生成图像超分辨率重建的代码
时间: 2023-10-10 08:11:49 浏览: 45
以下是一个使用PyTorch实现的基于深度学习的图像超分辨率重建的代码示例:
```python
import torch
import torch.nn as nn
# 定义超分辨率重建模型
class SuperResolutionModel(nn.Module):
def __init__(self, upscale_factor):
super(SuperResolutionModel, self).__init__()
self.upscale_factor = upscale_factor
self.conv1 = nn.Conv2d(3, 64, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(64, 32, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(32, 3 * (upscale_factor ** 2), kernel_size=3, padding=1)
self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.relu(self.conv2(x))
x = nn.functional.relu(self.conv3(x))
x = self.pixel_shuffle(self.conv4(x))
return x
# 加载训练数据
train_data = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)
# 定义超分辨率重建模型和优化器
model = SuperResolutionModel(upscale_factor=2)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 训练模型
for epoch in range(100):
for batch, (low_res, high_res) in enumerate(train_data):
optimizer.zero_grad()
output = model(low_res)
loss = nn.functional.mse_loss(output, high_res)
loss.backward()
optimizer.step()
print("Epoch [{}/{}], Batch [{}/{}], Loss: {:.4f}".format(epoch+1, 100, batch+1, len(train_data), loss.item()))
# 使用测试数据测试模型
low_res_image = load_image("low_res_image.jpg")
model.eval()
with torch.no_grad():
high_res_image = model(low_res_image)
save_image(high_res_image, "high_res_image.jpg")
```
以上代码实现了一个基于深度学习的图像超分辨率重建模型,并使用训练数据对模型进行训练。在测试阶段,可以使用模型对低分辨率图像进行重建,得到高分辨率图像。