Deblurring
313 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
Latest papers
SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields
jho-yonsei/smurf
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Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity.
Dual-domain strip attention for image restoration
c-yn/DSANet
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Neural Networks 2024
In this paper, we develop a dual-domain strip attention mechanism for image restoration by enhancing representation learning, which consists of spatial and frequency strip attention units.
Deep, convergent, unrolled half-quadratic splitting for image deconvolution
6zhc/decun
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Through extensive experimental studies, we verify that our approach achieves competitive performance with state-of-the-art unrolled layer-specific learning and significantly improves over the traditional HQS algorithm.
Gyroscope-Assisted Motion Deblurring Network
Yet, their practical usage in real-world deblurring, especially motion blur, remains limited due to the lack of pixel-aligned training triplets (background, blurred image, and blur heat map) and restricted information inherent in blurred images.
Plug-and-Play image restoration with Stochastic deNOising REgularization
Even if they produce impressive image restoration results, these algorithms rely on a non-standard use of a denoiser on images that are less and less noisy along the iterations, which contrasts with recent algorithms based on Diffusion Models (DM), where the denoiser is applied only on re-noised images.
InstructIR: High-Quality Image Restoration Following Human Instructions
All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model.
Efficient Image Deblurring Networks based on Diffusion Models
This article introduces a sliding window model for defocus deblurring that achieves the best performance to date with extremely low memory usage.
Application of Deep Learning in Blind Motion Deblurring: Current Status and Future Prospects
visionverse/blind-motion-deblurring-survey
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As a response, blind motion deblurring has emerged, aiming to restore clear and detailed images without prior knowledge of the blur type, fueled by the advancements in deep learning methodologies.
Short-Time Fourier Transform for deblurring Variational Autoencoders
Vibhu04/Deblurring-Variational-Autoencoders-with-STFT
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Variational Autoencoders (VAEs) are powerful generative models, however their generated samples are known to suffer from a characteristic blurriness, as compared to the outputs of alternative generating techniques.
Exposure Bracketing is All You Need for Unifying Image Restoration and Enhancement Tasks
It is highly desired but challenging to acquire high-quality photos with clear content in low-light environments.
