# An Attention-Based Approach for Single Image Super Resolution

arXiv: https://arxiv.org/abs/1807.06779 · HTML: https://mlsanity.com/paper/1807.06779/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [INSIGHT: An Interpretable Neural Vision-Language Framework for Reasoning of Generative Artifacts](https://mlsanity.com/paper/2511.22351/) (2025)
- [Open Set Domain Recognition via Attention-Based GCN and Semantic Matching Optimization](https://mlsanity.com/paper/2105.04967/) (2021)
- [Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks](https://mlsanity.com/paper/2210.12310/) (2022)

## Abstract (arXiv)

The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.