# Image Super-Resolution Using a Wavelet-based Generative Adversarial Network

arXiv: https://arxiv.org/abs/1907.10213 · HTML: https://mlsanity.com/paper/1907.10213/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances](https://mlsanity.com/paper/2209.13131/) (2022)
- [SWAGAN: A Style-based Wavelet-driven Generative Model](https://mlsanity.com/paper/2102.06108/) (2021)
- [USIS: Unsupervised Semantic Image Synthesis](https://mlsanity.com/paper/2109.14715/) (2021)
- [Wavelet-based Loss for High-frequency Interface Dynamics](https://mlsanity.com/paper/2209.02316/) (2022)
- [Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative Models](https://mlsanity.com/paper/2208.02089/) (2022)
- [Locality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs](https://mlsanity.com/paper/2309.14883/) (2023)

## Abstract (arXiv)

In this paper, we consider the problem of super-resolution recons-truction. This is a hot topic because super-resolution reconstruction has a wide range of applications in the medical field, remote sensing monitoring, and criminal investigation. Compared with traditional algorithms, the current super-resolution reconstruction algorithm based on deep learning greatly improves the clarity of reconstructed pictures. Existing work like Super-Resolution Using a Generative Adversarial Network (SRGAN) can effectively restore the texture details of the image. However, experimentally verified that the texture details of the image recovered by the SRGAN are not robust. In order to get super-resolution reconstructed images with richer high-frequency details, we improve the network structure and propose a super-resolution reconstruction algorithm combining wavelet transform and Generative Adversarial Network. The proposed algorithm can efficiently reconstruct high-resolution images with rich global information and local texture details. We have trained our model by PyTorch framework and VOC2012 dataset, and tested it by Set5, Set14, BSD100 and Urban100 test datasets.