使用CIFAR10数据集实现椒盐噪声的图像去噪并显示图片，要完整代码

下面是使用PyTorch实现椒盐噪声的图像去噪并显示图片的完整代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

# 超参数
batch_size = 128
learning_rate = 0.001
num_epochs = 10
noise_ratio = 0.2

# 加载数据集
transform = transforms.Compose([transforms.ToTensor()])
train_data = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_data = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_data, batch_size=batch_size, shuffle=False)

# 定义模型
class DenoiseCNN(nn.Module):
    def __init__(self):
        super(DenoiseCNN, self).__init__()

        self.encoder = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.ConvTranspose2d(16, 3, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
            nn.BatchNorm2d(3),
            nn.Sigmoid()
        )

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

model = DenoiseCNN()

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for data in train_loader:
        img, _ = data

        # 添加椒盐噪声
        noise = np.random.rand(*img.shape) < noise_ratio
        img[noise] = 0.0 if np.random.rand() < 0.5 else 1.0

        # 前向传播
        output = model(img)
        loss = criterion(output, img)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # 打印训练信息
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 测试模型
model.eval()
with torch.no_grad():
    for data in test_loader:
        img, _ = data

        # 添加椒盐噪声
        noise = np.random.rand(*img.shape) < noise_ratio
        img[noise] = 0.0 if np.random.rand() < 0.5 else 1.0

        # 前向传播
        output = model(img)

        # 显示图片
        fig, ax = plt.subplots(1, 2)
        ax[0].imshow(np.transpose(img[0], (1, 2, 0)))
        ax[0].set_title('Original Image')
        ax[1].imshow(np.transpose(output[0], (1, 2, 0)))
        ax[1].set_title('Denoised Image')
        plt.show()
        break

在训练过程中，我们添加了椒盐噪声来模拟图像噪声。在测试过程中，我们同样添加了椒盐噪声，并将模型的输出与原始图像一起显示，以便进行比较。