# Real-World Single Image Super-Resolution Under Rainy Condition

arXiv: https://arxiv.org/abs/2206.08345 · HTML: https://mlsanity.com/paper/2206.08345/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 17 of 17)

- [Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model](https://mlsanity.com/paper/1904.00523/) (2019)
- [Deep Joint Rain Detection and Removal from a Single Image](https://mlsanity.com/paper/1609.07769/) (2016)
- [Attentive Generative Adversarial Network for Raindrop Removal from a Single Image](https://mlsanity.com/paper/1711.10098/) (2017)
- [Blind Super-Resolution With Iterative Kernel Correction](https://mlsanity.com/paper/1904.03377/) (2019)
- [Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks](https://mlsanity.com/paper/1809.00437/) (2018)
- [Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining](https://mlsanity.com/paper/1906.11129/) (2019)
- [Frequency Separation for Real-World Super-Resolution](https://mlsanity.com/paper/1911.07850/) (2019)
- [Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training](https://mlsanity.com/paper/2004.01178/) (2020)
- [Real-World Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/2103.02368/) (2021)
- [Unsupervised Learning for Real-World Super-Resolution](https://mlsanity.com/paper/1909.09629/) (2019)
- [Fast Adaptation to Super-Resolution Networks via Meta-Learning](https://mlsanity.com/paper/2001.02905/) (2020)
- [Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques](https://mlsanity.com/paper/2006.16471/) (2020)
- [Unsupervised Image Super-Resolution with an Indirect Supervised Path](https://mlsanity.com/paper/1910.02593/) (2019)
- [ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig](https://mlsanity.com/paper/2004.08513/) (2020)
- [Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution](https://mlsanity.com/paper/2005.00953/) (2020)
- [Gated Fusion Network for Degraded Image Super Resolution](https://mlsanity.com/paper/2003.00893/) (2020)
- [Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data](https://mlsanity.com/paper/1809.06420/) (2018)

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

- [Seeing Through the Rain: Resolving High-Frequency Conflicts in Deraining and Super-Resolution via Diffusion Guidance](https://mlsanity.com/paper/2511.12419/) (2025)

## Abstract (arXiv)

Image super-resolution is an important research area in computer vision that has a wide variety of applications including surveillance, medical imaging etc. Real-world signal image super-resolution has become very popular now-a-days due to its real-time application. There are still a lot of scopes to improve real-world single image super-resolution specially during challenging weather scenarios. In this paper, we have proposed a new algorithm to perform real-world single image super-resolution during rainy condition. Our proposed method can mitigate the influence of rainy conditions during image super-resolution. Our experiment results show that our proposed algorithm can perform image super-resolution decreasing the negative effects of the rain.