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20122023
most cited3D Point Cloud Denoising via Bipartite Graph Approximation and Reweighted Graph Laplacian

12 citations · 23 across the 16 of their papers we have counts for

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8 papers · 1 filter

cs.CV2021

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…

cs.CV2020

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…

cs.CV2019

Feature Graph Learning for 3D Point Cloud Denoising

Wei Hu, Xiang Gao, Gene Cheung +1

Identifying an appropriate underlying graph kernel that reflects pairwise similarities is critical in many recent graph spectral signal restoration schemes, including image denoisi…

cs.CV2018

SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination

Chih-Chung Hsu, Chia-Wen Lin, Weng-Tai Su +1

Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the ident…

cs.CV2018

Deep Graph Laplacian Regularization for Robust Denoising of Real Images

Jin Zeng, Jiahao Pang, Wenxiu Sun +1

Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitiv…

cs.CV2018

3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model

Jin Zeng, Gene Cheung, Michael Ng +2

3D point cloud - a new signal representation of volumetric objects - is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfe…