Image Deblurring
56 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Image Deblurring
Most implemented papers
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.
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.
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.
Residual Dense Network for Image Restoration
yulunzhang/RDN
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We fully exploit the hierarchical features from all the convolutional layers.
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.
