VDSR全称Very Deep Super Resolution,意思就是非常深的超分辨率网络(相比于SRCNN的3层网络结构),一共有20层,其文章见参考链接【1】。
We present a highly accurate single-image super-resolution (SR) method. Our method uses a very deep con-volutional network inspired by VGG-net used for ImageNet classification. We find increasing our network depth shows a significant improvement in accuracy.Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates (104times higher than SRCNN) enabled by adjustable gradient clipping. Our proposed method performs better than existing meth- ods in accuracy and visual improvements in our results are easily noticeable.
大意就是作者仿照VGG提出了一个有20层的深层网络,且通过残差学习来提升网络的收敛速度,在超分的任务上表现由于传统的SRCNN。
作者认为SRCNN的主要缺点有:
VDSR网络的架构图如下所示:
跟SRCNN主要的不同有:
主要可以分为如下几个部分:
有了上面的结构信息,就可以按照积木思想搭建网络了,只要注意几个细节就好:
class VDSR(nn.Module):
def __init__(self, in_channels) -> None:
super(VDSR, self).__init__()
self.block1 = nn.Sequential(
nn.Conv2d(in_channels=in_channels,out_channels=64, kernel_size=3,stride=1, padding=1),
nn.ReLU()
)
self.block2 = nn.Sequential(
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
nn.Conv2d(64,64,kernel_size=3, stride=1,padding=1),
nn.ReLU(),
)
self.block3 = nn.Sequential(
nn.Conv2d(64,in_channels,kernel_size=3, stride=1,padding=1)
)
def forward(self, x):
y = self.block1(x)
y = self.block2(y)
y = self.block3(y)
y = x + y
return y
VDSR这个网络整体来说没什么特别的,就是用了残差学习的思想,然后网络层深了点。其他的跟SRCNN是一样的思想。
【1】KIM J, LEE J K, LEE K M 2016. Accurate Image Super-Resolution Using Very Deep Convolutional Networks. 2016 Ieee Conference on Computer Vision and Pattern Recognition (Cvpr) [J]: 1646-1654.
【2】https://medium.com/towards-data-science/review-vdsr-super-resolution-f8050d49362f
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