Deep wiener deconvolution: Wiener meets deep learning for image deblurring
We present a simple and effective approach for non-blind image deblurring, combining
classical techniques and deep learning. In contrast to existing methods that deblur the …
classical techniques and deep learning. In contrast to existing methods that deblur the …
DWDN: Deep Wiener deconvolution network for non-blind image deblurring
We present a simple and effective approach for non-blind image deblurring, combining
classical techniques and deep learning. In contrast to existing methods that deblur the …
classical techniques and deep learning. In contrast to existing methods that deblur the …
Deep Richardson–Lucy deconvolution for low-light image deblurring
Images taken under the low-light condition often contain blur and saturated pixels at the
same time. Deblurring images with saturated pixels is quite challenging. Because of the …
same time. Deblurring images with saturated pixels is quite challenging. Because of the …
Deep learning for handling kernel/model uncertainty in image deconvolution
Most existing non-blind image deconvolution methods assume that the given blurring kernel
is error-free. In practice, blurring kernel often is estimated via some blind deblurring …
is error-free. In practice, blurring kernel often is estimated via some blind deblurring …
Variational-EM-based deep learning for noise-blind image deblurring
Non-blind deblurring is an important problem encountered in many image restoration tasks.
The focus of non-blind deblurring is on how to suppress noise magnification during …
The focus of non-blind deblurring is on how to suppress noise magnification during …
EDPN: Enhanced deep pyramid network for blurry image restoration
Image deblurring has seen a great improvement with the development of deep neural
networks. In practice, however, blurry images often suffer from additional degradations such …
networks. In practice, however, blurry images often suffer from additional degradations such …
Learning spatially variant linear representation models for joint filtering
Joint filtering mainly uses an additional guidance image as a prior and transfers its
structures to the target image in the filtering process. Different from existing approaches that …
structures to the target image in the filtering process. Different from existing approaches that …
Learning a non-blind deblurring network for night blurry images
Deblurring night blurry images is difficult, because the common-used blur model based on
the linear convolution operation does not hold in this situation due to the influence of …
the linear convolution operation does not hold in this situation due to the influence of …
Unfolding taylor's approximations for image restoration
Deep learning provides a new avenue for image restoration, which demands a delicate
balance between fine-grained details and high-level contextualized information during …
balance between fine-grained details and high-level contextualized information during …
Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural network
Nonblind image deconvolution (NID) is about restoring the latent image with sharp details
from a noisy blurred one using a known blur kernel. This paper presents a dataset-free deep …
from a noisy blurred one using a known blur kernel. This paper presents a dataset-free deep …