Search Results for author: Tieyong Zeng

Found 21 papers, 5 papers with code

Spherical Image Inpainting with Frame Transformation and Data-driven Prior Deep Networks

no code implementations29 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.

Image Inpainting

Snow Mask Guided Adaptive Residual Network for Image Snow Removal

no code implementations11 Jul 2022 Bodong Cheng, Juncheng Li, Ying Chen, Shuyi Zhang, Tieyong Zeng

Recently, some methods have been proposed for snow removing, and most methods deal with snow images directly as the optimization object.

Image Restoration object-detection +3

A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification

no code implementations13 May 2022 Cheng Chang, Tieyong Zeng

The proposed model learns from both data and physics constraints through the training of a deep neural network, which serves as part of the covariance function in GPR.


Convex Augmentation for Total Variation Based Phase Retrieval

no code implementations21 Apr 2022 Jianwei Niu, Hok Shing Wong, Tieyong Zeng

Phase retrieval is an important problem with significant physical and industrial applications.

CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution

no code implementations19 Apr 2022 Guangwei Gao, Zixiang Xu, Juncheng Li, Jian Yang, Tieyong Zeng, Guo-Jun Qi

Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously.

Image Super-Resolution

A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications

no code implementations3 Nov 2021 Xinlei Zhou, Han Liu, Farhad Pourpanah, Tieyong Zeng, XiZhao Wang

This paper provides a comprehensive review of epistemic uncertainty learning techniques in supervised learning over the last five years.

From Beginner to Master: A Survey for Deep Learning-based Single-Image Super-Resolution

1 code implementation29 Sep 2021 Juncheng Li, Zehua Pei, Tieyong Zeng

In this survey, we give an overview of DL-based SISR methods and group them according to their targets, such as reconstruction efficiency, reconstruction accuracy, and perceptual accuracy.

Image Quality Assessment Image Super-Resolution +1

Transformer for Single Image Super-Resolution

1 code implementation25 Aug 2021 Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, Tieyong Zeng

LTB is composed of a series of Efficient Transformers (ET), which occupies a small GPU memory occupation, thanks to the specially designed Efficient Multi-Head Attention (EMHA).

Image Super-Resolution Single Image Super Resolution

Structure-Preserving Deraining with Residue Channel Prior Guidance

1 code implementation ICCV 2021 Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, Tieyong Zeng

Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures.

Single Image Deraining

Rank-One Prior: Toward Real-Time Scene Recovery

no code implementations CVPR 2021 Jun Liu, Ryan Wen Liu, Jianing Sun, Tieyong Zeng

To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze.

Autonomous Vehicles

A deep neural network approach on solving the linear transport model under diffusive scaling

no code implementations24 Feb 2021 Liu Liu, Tieyong Zeng, Zecheng Zhang

In our framework, the solution is approximated by a neural network that satisfies both the governing equation and other constraints.

Numerical Analysis Numerical Analysis

Edge Adaptive Hybrid Regularization Model For Image Deblurring

no code implementations20 Nov 2020 Tingting Zhang, Jie Chen, Caiying Wu, Zhifei He, Tieyong Zeng, Qiyu Jin

In the proposed model, it detects the edges and then spatially adjusts the parameters of Tikhonov and TV regularization terms for each pixel according to the edge information.

Deblurring Image Deblurring +2

Residual Learning for Effective joint Demosaicing-Denoising

no code implementations14 Sep 2020 Yu Guo, Qiyu Jin, Gabriele Facciolo, Tieyong Zeng, Jean-Michel Morel

Moreover, it is very difficult to change this order, because once the image is demosaicked, the statistical properties of the noise will be changed dramatically.

Demosaicking Denoising

Deep Tensor CCA for Multi-view Learning

1 code implementation25 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.

MULTI-VIEW LEARNING Tensor Decomposition

Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points

no code implementations4 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.

Graph structure learning

Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation

no code implementations26 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.

Image Restoration Image Segmentation +1

A Three-stage Approach for Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT)

no code implementations30 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.

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