Accurate Image Super-Resolution Using Very Deep Convolutional Networks
We present a highly accurate single-image super-resolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification \cite{simonyan2015very}. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates ($10^4$ times higher than SRCNN \cite{dong2015image}) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.
PDF Abstract CVPR 2016 PDF CVPR 2016 Abstract
Colab
Urban100
VGGFace2
Set14
Set5
CASIA-WebFace
Manga109
MSU Video Upscalers: Quality Enhancement
IXI
