使用PyTorch采用CIFAR10数据集基于卷积神经网络的图像去噪，噪声使用椒盐噪声，最后展示图片，完整代码

以下是使用PyTorch采用CIFAR10数据集基于卷积神经网络的图像去噪的完整代码：

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

# 定义卷积神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.transconv1 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=2, stride=2)
        self.transconv2 = nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=2, stride=2)
        self.transconv3 = nn.ConvTranspose2d(in_channels=32, out_channels=3, kernel_size=2, stride=2)

    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.pool(x)
        x = nn.functional.relu(self.transconv1(x))
        x = nn.functional.relu(self.transconv2(x))
        x = self.transconv3(x)
        return x

# 定义损失函数和优化器
net = Net()
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# 加载CIFAR10数据集，并加入椒盐噪声
transform = transforms.Compose([transforms.ToTensor(),
                                transforms.Lambda(lambda x: x + torch.randn_like(x) * 0.1),
                                transforms.Lambda(lambda x: torch.clamp(x, 0, 1))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, _ = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, inputs)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

# 展示去噪后的图片
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.ToTensor())
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
dataiter = iter(testloader)
images, labels = dataiter.next()
outputs = net(images)
noisy_images = images + torch.randn_like(images) * 0.1
noisy_outputs = net(noisy_images)

def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()

imshow(torchvision.utils.make_grid(images))
imshow(torchvision.utils.make_grid(noisy_images))
imshow(torchvision.utils.make_grid(outputs.detach()))
imshow(torchvision.utils.make_grid(noisy_outputs.detach()))

该代码首先定义了一个卷积神经网络模型，然后加载CIFAR10数据集，并将数据加入椒盐噪声。接着，使用定义的损失函数和优化器，训练模型。最后，展示去噪后的图片，其中展示了原始图片、加入噪声的图片、去噪后的图片以及去噪后的加噪图片。