# Perceptual Extreme Super Resolution Network with Receptive Field Block

arXiv: https://arxiv.org/abs/2005.12597 · HTML: https://mlsanity.com/paper/2005.12597/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 15 of 15)

- [GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond](https://mlsanity.com/paper/2207.14812/) (2022)
- [Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution](https://mlsanity.com/paper/2105.03939/) (2021)
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- [Generative AI in Vision: A Survey on Models, Metrics and Applications](https://mlsanity.com/paper/2402.16369/) (2024)
- [Single Image Super-Resolution Methods: A Survey](https://mlsanity.com/paper/2202.11763/) (2022)
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- [EarthGen: Generating the World from Top-Down Views](https://mlsanity.com/paper/2409.01491/) (2024)
- [UltraZoom: Generating Gigapixel Images from Regular Photos](https://mlsanity.com/paper/2506.13756/) (2025)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [High-Frequency aware Perceptual Image Enhancement](https://mlsanity.com/paper/2105.11711/) (2021)
- [Cascading Modular Network (CAM-Net) for Multimodal Image Synthesis](https://mlsanity.com/paper/2106.09015/) (2021)
- [One-to-many Approach for Improving Super-Resolution](https://mlsanity.com/paper/2106.10437/) (2021)
- [Reinforced Swin-Convs Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2205.00434/) (2022)
- [Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution](https://mlsanity.com/paper/2208.02861/) (2022)

## Abstract (arXiv)

Perceptual Extreme Super-Resolution for single image is extremely difficult, because the texture details of different images vary greatly. To tackle this difficulty, we develop a super resolution network with receptive field block based on Enhanced SRGAN. We call our network RFB-ESRGAN. The key contributions are listed as follows. First, for the purpose of extracting multi-scale information and enhance the feature discriminability, we applied receptive field block (RFB) to super resolution. RFB has achieved competitive results in object detection and classification. Second, instead of using large convolution kernels in multi-scale receptive field block, several small kernels are used in RFB, which makes us be able to extract detailed features and reduce the computation complexity. Third, we alternately use different upsampling methods in the upsampling stage to reduce the high computation complexity and still remain satisfactory performance. Fourth, we use the ensemble of 10 models of different iteration to improve the robustness of model and reduce the noise introduced by each individual model. Our experimental results show the superior performance of RFB-ESRGAN. According to the preliminary results of NTIRE 2020 Perceptual Extreme Super-Resolution Challenge, our solution ranks first among all the participants.