# GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention

arXiv: https://arxiv.org/abs/2111.05014 · HTML: https://mlsanity.com/paper/2111.05014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model](https://mlsanity.com/paper/2103.08255/) (2021)
- [Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning](https://mlsanity.com/paper/2103.08233/) (2021)

## Abstract (arXiv)

Single Image Super-Resolution (SISR) is a very active research field. This paper addresses SISR by using a GAN-based approach with dual discriminators and incorporating it with an attention mechanism. The experimental results show that GDCA can generate sharper and high pleasing images compare to other conventional methods.