图像修复深度学习代码

很抱歉，根据提供的引用内容，没有提供具体的图像修复深度学习代码。但是，我可以为您提供一个常见的图像修复深度学习算法的代码示例，供您参考。

以下是一个使用深度学习框架PyTorch实现的图像修复算法的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader

# 定义图像修复模型
class ImageRestorationModel(nn.Module):
    def __init__(self):
        super(ImageRestorationModel, self).__init__()
        # 定义网络结构
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU()
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(256, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 3, kernel_size=3, stride=1, padding=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

# 加载数据集
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])
dataset = ImageFolder('path/to/dataset', transform=transform)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# 初始化模型和优化器
model = ImageRestorationModel()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 10
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
criterion = nn.MSELoss()
for epoch in range(num_epochs):
    for images, _ in dataloader:
        images = images.to(device)
        outputs = model(images)
        loss = criterion(outputs, images)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# 使用模型进行图像修复
test_image = Image.open('path/to/test_image.jpg')
test_image = transform(test_image).unsqueeze(0).to(device)
restored_image = model(test_image)
restored_image = restored_image.squeeze(0).cpu().detach().numpy()

# 显示修复后的图像
plt.imshow(restored_image.transpose(1, 2, 0))
plt.axis('off')
plt.show()

请注意，这只是一个简单的示例代码，实际的图像修复深度学习算法可能更加复杂，并且需要根据具体的任务和数据集进行调整和优化。