# Edge-Informed Single Image Super-Resolution

arXiv: https://arxiv.org/abs/1909.05305 · HTML: https://mlsanity.com/paper/1909.05305/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

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

- [Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis](https://mlsanity.com/paper/2307.12084/) (2023)
- [Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution](https://mlsanity.com/paper/2212.01628/) (2022)
- [Scene Structure Guidance Network: Unfolding Graph Partitioning into Pixel-Wise Feature Learning](https://mlsanity.com/paper/2301.00555/) (2023)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [Deep Learning-Based MR Image Re-parameterization](https://mlsanity.com/paper/2206.05516/) (2022)
- [Multi-Modality Image Super-Resolution using Generative Adversarial Networks](https://mlsanity.com/paper/2206.09193/) (2022)
- [How to Re-enable PDE Loss for Physical Systems Modeling Under Partial Observation](https://mlsanity.com/paper/2412.09116/) (2024)

## Abstract (arXiv)

The recent increase in the extensive use of digital imaging technologies has brought with it a simultaneous demand for higher-resolution images. We develop a novel edge-informed approach to single image super-resolution (SISR). The SISR problem is reformulated as an image inpainting task. We use a two-stage inpainting model as a baseline for super-resolution and show its effectiveness for different scale factors (x2, x4, x8) compared to basic interpolation schemes. This model is trained using a joint optimization of image contents (texture and color) and structures (edges). Quantitative and qualitative comparisons are included and the proposed model is compared with current state-of-the-art techniques. We show that our method of decoupling structure and texture reconstruction improves the quality of the final reconstructed high-resolution image. Code and models available at: https://github.com/knazeri/edge-informed-sisr