Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)

Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural ne...

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Bibliographic Details
Published in:Journal of Computational Vision and Imaging Systems 2021-01, Vol.6 (1), p.1-6
Main Authors: Singh, Ayush, Ebrahimi, Mehran
Format: Article
Language:English
Online Access:Get full text
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Summary:Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural network that incorporates temporal information along with the current frame's structural information will be used. The edge information in each frame along with optical flow technique for motion estimation among frames will be applied. Promising results on validation datasets will be presented.
ISSN:2562-0444
2562-0444
DOI:10.15353/jcvis.v6i1.3555