Abstract
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.
Abstract (translated)
近年来，在单图像超分辨率（SISR）任务中，采用较深层次的学习方法以较高的峰信噪比取得了令人印象深刻的效果。但是，它们的应用非常有限，因为它们需要很高的计算能力。此外，现有的方法大多很少充分利用有助于恢复的中间特征。为了解决这些问题，我们提出了一种中等规模的SISR网络，即矩阵式信道注意网络（MCAN），通过构造多连接信道注意块（MCAB）的矩阵集合。为了满足各种实际需求，发布了几种不同尺寸的型号。从大量的基准实验中可以得出结论，即所提出的模型具有更好的性能，且乘法加法和参数更少。我们的模型将公开发布。
URL
https://arxiv.org/abs/1903.07949
