Image Deblurring

93 papers with code • 5 benchmarks • 5 datasets

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Libraries

Use these libraries to find Image Deblurring models and implementations
2 papers
867
2 papers
513
2 papers
408
2 papers
194
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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.

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.

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.

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.

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.

Gated Fusion Network for Joint Image Deblurring and Super-Resolution

jacquelinelala/GFN 27 Jul 2018

Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution.

Burst ranking for blind multi-image deblurring

pedrodiamel/ferattention 29 Oct 2018

The primary motivation is that current bursts deblurring methods do not handle well situations in which misalignment or out-of-context frames are present in the burst.