# Tarsier: Evolving Noise Injection in Super-Resolution GANs

arXiv: https://arxiv.org/abs/2009.12177 · HTML: https://mlsanity.com/paper/2009.12177/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Assessing the Impact of Image Super Resolution on White Blood Cell Classification Accuracy](https://mlsanity.com/paper/2508.03759/) (2025)

## Abstract (arXiv)

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.