3 papers
eess.SP2026
Sparse Graph Learning from Sparse Data via Fiedler Number Maximization
Bahar Oveisgharan, Gene Cheung, Andrew Eckford
We aim to learn a sparse and connected graph from sparse data, where the number of observations K can be substantially smaller than the signal dimension N for signals x in R^N, and…
cs.CV2025
Unrolling Graph-based Douglas-Rachford Algorithm for Image Interpolation with Informed Initialization
Xue Zhang, Bingshuo Hu, Gene Cheung
Conventional deep neural nets (DNNs) initialize network parameters at random and then optimize each one via stochastic gradient descent (SGD), resulting in substantial risk of poor…
eess.IV2025
Unrolling Nonconvex Graph Total Variation for Image Denoising
Songlin Wei, Gene Cheung, Fei Chen +1
Conventional model-based image denoising optimizations employ convex regularization terms, such as total variation (TV) that convexifies the -norm to promote sparse signal…