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Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring [article]

Chao Zhu, Hang Dong, Jinshan Pan, Boyang Liang, Yuhao Huang, Lean Fu, Fei Wang
2021 arXiv   pre-print
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]

Dongwon Park, Dong Un Kang, Se Young Chun
2020 arXiv   pre-print
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

Meiguang Jin, Zhe Hu, Paolo Favaro
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
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]

Miika Aittala, Frédo Durand
2018 Lecture Notes in Computer Science  
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]

Kaihao Zhang, Wenqi Ren, Wenhan Luo, Wei-Sheng Lai, Bjorn Stenger, Ming-Hsuan Yang, Hongdong Li
2022 arXiv   pre-print
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]

Dongxu Li, Chenchen Xu, Kaihao Zhang, Xin Yu, Yiran Zhong, Wenqi Ren, Hanna Suominen, Hongdong Li
2021 arXiv   pre-print
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]

Youjian Zhang, Chaoyue Wang, Dacheng Tao
2021 arXiv   pre-print
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]

Jochen Gast, Stefan Roth
2019 arXiv   pre-print
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

A. Murat Tekalp, Michele Covell, Radu Timofte, Chao Dong
2021 IEEE Journal on Selected Topics in Signal Processing  
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]

Sharib Ali, Felix Zhou, Adam Bailey, Barbara Braden, James East, Xin Lu, Jens Rittscher
2019 arXiv   pre-print
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

Sharib Ali, Felix Zhou, Adam Bailey, Barbara Braden, James East, Xin Lu, Jens Rittscher
2020 Medical Image Analysis  
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  
., +, 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  
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  
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)  
., +, 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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