no code implementations • 13 Feb 2024 • Zhaoming Kong, Xiaowei Yang
In this paper, we propose a simple and effective one step GCP-based image denoising (GCP-ID) method, which aims to exploit the GCP for denoising in the sRGB space by integrating it into the classic nonlocal transform domain denoising framework.
1 code implementation • 18 Apr 2023 • Zhaoming Kong, Fangxi Deng, Haomin Zhuang, Jun Yu, Lifang He, Xiaowei Yang
In this paper, to investigate the applicability of existing denoising techniques, we compare a variety of denoising methods on both synthetic and real-world datasets for different applications.
1 code implementation • 23 Sep 2022 • Jun Yu, Zhaoming Kong, Liang Zhan, Li Shen, Lifang He
The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task.
no code implementations • 21 Nov 2021 • Jun Yu, Zhaoming Kong, Aditya Kendre, Hao Peng, Carl Yang, Lichao Sun, Alex Leow, Lifang He
This paper presents a novel graph-based kernel learning approach for connectome analysis.
1 code implementation • 31 Jul 2021 • Zhaoming Kong, Lichao Sun, Hao Peng, Liang Zhan, Yong Chen, Lifang He
In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis.
no code implementations • 6 Nov 2020 • Zhaoming Kong, Xiaowei Yang, Lifang He
Leveraging the nonlocal self-similarity (NLSS) characteristic of images and sparse representation in the transform domain, the block-matching and 3D filtering (BM3D) based methods show powerful denoising performance.
no code implementations • 11 Feb 2019 • Zhaoming Kong, Xiaowei Yang
In this paper, we mainly investigate the influence and potential of representation at patch level by considering a general formulation with block diagonal matrix.
1 code implementation • 10 Sep 2018 • Zhaoming Kong, Xiaowei Yang
Filtering real-world color images is challenging due to the complexity of noise that can not be formulated as a certain distribution.