Deblurring
312 papers with code • 15 benchmarks • 14 datasets
Deblurring is a computer vision task that involves removing the blurring artifacts from images or videos to restore the original, sharp content. Blurring can be caused by various factors such as camera shake, fast motion, and out-of-focus objects, and can result in a loss of detail and quality in the captured images. The goal of deblurring is to produce a clear, high-quality image that accurately represents the original scene.
( Image credit: Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks )
Benchmarks
These leaderboards are used to track progress in Deblurring
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Libraries
Use these libraries to find Deblurring models and implementationsDatasets
GoPro
Real Blur Dataset
HIDE
Dialogue State Tracking Challenge
Deblur-NeRF
RSBlur
REDS
MSU BASED
Rendered WB dataset
Most implemented papers
Learning to See in the Dark
cchen156/Learning-to-See-in-the-Dark
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CVPR 2018
Imaging in low light is challenging due to low photon count and low SNR.
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
KupynOrest/DeblurGAN
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CVPR 2018
The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images.
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
xinntao/EDVR
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In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.
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.
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i. e. temporal super-resolution).
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.
Plug-and-Play Image Restoration with Deep Denoiser Prior
cszn/DPIR
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Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems.
