Image Deblurring
127 papers with code • 6 benchmarks • 5 datasets
Libraries
Use these libraries to find Image Deblurring models and implementations
ImageNet
CelebA
GoPro
HIDE
Most implemented papers
Restormer: Efficient Transformer for High-Resolution Image Restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Simple Baselines for Image Restoration
Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods.
Multi-Stage Progressive Image Restoration
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
KupynOrest/DeblurGANv2
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ICCV 2019
We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility.
Scale-recurrent Network for Deep Image Deblurring
jiangsutx/SRN-Deblur
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CVPR 2018
In single image deblurring, the "coarse-to-fine" scheme, i. e. gradually restoring the sharp image on different resolutions in a pyramid, is very successful in both traditional optimization-based methods and recent neural-network-based approaches.
Uformer: A General U-Shaped Transformer for Image Restoration
ZhendongWang6/Uformer
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CVPR 2022
Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
Rethinking Coarse-to-Fine Approach in Single Image Deblurring
chosj95/mimo-unet
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ICCV 2021
Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks.
Residual Dense Network for Image Restoration
yulunzhang/RDN
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We fully exploit the hierarchical features from all the convolutional layers.
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
wyhuai/ddnm
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Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators.
The Little Engine that Could: Regularization by Denoising (RED)
As opposed to the $P^3$ method, we offer Regularization by Denoising (RED): using the denoising engine in defining the regularization of the inverse problem.
