# A Matrix-in-matrix Neural Network for Image Super Resolution

arXiv: https://arxiv.org/abs/1903.07949 · HTML: https://mlsanity.com/paper/1903.07949/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2019.

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

- [ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution](https://mlsanity.com/paper/2307.00781/) (2023)
- [GRAN: Ghost Residual Attention Network for Single Image Super Resolution](https://mlsanity.com/paper/2302.14557/) (2023)
- [DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution](https://mlsanity.com/paper/2311.18508/) (2023)

## Abstract (arXiv)

In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks by utilizing deeper layers. However, their application is quite limited since they require high computing power. In addition, most of the existing methods rarely take full advantage of the intermediate features which are helpful for restoration. To address these issues, we propose a moderate-size SISR net work named matrixed channel attention network (MCAN) by constructing a matrix ensemble of multi-connected channel attention blocks (MCAB). Several models of different sizes are released to meet various practical requirements. Conclusions can be drawn from our extensive benchmark experiments that the proposed models achieve better performance with much fewer multiply-adds and parameters. Our models will be made publicly available.