Search Results for author: Zongsheng Yue

Found 22 papers, 17 papers with code

Arbitrary-steps Image Super-resolution via Diffusion Inversion

1 code implementation12 Dec 2024 Zongsheng Yue, Kang Liao, Chen Change Loy

This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance.

Image Super-Resolution

Omegance: A Single Parameter for Various Granularities in Diffusion-Based Synthesis

1 code implementation26 Nov 2024 Xinyu Hou, Zongsheng Yue, Xiaoming Li, Chen Change Loy

In this work, we introduce a single parameter $\omega$, to effectively control granularity in diffusion-based synthesis.

Denoising

Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors

1 code implementation25 Sep 2024 Aiping Zhang, Zongsheng Yue, Renjing Pei, Wenqi Ren, Xiaochun Cao

Diffusion-based image super-resolution (SR) methods have achieved remarkable success by leveraging large pre-trained text-to-image diffusion models as priors.

Image Super-Resolution

Blind Image Deconvolution by Generative-based Kernel Prior and Initializer via Latent Encoding

1 code implementation20 Jul 2024 Jiangtao Zhang, Zongsheng Yue, Hui Wang, Qian Zhao, Deyu Meng

To alleviate this issue and further improve their performance, we propose a new framework for BID that better considers the prior modeling and the initialization for blur kernels, leveraging a deep generative model.

Generative Adversarial Network Image Deconvolution

Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration

1 code implementation26 Jun 2024 Kang Liao, Zongsheng Yue, Zhouxia Wang, Chen Change Loy

Previous domain adaptation methods have sought to bridge the domain gap by learning domain-invariant knowledge in either feature or pixel space.

Contrastive Learning Deblurring +4

MOWA: Multiple-in-One Image Warping Model

no code implementations16 Apr 2024 Kang Liao, Zongsheng Yue, Zhonghua Wu, Chen Change Loy

To our knowledge, this is the first work that solves multiple practical warping tasks in one single model.

model Motion Estimation +1

Efficient Diffusion Model for Image Restoration by Residual Shifting

1 code implementation12 Mar 2024 Zongsheng Yue, Jianyi Wang, Chen Change Loy

While diffusion-based image restoration (IR) methods have achieved remarkable success, they are still limited by the low inference speed attributed to the necessity of executing hundreds or even thousands of sampling steps.

Blind Face Restoration Image Inpainting +2

ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting

2 code implementations NeurIPS 2023 Zongsheng Yue, Jianyi Wang, Chen Change Loy

Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed due to the requirements of hundreds or even thousands of sampling steps.

Image Super-Resolution

Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model

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

To address these issues, in this work, we propose a low-rank diffusion model for hyperspectral pansharpening by simultaneously leveraging the power of the pre-trained deep diffusion model and better generalization ability of Bayesian methods.

Pansharpening

Exploiting Diffusion Prior for Real-World Image Super-Resolution

3 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

2 code implementations13 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.

Image Deblurring Variational Inference

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.

Image Denoising Noise Estimation

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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