Deblurring
314 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 Deep CNN Denoiser Prior for Image Restoration
cszn/ircnn
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CVPR 2017
Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).
Unrolled Optimization with Deep Priors
A broad class of problems at the core of computational imaging, sensing, and low-level computer vision reduces to the inverse problem of extracting latent images that follow a prior distribution, from measurements taken under a known physical image formation model.
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
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
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.
Image Reconstruction with Predictive Filter Flow
aimerykong/predictive-filter-flow
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We propose a simple, interpretable framework for solving a wide range of image reconstruction problems such as denoising and deconvolution.
Neumann Networks for Inverse Problems in Imaging
dgilton/neumann_networks_code
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We present an end-to-end, data-driven method of solving inverse problems inspired by the Neumann series, which we call a Neumann network.
SABER: A Systems Approach to Blur Estimation and Reduction in X-ray Imaging
Blur in X-ray radiographs not only reduces the sharpness of image edges but also reduces the overall contrast.
Non-Causal Tracking by Deblatting
rozumden/tbd
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Tracking by Deblatting stands for solving an inverse problem of deblurring and image matting for tracking motion-blurred objects.
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
rozumden/deblatting_python
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CVPR 2020
We propose a novel method that tracks fast moving objects, mainly non-uniform spherical, in full 6 degrees of freedom, estimating simultaneously their 3D motion trajectory, 3D pose and object appearance changes with a time step that is a fraction of the video frame exposure time.
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging
We propose a recurrent neural-network for real-time reconstruction of acoustic camera spherical maps.
