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Found 11 papers, 8 papers with code
Unsupervised Pansharpening via Low-rank Diffusion Model
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
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
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.
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.
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.
Semi-Supervised Video Deraining with Dynamical Rain Generator
Firstly, most of them do not sufficiently model the characteristics of rain layers of rainy videos.
Deep Variational Network Toward Blind Image Restoration
In this proposed model, a pixel-wise non-i. i. d.
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.
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 #8 on
Image Denoising
on DND
(using extra training data)
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
