iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks

iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks

Abstract Recently, learning-based models have enhanced the performance of single-image super-resolution (SISR). However, applying SISR successively to each video frame leads to a lack of temporal coherency. Convolutional neural networks (CNNs) outperform traditional approaches in terms of image qual...

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Bibliographic Details
Main Authors: Aman Chadha, John Britto, M. Mani Roja
Format: Article
Language:English
Published: SpringerOpen 2020-07-01
Series:Computational Visual Media
Subjects:
super resolution
video upscaling
frame recurrence
optical flow
generative adversarial networks
convolutional neural networks
Online Access:https://doi.org/10.1007/s41095-020-0175-7

Internet

https://doi.org/10.1007/s41095-020-0175-7

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