An Efficient Network Design for Face Video Super-resolution
Feng Yu, He Li, Sige Bian, Yongming Tang

TL;DR
This paper introduces HO-FVSR, a face video super-resolution network optimized for efficiency, reducing parameters and FLOPs while maintaining or improving quality, enabling real-time processing.
Contribution
It proposes a novel face-specific super-resolution network with hyper-parameter optimization and a simultaneous train-evaluation method for faster training and efficiency.
Findings
Reduces at least 52.4% parameters and 20.7% FLOPs
Achieves better PSNR and SSIM than state-of-the-art methods
Provides real-time processing at 47.62 FPS for 36x36x1x3 input sequences
Abstract
Face video super-resolution algorithm aims to reconstruct realistic face details through continuous input video sequences. However, existing video processing algorithms usually contain redundant parameters to guarantee different super-resolution scenes. In this work, we focus on super-resolution of face areas in original video scenes, while rest areas are interpolated. This specific super-resolved task makes it possible to cut redundant parameters in general video super-resolution networks. We construct a dataset consisting entirely of face video sequences for network training and evaluation, and conduct hyper-parameter optimization in our experiments. We use three combined strategies to optimize the network parameters with a simultaneous train-evaluation method to accelerate optimization process. Results show that simultaneous train-evaluation method improves the training speed and…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Image and Signal Denoising Methods
