论文标题
有效的图像超分辨率使用广泛的现场注意
Efficient Image Super-Resolution using Vast-Receptive-Field Attention
论文作者
论文摘要
注意机制在设计高级超分辨率（SR）网络中起着关键作用。在这项工作中，我们通过改善注意力机制设计有效的SR网络。我们从一个简单的像素注意模块开始，然后逐渐修改它，以通过降低参数实现更好的超分辨率性能。特定方法包括：（1）增加注意力分支的接受场，（2）用深度可分开的卷积代替大型致密卷积内核，以及（3）引入像素归一化。这些方法为注意机制设计了一个明确的进化路线图。基于这些观察结果，我们提出了VAPSR，即广泛的场景像素注意网络。实验证明了VAPSR的出色性能。 VAPSR的表现优于当前的轻量级网络，其参数更少。 VAPSR的轻型版本只能使用IMDB和RFDN的21.68％和28.18％的参数来实现与这些网络相似的性能。代码和型号可在https://github.com/zhoumumu/vapsr上找到。
The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel attention module and gradually modify it to achieve better super-resolution performance with reduced parameters. The specific approaches include: (1) increasing the receptive field of the attention branch, (2) replacing large dense convolution kernels with depth-wise separable convolutions, and (3) introducing pixel normalization. These approaches paint a clear evolutionary roadmap for the design of attention mechanisms. Based on these observations, we propose VapSR, the VAst-receptive-field Pixel attention network. Experiments demonstrate the superior performance of VapSR. VapSR outperforms the present lightweight networks with even fewer parameters. And the light version of VapSR can use only 21.68% and 28.18% parameters of IMDB and RFDN to achieve similar performances to those networks. The code and models are available at https://github.com/zhoumumu/VapSR.
