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Recent progress in image deblurring
This paper comprehensively reviews the recent development of image deblurring, including
non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques …
non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques …
From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur
Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature
of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but …
of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but …
A comparative study for single image blind deblurring
Numerous single image blind deblurring algorithms have been proposed to restore latent
sharp images under camera motion. However, these algorithms are mainly evaluated using …
sharp images under camera motion. However, these algorithms are mainly evaluated using …
Recurrent neural networks with intra-frame iterations for video deblurring
Recurrent neural networks (RNNs) are widely used for sequential data processing. Recent
state-of-the-art video deblurring methods bank on convolutional recurrent neural network …
state-of-the-art video deblurring methods bank on convolutional recurrent neural network …
Spatio-temporal transformer network for video restoration
TH Kim, MSM Sajjadi, M Hirsch… - Proceedings of the …, 2018 - openaccess.thecvf.com
State-of-the-art video restoration methods integrate optical flow estimation networks to utilize
temporal information. However, these networks typically consider only a pair of consecutive …
temporal information. However, these networks typically consider only a pair of consecutive …
Flow-guided sparse transformer for video deblurring
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for
video deblurring. However, CNN-based methods show limitations in capturing long-range …
video deblurring. However, CNN-based methods show limitations in capturing long-range …
Online video deblurring via dynamic temporal blending network
T Hyun Kim, K Mu Lee, B Scholkopf… - Proceedings of the …, 2017 - openaccess.thecvf.com
State-of-the-art video deblurring methods are capable of removing non-uniform blur caused
by unwanted camera shake and/or object motion in dynamic scenes. However, most existing …
by unwanted camera shake and/or object motion in dynamic scenes. However, most existing …
Arvo: Learning all-range volumetric correspondence for video deblurring
Video deblurring models exploit consecutive frames to remove blurs from camera shakes
and object motions. In order to utilize neighboring sharp patches, typical methods rely …
and object motions. In order to utilize neighboring sharp patches, typical methods rely …
Estimating generalized gaussian blur kernels for out-of-focus image deblurring
Out-of-focus blur is a common image degradation phenomenon that occurs in case of lens
defocusing. The out-of-focus blur kernel is usually modeled as a Gaussian function or a …
defocusing. The out-of-focus blur kernel is usually modeled as a Gaussian function or a …
Robust kernel estimation with outliers handling for image deblurring
Estimating blur kernels from real world images is a challenging problem as the linear image
formation assumption does not hold when significant outliers, such as saturated pixels and …
formation assumption does not hold when significant outliers, such as saturated pixels and …