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

arXiv: https://arxiv.org/abs/2005.00953 · HTML: https://mlsanity.com/paper/2005.00953/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 17 of 17)

- [Real-World Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/2103.02368/) (2021)
- [Learning the Degradation Distribution for Blind Image Super-Resolution](https://mlsanity.com/paper/2203.04962/) (2022)
- [Cross-Modality High-Frequency Transformer for MR Image Super-Resolution](https://mlsanity.com/paper/2203.15314/) (2022)
- [Contextual colorization and denoising for low-light ultra high resolution sequences](https://mlsanity.com/paper/2101.01597/) (2021)
- [Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling](https://mlsanity.com/paper/2310.04964/) (2023)
- [The Perception-Robustness Tradeoff in Deterministic Image Restoration](https://mlsanity.com/paper/2311.09253/) (2023)
- [Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation](https://mlsanity.com/paper/2404.16564/) (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)
- [A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective](https://mlsanity.com/paper/2111.13078/) (2021)
- [Real-World Single Image Super-Resolution Under Rainy Condition](https://mlsanity.com/paper/2206.08345/) (2022)
- [Kernel Adversarial Learning for Real-world Image Super-resolution](https://mlsanity.com/paper/2104.09008/) (2021)
- [Real Image Super-Resolution using GAN through modeling of LR and HR process](https://mlsanity.com/paper/2210.10413/) (2022)
- [SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution](https://mlsanity.com/paper/2211.12180/) (2022)
- [Assessing the Impact of Image Super Resolution on White Blood Cell Classification Accuracy](https://mlsanity.com/paper/2508.03759/) (2025)
- [UnCapsTSR: An Unsupervised Transformer-based Image Super-Resolution Approach for Capsule Endoscopy Images](https://mlsanity.com/paper/2609.02476/) (2026)
- [CEM-TUDASR: Computationally efficient multi-modality transformer based unsupervised domain adaptive super-resolution approach](https://mlsanity.com/paper/2609.11201/) (2026)

## Abstract (arXiv)

Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of paired (LR/HR) training data. They usually take as assumption that the LR image is a bicubic down-sampled version of the HR image. However, such degradation process is not available in real-world settings i.e. inherent sensor noise, stochastic noise, compression artifacts, possible mismatch between image degradation process and camera device. It reduces significantly the performance of current SISR methods due to real-world image corruptions. To address these problems, we propose a deep Super-Resolution Residual Convolutional Generative Adversarial Network (SRResCGAN) to follow the real-world degradation settings by adversarial training the model with pixel-wise supervision in the HR domain from its generated LR counterpart. The proposed network exploits the residual learning by minimizing the energy-based objective function with powerful image regularization and convex optimization techniques. We demonstrate our proposed approach in quantitative and qualitative experiments that generalize robustly to real input and it is easy to deploy for other down-scaling operators and mobile/embedded devices.