RESEARCH
COMPUTER VISION
Tarsier: Evolving Noise Injection in Super-Resolution GANs
January 09, 2021
Abstract
Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by NESRGAN+, which injects a Gaussian noise after each residual layer at training time. In this paper, we harness evolutionary methods to improve NESRGAN+ by optimizing the noise injection at inference time. More precisely, we use Diagonal CMA to optimize the injected noise according to a novel criterion combining quality assessment and realism. Our results are validated by the PIRM perceptual score and a human study. Our method outperforms NESRGAN+ on several standard super-resolution datasets. More generally, our approach can be used to optimize any method based on noise injection.
AUTHORS
Written by
Camille Couprie
Olivier Teytaud
Andry Rasoanaivo
Hanhe Lin
Nathanaël Carraz Rakotonirina
Vlad Hosu
Publisher
ICPR
Research Topics
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