28 December 2020 Video super-resolution network via enhanced deep feature extraction and residual up-down block
Jiajia Lei, Xiaohai He, Chao Ren, Xiaohong Wu, Yi Wang
Author Affiliations +
Abstract

Video super-resolution (VSR) is an image restoration task, aiming to reconstruct a high-resolution (HR) video from its down-sampled low-resolution (LR) version. Convolutional neural networks (CNNs) have been applied to VSR successfully. Explicit motion estimation and motion compensation (ME&MC) module is commonly used in the previous CNNs-based methods to better exploit input frames’ temporal similarity. We proposed a VSR network without an explicit ME&MC module. Our network makes full use of spatiotemporal information and can implicitly capture motion relations between frames. Specifically, we proposed an enhanced deep feature extraction module (EDFEM) to extract deep features from input frames. EDFEM exploits not only intra-frame spatial information but also inter-frame temporal information to enhance feature representation. Furthermore, we proposed a residual up-down block (RUDB) to fuse features. RUDB adopts up- and down-sampling layers as the residual branch. Compared to the common residual block, RUDB addresses mutual dependencies of LR and HR images. Visual and quantitative results show that our method achieves state-of-the-art performance.

© 2020 SPIE and IS&T 1017-9909/2020/$28.00© 2020 SPIE and IS&T
Jiajia Lei, Xiaohai He, Chao Ren, Xiaohong Wu, and Yi Wang "Video super-resolution network via enhanced deep feature extraction and residual up-down block," Journal of Electronic Imaging 29(6), 063016 (28 December 2020). https://doi.org/10.1117/1.JEI.29.6.063016
Received: 18 August 2020; Accepted: 25 November 2020; Published: 28 December 2020
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Cited by 2 scholarly publications.
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KEYWORDS
Feature extraction

Video

Lawrencium

Super resolution

Bismuth

Visualization

Associative arrays

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