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Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring
[article]
2021
arXiv
pre-print
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information from unaligned neighboring frames for better video deblurring. ...
Instead of estimating alignment information, we propose a simple and effective deep Recurrent Neural Network with Multi-scale Bi-directional Propagation (RNN-MBP) to effectively propagate and gather the ...
Conclusion In this paper, we proposed an effective Recurrent Neural Network with Multi-scale Bi-directional Propagation (RNN-MBP) for video deblurring. ...
arXiv:2112.05150v1
fatcat:v7lww5dsojgvxatalut4rhfabm
Blur More To Deblur Better: Multi-Blur2Deblur For Efficient Video Deblurring
[article]
2020
arXiv
pre-print
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Secondly, we propose multi-blurring recurrent neural network (MBRNN) that can synthesize more blurred images from neighboring frames, yielding substantially improved performance with existing video deblurring ...
One of the key components for video deblurring is how to exploit neighboring frames. ...
the reference frame with deep neural networks (DNNs) or by propagating the information about past frames to the reference frame recurrently with recurrent neural network (RNN). ...
arXiv:2012.12507v1
fatcat:ji5zrqe7g5d4tdyaa2m22tqkhu
Learning to Extract Flawless Slow Motion From Blurry Videos
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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While it is possible to train a neural network to recover the sharp frames from their average, there is no guarantee of the temporal smoothness for the formed video, as the frames are estimated independently ...
In this paper, we introduce the task of generating a sharp slow-motion video given a low frame rate blurry video. ...
A possible solution is to use a recurrent neural network, which could store the past in its state. However, the training of recurrent neural networks to generate videos is extremely challenging. ...
doi:10.1109/cvpr.2019.00830
dblp:conf/cvpr/JinHF19
fatcat:gpbb5hkdxncz3etik3staxcupm
Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks
[chapter]
2018
Lecture Notes in Computer Science
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We train the network with richly varied synthetic data consisting of camera shake, realistic noise, and other common imaging defects. ...
We propose a neural approach for fusing an arbitrary-length burst of photographs suffering from severe camera shake and noise into a sharp and noise-free image. ...
Burst Image Deblurring Using Permutation Invariant CNNs ...
doi:10.1007/978-3-030-01237-3_45
fatcat:e4l2tmdgtzfb3fj7rujcnrduoi
Deep Image Deblurring: A Survey
[article]
2022
arXiv
pre-print
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Next we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison. ...
Recent advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. ...
By using intra-frame iterations, the RNNbased video deblurring network by Nah et al. [83] achieves better performance than Su et al. [120] . Zhou et al. ...
arXiv:2201.10700v1
fatcat:z77ogbieirf23brn73375dlht4
ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring
[article]
2021
arXiv
pre-print
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Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). ...
video deblurring. ...
We also present a newly-collected high-frame-rate dataset for video deblurring (HFR-DVD), featuring sharper frames and more realistic blurs. ...
arXiv:2103.04260v1
fatcat:xcxqunwufzaupbphtz4n2xpz44
Video Frame Interpolation without Temporal Priors
[article]
2021
arXiv
pre-print
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Video frame interpolation, which aims to synthesize non-exist intermediate frames in a video sequence, is an important research topic in computer vision. ...
Finally, experiments demonstrate that one well-trained model is enough for synthesizing high-quality slow-motion videos under complicated real-world situations. ...
[20] propose a recurrent neural network (RNN) to iteratively update the hidden state for output frames. Wang et al. ...
arXiv:2112.01161v1
fatcat:bbr75klttrblzp43nd32hntvfe
Deep Video Deblurring: The Devil is in the Details
[article]
2019
arXiv
pre-print
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Video deblurring for hand-held cameras is a challenging task, since the underlying blur is caused by both camera shake and object motion. ...
State-of-the-art deep networks exploit temporal information from neighboring frames, either by means of spatio-temporal transformers or by recurrent architectures. ...
[51] suggest a scale-recurrent neural network (RNN) to solve the deblurring problem at multiple resolutions in conjunction with a multi-scale loss. Deep image deblurring via GANs. ...
arXiv:1909.12196v1
fatcat:qnpy7jucsrfwrlbcsusuhxqoey
Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression
2021
IEEE Journal on Selected Topics in Signal Processing
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The second of these papers, "Attention-based neural networks for chroma intra prediction in video coding," also looks at intra-frame chroma prediction but does so with a very different approach. ...
for ×265, using recurrent probability models for the latent variables of the recurrent auto-encoder network that is used to encode the motion-compensated video frames. ...
doi:10.1109/jstsp.2021.3053364
fatcat:hjo5pvw6lvgpfga2wfq4vpaq3q
A deep learning framework for quality assessment and restoration in video endoscopy
[article]
2019
arXiv
pre-print
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Generative adversarial networks with carefully chosen regularization are finally used to restore corrupted frames. ...
To detect different artifacts our framework exploits fast multi-scale, single stage convolutional neural network detector. ...
different neural network architectures. ...
arXiv:1904.07073v1
fatcat:aixdba6zazdzzjqebwbeiu7snm
A deep learning framework for quality assessment and restoration in video endoscopy
2020
Medical Image Analysis
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Generative adversarial networks with carefully chosen regularization and training strategies for discriminator-generator networks are finally used to restore corrupted frames. ...
To detect and classify different artifacts, the proposed framework exploits fast, multi-scale and single stage convolution neural network detector. ...
Lu are supported by the Ludwig Institute for Cancer Research. J. ...
doi:10.1016/j.media.2020.101900
pmid:33246229
fatcat:4i5pqs27tfd3za3ikzq5bfw6aq
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020
303-312
Graph Sequence Recurrent Neural Network for Vision-Based Freezing of
Gait Detection. ...
Zhang,
Y., +, TIP 2020 1001-1015
Graph Sequence Recurrent Neural Network for Vision-Based Freezing of
Gait Detection. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Table of contents
2020
IEEE Transactions on Image Processing
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Liu 3039 A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections R. Spilger, A. Imle, J.-Y. Lee, B. Müller, O. T. ...
Limuti 8213 Optical Flow Based Co-Located Reference Frame for Video Compression ......... B. Li, J. Han, Y. Xu, and K. ...
doi:10.1109/tip.2019.2940373
fatcat:i7hktzn4wrfz5dhq7hj75u6esa
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TIP 2021 767-782 A Spatial-Temporal Recurrent Neural Network for Video Saliency Prediction. ...
., +, TIP 2021 963-975 Video Frame Interpolation and Enhancement via Pyramid Recurrent Frame-work. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30
2020
IEEE transactions on circuits and systems for video technology (Print)
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., +, TCSVT Jan. 2020 145-154 Recursive Residual Convolutional Neural Network-Based In-Loop Filtering for Intra Frames. ...
., +, TCSVT March 2020 646-660
Recursive Residual Convolutional Neural Network-Based In-Loop Filtering
for Intra Frames. ...
A Memory-Efficient Hardware Architecture for Connected Component Labeling in Embedded System. ...
doi:10.1109/tcsvt.2020.3043861
fatcat:s6z4wzp45vfflphgfcxh6x7npu
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