Search Results for author: Zongsheng Yue

Found 11 papers, 8 papers with code

Unsupervised Pansharpening via Low-rank Diffusion Model

1 code implementation18 May 2023 Xiangyu Rui, Xiangyong Cao, Zeyu Zhu, Zongsheng Yue, Deyu Meng

Specifically, we assume that the HRMS image is decomposed into the product of two low-rank tensors, i. e., the base tensor and the coefficient matrix.


Exploiting Diffusion Prior for Real-World Image Super-Resolution

no code implementations11 May 2023 Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin C. K. Chan, Chen Change Loy

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR).

Blind Super-Resolution Image Super-Resolution

DifFace: Blind Face Restoration with Diffused Error Contraction

1 code implementation13 Dec 2022 Zongsheng Yue, Chen Change Loy

Moreover, the transition distribution can contract the error of the restoration backbone and thus makes our method more robust to unknown degradations.

Blind Face Restoration

Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel

1 code implementation CVPR 2022 Zongsheng Yue, Qian Zhao, Jianwen Xie, Lei Zhang, Deyu Meng, Kwan-Yee K. Wong

To address the above issues, this paper proposes a model-based blind SISR method under the probabilistic framework, which elaborately models image degradation from the perspectives of noise and blur kernel.

Image Super-Resolution

Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise

no code implementations CVPR 2021 Xiangyu Rui, Xiangyong Cao, Qi Xie, Zongsheng Yue, Qian Zhao, Deyu Meng

A general approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements.

Denoising Variational Inference

A Deep Variational Bayesian Framework for Blind Image Deblurring

no code implementations5 Jun 2021 Hui Wang, Zongsheng Yue, Qian Zhao, Deyu Meng

Under this framework, the posterior of the latent clean image and blur kernel can be jointly estimated in an amortized inference fashion with DNNs, and the involved inference DNNs can be trained by fully considering the physical blur model, together with the supervision of data driven priors for the clean image and blur kernel, which is naturally led to by the evidence lower bound objective.

Blind Image Deblurring Image Deblurring +1

Semi-Supervised Video Deraining with Dynamical Rain Generator

1 code implementation CVPR 2021 Zongsheng Yue, Jianwen Xie, Qian Zhao, Deyu Meng

Firstly, most of them do not sufficiently model the characteristics of rain layers of rainy videos.

Rain Removal

From Rain Generation to Rain Removal

1 code implementation CVPR 2021 Hong Wang, Zongsheng Yue, Qi Xie, Qian Zhao, Yefeng Zheng, Deyu Meng

For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets.

Single Image Deraining Variational Inference

Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation

2 code implementations ECCV 2020 Zongsheng Yue, Qian Zhao, Lei Zhang, Deyu Meng

Specifically, we approximate the joint distribution with two different factorized forms, which can be formulated as a denoiser mapping the noisy image to the clean one and a generator mapping the clean image to the noisy one.

Ranked #8 on Image Denoising on DND (using extra training data)

Image Denoising

Variational Denoising Network: Toward Blind Noise Modeling and Removal

2 code implementations NeurIPS 2019 Zongsheng Yue, Hongwei Yong, Qian Zhao, Lei Zhang, Deyu Meng

On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression.

Image Denoising Noise Estimation +1

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