Search Results for author:
Found 14 papers, 11 papers with code
Exploiting Diffusion Prior for Real-World Image Super-Resolution
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR).
DifFace: Blind Face Restoration with Diffused Error Contraction
Moreover, the transition distribution can contract the error of the restoration backbone and thus makes our method more robust to unknown degradations.
Ranked #5 on
Blind Face Restoration
on CelebA-Test
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting
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.
Efficient Diffusion Model for Image Restoration by Residual Shifting
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.
Variational Denoising Network: Toward Blind Noise Modeling and Removal
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.
Ranked #10 on
Image Denoising
on DND
Deep Variational Network Toward Blind Image Restoration
In this proposed model, a pixel-wise non-i. i. d.
Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation
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 #2 on
Noise Estimation
on SIDD
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
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.
Semi-Supervised Video Deraining with Dynamical Rain Generator
Firstly, most of them do not sufficiently model the characteristics of rain layers of rainy videos.
From Rain Generation to Rain Removal
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.
Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model
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.
A Deep Variational Bayesian Framework for Blind Image Deblurring
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.
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise
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.
MOWA: Multiple-in-One Image Warping Model
To our knowledge, this is the first work that solves multiple practical warping tasks in one single model.
