no code implementations • 29 Feb 2024 • Lingfeng li, Xue-Cheng Tai, Raymond Chan
Unlike previous methods, our approach requires the predicted ABP waveforms to satisfy the Navier-Stokes equation with a time-periodic condition and a Windkessel boundary condition.
no code implementations • 26 Jan 2024 • Yuxiang Hui, Yang Liu, Yaofang Liu, Fan Jia, Jinshan Pan, Raymond Chan, Tieyong Zeng
Video restoration task aims to recover high-quality videos from low-quality observations.
no code implementations • 31 Dec 2023 • Hao liu, Jun Liu, Raymond Chan, Xue-Cheng Tai
In this study, our goal is to integrate classical mathematical models with deep neural networks by introducing two novel deep neural network models for image segmentation known as Double-well Nets.
1 code implementation • 17 Oct 2023 • Yaofang Liu, Xiaodong Cun, Xuebo Liu, Xintao Wang, Yong Zhang, Haoxin Chen, Yang Liu, Tieyong Zeng, Raymond Chan, Ying Shan
For video generation, various open-sourced models and public-available services have been developed to generate high-quality videos.
no code implementations • 18 Jul 2023 • Hao liu, Xue-Cheng Tai, Raymond Chan
In this paper, we give an algorithmic explanation for deep neural networks, especially in their connections with operator splitting.
no code implementations • 18 Jul 2023 • Xue-Cheng Tai, Hao liu, Raymond Chan
We use the two-phase Potts model for image segmentation as an example for our explanations.
no code implementations • 27 Jan 2023 • Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Chan, Dong Sun
In this study, we developed a novel deep-learning algorithm called dual-view selective instance segmentation network (DVSISN) for segmenting unstained adherent cells in differential interference contrast (DIC) images.
no code implementations • 29 Sep 2022 • Jianfei Li, Chaoyan Huang, Raymond Chan, Han Feng, Micheal Ng, Tieyong Zeng
Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging.
1 code implementation • 25 May 2020 • Hok Shing Wong, Li Wang, Raymond Chan, Tieyong Zeng
We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order.
no code implementations • 4 Dec 2019 • Zitong Wang, Li Wang, Raymond Chan, Tieyong Zeng
A novel approach is then proposed to construct the graph of the input data from the learned graph of a small number of vertexes with some preferred properties.
no code implementations • 10 Oct 2019 • Jianchao Zhang, Angelica I. Aviles-Rivero, Daniel Heydecker, Xiaosheng Zhuang, Raymond Chan, Carola-Bibiane Schönlieb
We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects.
no code implementations • 21 May 2019 • Xiaohao Cai, Raymond Chan, Xiaoyu Xie, Tieyong Zeng
High-dimensional data classification is a fundamental task in machine learning and imaging science.
no code implementations • 27 Sep 2018 • Chao Wang, Robert Plemmons, Sudhakar Prasad, Raymond Chan, Mila Nikolova
An optical imager that exploits off-center image rotation to encode both the lateral and depth coordinates of point sources in a single snapshot can perform 3D localization and tracking of space debris.
no code implementations • 26 Jul 2018 • Xiaohao Cai, Raymond Chan, Carola-Bibiane Schonlieb, Gabriele Steidl, Tieyong Zeng
The piecewise constant Mumford-Shah (PCMS) model and the Rudin-Osher-Fatemi (ROF) model are two important variational models in image segmentation and image restoration, respectively.
no code implementations • 30 May 2015 • Xiaohao Cai, Raymond Chan, Mila Nikolova, Tieyong Zeng
In this paper, we propose a SLaT (Smoothing, Lifting and Thresholding) method with three stages for multiphase segmentation of color images corrupted by different degradations: noise, information loss, and blur.