A Matrix-in-matrix Neural Network for Image Super Resolution [article]

Hailong Ma and Xiangxiang Chu and Bo Zhang and Shaohua Wan and Bo Zhang
2019 arXiv   pre-print
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
more » ... n 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.
arXiv:1903.07949v1 fatcat:3gsyj43v3jcehippnatp5lweie