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Learning for Video Super-Resolution through HR Optical Flow Estimation
[article]
2018
arXiv
pre-print
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However, LR optical flows are used in existing deep learning based methods for correspondence generation. ...
Video super-resolution (SR) aims to generate a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterparts. ...
Introduction Super-resolution (SR) aims to generate high-resolution (HR) images or videos from their low-resolution (LR) counterparts. ...
arXiv:1809.08573v2
fatcat:hlx4xzmwkzd35hyxdhjb7lb3eu
BFRVSR: A Bidirectional Frame Recurrent Method for Video Super-Resolution
2020
Applied Sciences
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In this work, a bidirectional frame recurrent video super-resolution method is proposed. ...
Video super-resolution is a challenging task. One possible solution, called the sliding window method, tries to divide the generation of high-resolution video sequences into independent subtasks. ...
[6] proposed a recursive algorithm for video super-resolution. The FRVSR [6] network estimates the optical flow F LR t→t−1 of I LR t−1 and I LR t , and uses I HR t−1 . ...
doi:10.3390/app10238749
fatcat:bqgrkgkuqrhgpb3o6huplz4oeq
Video Super Resolution Based on Deep Learning: A Comprehensive Survey
[article]
2022
arXiv
pre-print
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It is well known that the leverage of information within video frames is important for video super-resolution. ...
In this survey, we comprehensively investigate 33 state-of-the-art video super-resolution (VSR) methods based on deep learning. ...
Yaowei Wang (Associate Professor with Peng Cheng Laboratory, Shenzhen, China) for their help in improving the quality of this manuscript. ...
arXiv:2007.12928v3
fatcat:nxoejcfdnzas3jznbqsale36ty
Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.05778v1 fatcat:saaeydemmncynb4od7moywnukmOther Versions
This paper explores an efficient solution for Space-time Super-Resolution, aiming to generate High-resolution Slow-motion videos from Low Resolution and Low Frame rate videos. ...
Input LR frames are super-resolved using a state-of-the-art Video Super-Resolution method. ...
[39] learned self-supervised task-specific optical flow for various Video enhancement problems, including temporal interpolation. Niklaus et al. ...
arXiv:2104.05778v3
fatcat:cbgo3l5sxbdsdf77vcogmesx5y
Frame-Recurrent Video Super-Resolution
[article]
2018
arXiv
pre-print
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In this work, we propose an end-to-end trainable frame-recurrent video super-resolution framework that uses the previously inferred HR estimate to super-resolve the subsequent frame. ...
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate ...
the same optical flow and super-resolution networks. ...
arXiv:1801.04590v4
fatcat:l3b2fk3cyjeyxok3rbyhzeieyu
Deep Video Super-Resolution using HR Optical Flow Estimation
[article]
2020
arXiv
pre-print
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Existing deep learning based methods commonly estimate optical flows between LR frames to provide temporal dependency. ...
Video super-resolution (SR) aims at generating a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterparts. ...
Flow Estimation.: To obtain HR optical flows from LR inputs, an alternative is to perform single image super-resolution (SISR) on separated LR frames first and then estimate HR optical flows from these ...
arXiv:2001.02129v1
fatcat:a5n3elabhbhf3l7emwayfc3oh4
Medical Video Super-Resolution Based on Asymmetric Back-Projection Network with Multilevel Error Feedback
2021
IEEE Access
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We construct a single-frame medical video super-resolution model as the benchmark model, combine the optical flow algorithm and multiframe fusion strategy to propose a medical video super-resolution method ...
Medical video is important for medical diagnosis. ...
This article is extended from the Conference paper written by Sheng Ren in The 12th IEEE International Conference on Cyber, Physical and Social Computing (Towards Efficient Medical Video Super-Resolution ...
doi:10.1109/access.2021.3054433
fatcat:fyju5efvdbe5fd4q35i7m3d7vq
Frame and Feature-Context Video Super-Resolution
[article]
2019
arXiv
pre-print
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For video super-resolution, current state-of-the-art approaches either process multiple low-resolution (LR) frames to produce each output high-resolution (HR) frame separately in a sliding window fashion ...
or recurrently exploit the previously estimated HR frames to super-resolve the following frame. ...
The goal in image and video super-resolution (SR) is to reconstruct a high-resolution (HR) image or video from its down-sampled low-resolution (LR) version. ...
arXiv:1909.13057v1
fatcat:yqqyeeukojevxh7mt6nmgscavq
Frame and Feature-Context Video Super-Resolution
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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For video super-resolution, current state-of-the-art approaches either process multiple low-resolution (LR) frames to produce each output high-resolution (HR) frame separately in a sliding window fashion ...
or recurrently exploit the previously estimated HR frames to super-resolve the following frame. ...
The goal in image and video super-resolution (SR) is to reconstruct a high-resolution (HR) image or video from its down-sampled low-resolution (LR) version. ...
doi:10.1609/aaai.v33i01.33015597
fatcat:l2cikopu6bcuzi3ocetrmtx6ke
Super resolution reconstruction algorithm of UAV image based on residual neural network
2021
IEEE Access
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with the predicted high-resolution optical flow. ...
In low resolution space, the dense residual network is used to obtain the complementary information of adjacent video frames, and then the optical flow of highresolution video frames is predicted through ...
super-resolution fusion network for better fusion of video frames, and the loss function of the network and the super-resolution fusion module is estimated by using the sum of optical flow residuals. ...
doi:10.1109/access.2021.3114437
fatcat:7zvontrovrgtlkqpepb3hfqhbi
DSTnet: Deformable Spatio-Temporal Convolutional Residual Network for Video Super-Resolution
2021
Mathematics
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Video super-resolution (VSR) aims at generating high-resolution (HR) video frames with plausible and temporally consistent details using their low-resolution (LR) counterparts, and neighboring frames. ...
These methods cannot fully exploit the spatio-temporal information that significantly affects the quality of resultant HR videos. ...
Earlier VSR methods estimated motion through separate multiple optical flow algorithms [5] and enabled an end-to-end trainable process for VSR. ...
doi:10.3390/math9222873
fatcat:dlh2t4fs25bbvp55wb3mbgt42y
Prediction-assistant Frame Super-Resolution for Video Streaming
[article]
2021
arXiv
pre-print
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., lossy), we propose to use previously received high-resolution frames to enhance the low-quality current ones. For the first case, we propose a small yet effective video frame prediction network. ...
For the second case, we improve the video prediction network to a video enhancement network to associate current frames as well as previous frames to restore high-quality images. ...
Moreover, we learn the residual optical flow using the U-net, which can be added to the initial estimated optical flow for refinement. ...
arXiv:2103.09455v1
fatcat:53if7lovwnaxplhtos54vsh4lq
Deep Gradient Prior Regularized Robust Video Super-Resolution
2021
Electronics
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This paper proposes a robust multi-frame video super-resolution (SR) scheme to obtain high SR performance under large upscaling factors. ...
A forward and backward motion field prior is used to regularize the estimation of the motion flow between frames. ...
Figure 5 . 5 Optical flow estimation results (color coded) of video "city". ...
doi:10.3390/electronics10141641
fatcat:xp2igllfr5ftbbrbwzswt3w6oi
Deformable Non-local Network For Video Super-Resolution
[article]
2019
arXiv
pre-print
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The video super-resolution (VSR) task aims to restore a high-resolution video frame by using its corresponding low-resolution frame and multiple neighboring frames. ...
At present, many deep learning-based VSR methods rely on optical flow to perform frame alignment. The final recovery results will be greatly affected by the accuracy of optical flow. ...
It is a non-flow-based method for effective and efficient video super-resolutions. ...
arXiv:1909.10692v1
fatcat:kvmdpw3gpjhyvitfj7cjhc7e4e
HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area Surveillance
[article]
2022
arXiv
pre-print
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In this paper we propose an end-to-end trainable deep network that performs optical flow estimation and frame reconstruction by combining inputs from both video feeds. ...
This paper presents the usage of multiple video feeds for the generation of HSTR video as an extension of reference based super resolution (RefSR). ...
Applying single image super-resolution (SISR) directly to each LR video frame produces HR video but lacks temporal coherency. ...
arXiv:2204.04435v1
fatcat:wxjnbtnccratjiv6hqn2dksbuy
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