframes, leveraging recent progress in fast graph sampling. Specifically, we first construct a similari…
Fast sensor placement by enlarging principle submatrix for large-scale linear inverse problems
Fen Wang, Gene Cheung, Taihao Li +2
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. In…
Unfolding Projection-free SDP Relaxation of Binary Graph Classifier via GDPA Linearization
Cheng Yang, Gene Cheung, Wai-tian Tan +1
Algorithm unfolding creates an interpretable and parsimonious neural network architecture by implementing each iteration of a model-based algorithm as a neural layer. However, unfo…
Projection-free Graph-based Classifier Learning using Gershgorin Disc Perfect Alignment
Cheng Yang, Gene Cheung, Guangtao Zhai
In semi-supervised graph-based binary classifier learning, a subset of known labels are used to infer unknown labels, assuming that the label signal is smo…
Landmarking for Navigational Streaming of Stored High-Dimensional Media
Yuan Yuan, Gene Cheung, Pascal Frossard +2
Modern media data such as 360 videos and light field (LF) images are typically captured in much higher dimensions than the observers' visual displays. To efficiently browse high-di…
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment
Chinthaka Dinesh, Gene Cheung, Ivan Bajic
3D point cloud (PC) -- a collection of discrete geometric samples of a physical object's surface -- is typically large in size, which entails expensive subsequent operations like v…
Pre-demosaic Graph-based Light Field Image Compression
Yung-Hsuan Chao, Haoran Hong, Gene Cheung +1
An unfocused plenoptic light field (LF) camera places an array of microlenses in front of an image sensor in order to separately capture different directional rays arriving at an i…
Fast & Robust Image Interpolation using Gradient Graph Laplacian Regularizer
Fei Chen, Gene Cheung, Xue Zhang
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 r…
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Saghar Bagheri, Gene Cheung, Antonio Ortega +1
Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph spectral signal compression and denoising. Previous graph learnin…
Unrolling of Deep Graph Total Variation for Image Denoising
Huy Vu, Gene Cheung, Yonina C. Eldar
While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely o…