Results 21 to 30 of about 15,641 (253)
An Overview of Video Super-Resolution Algorithms
Journal of Physics: Conference Series, 2021 Abstract We investigate some excellent algorithms in the field of video space super-resolution based on artificial intelligence, structurally analyze the network structure of the algorithm and the commonly used loss functions. We also analyze the characteristics of algorithms in the new field of video space-time super-resolution.
Liu, C. +4 more
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FM-VSR: Feature Multiplexing Video Super-Resolution for Compressed Video
IEEE Access, 2021 Due to the limitation of shooting conditions in the real world, there exist many insufficient-resolution videos. To be transmitted under limited bandwidth conditions, low-resolution videos often have to be compressed further, which introduces more ...
Gang He +6 more
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Downscaling atmospheric chemistry simulations with physically consistent deep learning [PDF]
Geoscientific Model Development, 2022 Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods.
A. Geiss +4 more
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Applied Sciences, 2023 Although deep learning-based approaches for video processing have been extensively investigated, the lack of generality in network construction makes it challenging for practical applications, particularly in video restoration.
Yuanfeng Zheng, Yuchen Yan, Hao Jiang
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Video Super Resolution via Deep Global-Aware Network
IEEE Access, 2019 Video super-resolution aims to increase the resolution of videos by exploiting the intra-frame and inter-frame dependencies of the low-resolution video sequences.
Kwok-Wai Hung +2 more
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Motion Compensated Video Super Resolution [PDF]
, 2007 In this paper we present a variational, spatiotemporal video super resolution scheme that produces not just one but n high resolution video frames from an n frame low resolution video sequence. We use a generic prior and the output is artifact-free, sharp and superior in quality to state of the art home cinema video processors.
Sune Høgild Keller +2 more
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Deep Blind Video Super-resolution
CoRR, 2020 Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration. However, this assumption does not hold for video SR and usually leads to over-smoothed super-resolved images.
Jinshan Pan +3 more
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Video Super-Resolution With Temporal Group Attention [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention. In this work, we propose a novel method that can effectively incorporate temporal information in a hierarchical way.
Takashi Isobe +8 more
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Video super‐resolution with non‐local alignment network
IET Image Processing, 2021 Video super‐resolution (VSR) aims at recovering high‐resolution frames from their low‐resolution counterparts. Over the past few years, deep neural networks have dominated the video super‐resolution task because of its strong non‐linear representational ...
Chao Zhou +3 more
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Accelerating the Training of Video Super-resolution Models
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Despite that convolution neural networks (CNN) have recently demonstrated high-quality reconstruction for video super-resolution (VSR), efficiently training competitive VSR models remains a challenging problem. It usually takes an order of magnitude more time than training their counterpart image models, leading to long research cycles.
Lijian Lin +3 more
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