Soft-IntroVAE for Continuous Latent space Image Super-Resolution
Zhi-Song Liu Zijia Wang Zhen Jia
Continuous image super-resolution (SR) recently receives a lot of attention
from researchers, for its practical and flexible image scaling for various
displays. Local implicit image representation is one of the methods that can
map the coordinates and 2D features for latent space interpolation. Inspired by
Variational AutoEncoder, we propose a Soft-introVAE for continuous latent space
image super-resolution (SVAE-SR). A novel latent space adversarial training is
achieved for photo-realistic image restoration. To further improve the quality,
a positional encoding scheme is used to extend the original pixel coordinates
by aggregating frequency information over the pixel areas. We show the
effectiveness of the proposed SVAE-SR through quantitative and qualitative
comparisons, and further, illustrate its generalization in denoising and
real-image super-resolution.