Image Deblurring
129 papers with code • 6 benchmarks • 5 datasets
Libraries
Use these libraries to find Image Deblurring models and implementations
ImageNet
CelebA
GoPro
HIDE
Most implemented papers
Group-based Sparse Representation for Image Restoration
In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR).
Class-specific image deblurring
In image deblurring, a fundamental problem is that the blur kernel suppresses a number of spatial frequencies that are difficult to recover reliably.
Patch-Ordering as a Regularization for Inverse Problems in Image Processing
Recent work in image processing suggests that operating on (overlapping) patches in an image may lead to state-of-the-art results.
Deep Video Deblurring
We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
On-Demand Learning for Deep Image Restoration
rhgao/on-demand-learning
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ICCV 2017
While machine learning approaches to image restoration offer great promise, current methods risk training models fixated on performing well only for image corruption of a particular level of difficulty---such as a certain level of noise or blur.
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
SeungjunNah/DeepDeblur-PyTorch
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CVPR 2017
To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear.
Deep Video Deblurring for Hand-Held Cameras
We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
Image Restoration by Iterative Denoising and Backward Projections
In this work, we propose an alternative method for solving inverse problems using off-the-shelf denoisers, which requires less parameter tuning.
Blind Image Deconvolution using Deep Generative Priors
axium/Blind-Image-Deconvolution-using-Deep-Generative-Priors
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This paper proposes a novel approach to regularize the \textit{ill-posed} and \textit{non-linear} blind image deconvolution (blind deblurring) using deep generative networks as priors.
Bringing Alive Blurred Moments
This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.
