Image Super-Resolution Using Very Deep Residual Channel Attention Networks

一，贡献

贡献有三点

• 提出了一个非常深的residual channel attention networks (RCAN)，用于超分辨率问题
• 提出了residual in residual (RIR)结构，用来构建非常深的能有效训练的网络，long skip 和short skip连接
• 使用channel attention(CA)，来rescale feature

二，方法

1，网络架构

输入低分辨率图片，首先过一个卷积层

F0为浅层特征，然后过一个RIR模块来提取深层次特征

然后使用上采样模块

然后过the reconstruction layer

损失为L1 loss

2， Residual in Residual (RIR)

看网络架构，RIR包含G个residual groups (RG) 和a long skip connection (LSC),每个RG包含B个residual channel attention blocks (RCAB) with short skip
connection (SSC)，
RCAB: 三层卷积+CA+残差

## Residual Channel Attention Block (RCAB)
class RCAB(nn.Module):
    def __init__(
        self, conv, n_feat, kernel_size, reduction,
        bias=True, bn=False, act=nn.ReLU(True), res_scale=1):

        super(RCAB, self).__init__()
        modules_body = []
        for i in range(2):
            modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))
            if bn: modules_body.append(nn.BatchNorm2d(n_feat))
            if i == 0: modules_body.append(act)
        modules_body.append(CALayer(n_feat, reduction))
        self.body = nn.Sequential(*modules_body)
        self.res_scale = res_scale

    def forward(self, x):
        res = self.body(x)
        #res = self.body(x).mul(self.res_scale)
        res += x
        return res

3，chanel attention(CA)

Channel Attention (CA) Layer
使用通道注意力机制要考虑两点

• 有能力学习到通道之间的非线性关系
• Second, as multiple channel-wise features can be emphasized opposed to one-hot activation, it must learn a non-mututually-exclusive relationship.（不太理解）

先过一个全局平均池化层

使用门机制

$W_D$为一个卷积层，使用reduction ratio r进行通道下采样，$W_U$也是一个卷积层，用ratio r来通道上采样，

进行channel 级别的rescale

代码如下

class CALayer(nn.Module):
    def __init__(self, channel, reduction=16):
        super(CALayer, self).__init__()
        # global average pooling: feature --> point
        self.avg_pool = nn.AdaptiveAvgPool2d(1) 
        # feature channel downscale and upscale --> channel weight
        self.conv_du = nn.Sequential(
                nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True), ##通道下采样
                nn.ReLU(inplace=True),
                nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True), ##通道上采样
                nn.Sigmoid()
        )

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.conv_du(y)
        return x * y