Deblurring
309 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
RSBlur
REDS
MSU BASED
Rendered WB dataset
Most implemented papers
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
dali92002/DE-GAN
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Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system.
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.
Intra-frame Object Tracking by Deblatting
rozumden/deblatting_python
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We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object.
Recurrent Video Restoration Transformer with Guided Deformable Attention
Specifically, RVRT divides the video into multiple clips and uses the previously inferred clip feature to estimate the subsequent clip feature.
Efficient Video Deblurring Guided by Motion Magnitude
Video deblurring is a highly under-constrained problem due to the spatially and temporally varying blur.
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.
A Neural Approach to Blind Motion Deblurring
We present a new method for blind motion deblurring that uses a neural network trained to compute estimates of sharp image patches from observations that are blurred by an unknown motion kernel.
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.
