Publications (80)
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…
Graph Neural Net using Analytical Graph Filters and Topology Optimization for Image Denoising
Weng-tai Su, Gene Cheung, Richard Wildes +1
While convolutional neural nets (CNNs) have achieved remarkable performance for a wide range of inverse imaging applications, the filter coefficients are computed in a purely data-…
Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer
Seyed Alireza Hosseini, Tam Thuc Do, Gene Cheung +1
An image denoiser can be used for a wide range of restoration problems via the Plug-and-Play (PnP) architecture. In this paper, we propose a general framework to build an interpret…
3D Point Cloud Denoising via Bipartite Graph Approximation and Reweighted Graph Laplacian
Chinthaka Dinesh, Gene Cheung, Ivan V. Bajic
Point cloud is a collection of 3D coordinates that are discrete geometric samples of an object's 2D surfaces. Imperfection in the acquisition process means that point clouds are of…
Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree
Amin Zheng, Gene Cheung, Dinei Florencio
With the advent of depth sensing technologies, the extraction of object contours in images---a common and important pre-processing step for later higher-level computer vision tasks…
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation
Chinthaka Dinesh, Gene Cheung, Ivan V. Bajic +1
Acquired 3D point cloud data, whether from active sensors directly or from stereo-matching algorithms indirectly, typically contain non-negligible noise. To address the point cloud…