Image Deblurring

127 papers with code • 6 benchmarks • 5 datasets

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Use these libraries to find Image Deblurring models and implementations
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Most implemented papers

Restormer: Efficient Transformer for High-Resolution Image Restoration

swz30/restormer CVPR 2022

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

megvii-research/NAFNet 10 Apr 2022

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

swz30/MPRNet CVPR 2021

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 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 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 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 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 25 Dec 2018

We fully exploit the hierarchical features from all the convolutional layers.

Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

wyhuai/ddnm 1 Dec 2022

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)

google/RED 9 Nov 2016

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.