使用pytorch训练一个基于多尺度自编码网络的红外与可见光图像融合的模型,实现红外与可见光图像的融合代码
时间: 2023-09-23 11:03:33 浏览: 47
以下是一个基于多尺度自编码网络的红外与可见光图像融合的模型的PyTorch代码。这个模型由两个自编码器组成,一个用于处理可见光图像,另一个用于处理红外图像。两个自编码器的输出再经过一个融合模块进行融合。具体实现如下:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(x)
x = F.relu(self.conv2(x))
x = self.pool(x)
x = F.relu(self.conv3(x))
x = self.pool(x)
x = F.relu(self.conv4(x))
x = self.pool(x)
return x
class Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.convT1 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1)
self.convT2 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1)
self.convT3 = nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1)
self.convT4 = nn.ConvTranspose2d(16, 3, kernel_size=3, stride=2, padding=1, output_padding=1)
def forward(self, x):
x = F.relu(self.convT1(x))
x = F.relu(self.convT2(x))
x = F.relu(self.convT3(x))
x = F.relu(self.convT4(x))
return x
class FusionModule(nn.Module):
def __init__(self):
super(FusionModule, self).__init__()
self.conv1 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(16, 3, kernel_size=3, stride=1, padding=1)
def forward(self, x1, x2):
x = torch.cat((x1, x2), dim=1)
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = F.relu(self.conv4(x))
x = F.relu(self.conv5(x))
return x
class MultiScaleNet(nn.Module):
def __init__(self):
super(MultiScaleNet, self).__init__()
self.encoder1 = Encoder()
self.encoder2 = Encoder()
self.decoder1 = Decoder()
self.decoder2 = Decoder()
self.fusion = FusionModule()
def forward(self, x1, x2):
x1_1 = F.interpolate(x1, scale_factor=0.5)
x1_2 = F.interpolate(x1, scale_factor=0.25)
x1_3 = F.interpolate(x1, scale_factor=0.125)
x2_1 = F.interpolate(x2, scale_factor=0.5)
x2_2 = F.interpolate(x2, scale_factor=0.25)
x2_3 = F.interpolate(x2, scale_factor=0.125)
x1_e = self.encoder1(x1)
x1_1_e = self.encoder1(x1_1)
x1_2_e = self.encoder1(x1_2)
x1_3_e = self.encoder1(x1_3)
x2_e = self.encoder2(x2)
x2_1_e = self.encoder2(x2_1)
x2_2_e = self.encoder2(x2_2)
x2_3_e = self.encoder2(x2_3)
x1_d = self.decoder1(x1_e)
x1_1_d = self.decoder1(x1_1_e)
x1_2_d = self.decoder1(x1_2_e)
x1_3_d = self.decoder1(x1_3_e)
x2_d = self.decoder2(x2_e)
x2_1_d = self.decoder2(x2_1_e)
x2_2_d = self.decoder2(x2_2_e)
x2_3_d = self.decoder2(x2_3_e)
x_f1 = self.fusion(x1_e, x2_e)
x_f2 = self.fusion(x1_1_e, x2_1_e)
x_f3 = self.fusion(x1_2_e, x2_2_e)
x_f4 = self.fusion(x1_3_e, x2_3_e)
x_f3 = F.interpolate(x_f3, scale_factor=2)
x_f4 = F.interpolate(x_f4, scale_factor=4)
x_f = torch.cat((x_f1, x_f2, x_f3, x_f4), dim=1)
x = self.fusion(x_f, x_f)
return x
```
在这个模型中,我们使用了两个Encoder和两个Decoder来分别处理可见光图像和红外图像。Encoder和Decoder的结构都比较简单,只是由几个卷积层和池化层组成。在每个Encoder和Decoder中,我们使用了相同的卷积核大小和步幅,但是在不同的尺度上输入不同大小的图像时,输出尺寸会不同。在每个Encoder中,我们将最终的输出作为融合模块的输入。
融合模块是这个模型的核心部分。我们首先将两个Encoder的输出在不同的尺度上进行融合,然后将融合后的特征图进行插值,使得它们的大小与最终的输出一致。最后,我们将插值后的特征图在通道维度上进行拼接,然后通过一系列卷积层进行融合。最终的输出就是红外和可见光图像的融合结果。
训练过程中,我们可以使用像MSE(均方误差)或SSIM(结构相似性)这样的损失函数来评估模型的性能。我们还可以使用像Adam或SGD这样的优化器来优化模型的参数。