# Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution

arXiv: https://arxiv.org/abs/2009.03693 · HTML: https://mlsanity.com/paper/2009.03693/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Augmenting Perceptual Super-Resolution via Image Quality Predictors](https://mlsanity.com/paper/2504.18524/) (2025)
- [Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem](https://mlsanity.com/paper/2409.03179/) (2024)
- [A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution](https://mlsanity.com/paper/2107.03145/) (2021)
- [RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network](https://mlsanity.com/paper/2110.13217/) (2021)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [Real Image Super-Resolution using GAN through modeling of LR and HR process](https://mlsanity.com/paper/2210.10413/) (2022)
- [A Reference-Free Framework for Evaluating Single-Frame ISP Pipelines](https://mlsanity.com/paper/2607.23321/) (2026)

## Abstract (arXiv)

Recent deep learning based single image super-resolution (SISR) methods mostly train their models in a clean data domain where the low-resolution (LR) and the high-resolution (HR) images come from noise-free settings (same domain) due to the bicubic down-sampling assumption. However, such degradation process is not available in real-world settings. We consider a deep cyclic network structure to maintain the domain consistency between the LR and HR data distributions, which is inspired by the recent success of CycleGAN in the image-to-image translation applications. We propose the Super-Resolution Residual Cyclic Generative Adversarial Network (SRResCycGAN) by training with a generative adversarial network (GAN) framework for the LR to HR domain translation in an end-to-end manner. We demonstrate our proposed approach in the quantitative and qualitative experiments that generalize well to the real image super-resolution and it is easy to deploy for the mobile/embedded devices. In addition, our SR results on the AIM 2020 Real Image SR Challenge datasets demonstrate that the proposed SR approach achieves comparable results as the other state-of-art methods.