pytorch 椒盐噪声

PyTorch是一个开源的机器学习框架，可以用于构建深度神经网络。在PyTorch中，可以使用torchvision.transforms中的RandomNoise类来添加椒盐噪声。该类可以指定噪声密度density和噪声比例ratio。噪声比例指的是添加噪声的像素点占总像素点的比例。添加椒盐噪声的代码如下：

import torch
import torchvision.transforms as transforms

# 加载图像
img = Image.open('lena.jpg')

# 定义transforms
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.RandomNoise(p=0.5, mode='salt'),
])

# 添加椒盐噪声
noisy_img = transform(img)

其中，ToTensor()将图像转换为张量，RandomNoise()添加椒盐噪声，p为噪声密度，mode为添加噪声的模式，这里选择'salt'表示添加椒盐噪声。

相关问题

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

好的，下面是一个简单的示例代码，使用PyTorch实现基于卷积神经网络的图像去噪：

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

# 加载CIFAR10数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True,
                                 transform=transforms.Compose([
                                     transforms.ToTensor(),
                                     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                                 ]))

# 添加椒盐噪声
def add_noise(img, noise_level):
    noise = torch.randn(img.size()) * noise_level
    noisy_img = img + noise
    return noisy_img

# 定义卷积神经网络模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv4 = nn.Conv2d(128, 64, 3, padding=1)
        self.conv5 = nn.Conv2d(64, 32, 3, padding=1)
        self.conv6 = nn.Conv2d(32, 3, 3, padding=1)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(2, 2)
        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = self.pool(self.relu(self.conv3(x)))
        x = self.relu(self.conv4(x))
        x = self.upsample(self.relu(self.conv5(x)))
        x = self.upsample(self.relu(self.conv6(x)))
        return x

# 训练模型
def train(model, device, train_loader, optimizer, epoch, noise_level):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        noisy_data = add_noise(data, noise_level).to(device)
        optimizer.zero_grad()
        output = model(noisy_data)
        loss = nn.MSELoss()(output, data)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 测试模型
def test(model, device, test_loader, noise_level):
    model.eval()
    test_loss = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            noisy_data = add_noise(data, noise_level).to(device)
            output = model(noisy_data)
            test_loss += nn.MSELoss()(output, data).item() # sum up batch loss

    test_loss /= len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}\n'.format(test_loss))
    return test_loss

# 设置超参数
batch_size = 128
noise_level = 0.2
lr = 0.001
momentum = 0.9
epochs = 10
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")

# 加载训练集和测试集
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True,
                                transform=transforms.Compose([
                                    transforms.ToTensor(),
                                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                                ]))
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)

# 初始化模型和优化器
model = CNN().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

# 训练模型并测试
train_loss = []
test_loss = []
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, optimizer, epoch, noise_level)
    test_loss.append(test(model, device, test_loader, noise_level))
    train_loss.append(train_loss)

上述代码中，我们定义了一个卷积神经网络模型 `CNN`，并且定义了一个添加椒盐噪声的函数 `add_noise`。在训练过程中，我们使用 `add_noise` 函数添加椒盐噪声，并且使用均方误差作为损失函数。在测试过程中，我们同样使用 `add_noise` 函数添加椒盐噪声，并且计算测试集的均方误差。最后，我们使用随机梯度下降算法进行模型训练，并且打印训练过程中的损失值和测试集的均方误差。

注意，上述代码中的模型和超参数可能需要根据实际情况进行调整，以达到更好的去噪效果。

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

### 回答1：
好的，以下是使用 PyTorch 实现基于卷积神经网络的图像去噪，噪声使用椒盐噪声的完整代码：

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

# 加载 CIFAR10 数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.CIFAR10(root='./data', train=False, transform=transforms.ToTensor(), download=True)

# 定义椒盐噪声函数
def salt_and_pepper_noise(image, noise_level=0.1):
    h, w = image.shape[1:]
    num_pixels = h * w
    noise_pixels = int(num_pixels * noise_level)
    coords = [np.random.randint(0, i - 1, noise_pixels) for i in (h, w)]
    for coord_h, coord_w in zip(*coords):
        if np.random.rand() < 0.5:
            image[:, coord_h, coord_w] = 0.0
        else:
            image[:, coord_h, coord_w] = 1.0
    return image

# 定义去噪自编码器模型
class DenoiseAutoEncoder(nn.Module):
    def __init__(self):
        super(DenoiseAutoEncoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(in_channels=32, out_channels=3, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.Sigmoid()
        )
        
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

# 训练函数
def train(model, train_loader, criterion, optimizer, device):
    model.train()
    train_loss = 0
    for data, _ in train_loader:
        data = salt_and_pepper_noise(data)  # 添加椒盐噪声
        data = data.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, data)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.size(0)
    return train_loss / len(train_loader.dataset)

# 测试函数
def test(model, test_loader, criterion, device):
    model.eval()
    test_loss = 0
    with torch.no_grad():
        for data, _ in test_loader:
            data = data.to(device)
            output = model(data)
            loss = criterion(output, data)
            test_loss += loss.item() * data.size(0)
    return test_loss / len(test_loader.dataset)

# 训练模型
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DenoiseAutoEncoder().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False)

epochs = 10
train_losses = []
test_losses = []
for epoch in range(epochs):
    train_loss = train(model, train_loader, criterion, optimizer, device)
    test_loss = test(model, test_loader, criterion, device)
    train_losses.append(train_loss)
    test_losses.append(test_loss)
    print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}")

# 展示去噪效果
fig, axs = plt.subplots(2, 10, figsize=(15, 3))
for i, (data, _) in enumerate(test_dataset):
    if i >= 10:
        break
    data = data.unsqueeze(0).to(device)
    output = model(data)
    axs[0, i].imshow(data.squeeze(0).permute(1, 2, 0).cpu().numpy())
    axs[1, i].imshow(output.squeeze(0).permute(1, 2, 0).cpu().numpy())
plt.show()

运行完毕后可以得到展示去噪效果的图像，其中第一行是加了椒盐噪声的原始图像，第二行是通过模型去噪后的结果：


### 回答2：
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 torchvision
import torchvision.transforms as transforms
import torch.optim as optim
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(3, 16, 3, padding=1)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.conv3 = nn.Conv2d(32, 3, 3, padding=1)
        
    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.conv3(x)
        return x

# 加载 CIFAR10 数据集并进行数据预处理
transform = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor()
])

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)

# 创建图像去噪模型 和 优化器
net = Net()
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):  # 迭代 10 次
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, _ = data
        
        # 添加椒盐噪声
        noise = torch.rand_like(inputs) < 0.02
        inputs[noise] = torch.rand_like(inputs[noise])
        
        optimizer.zero_grad()
        
        outputs = net(inputs)
        loss = criterion(outputs, inputs)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        if i % 200 == 199:   
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 200))
            running_loss = 0.0

print('Training finished')

# 加载测试集
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

dataiter = iter(testloader)
images, _ = dataiter.next()

# 预测去噪后的图像
outputs = net(images)
noisy_images = images + 0.02 * torch.randn_like(images)
denoised_images = outputs.detach() + noisy_images

# 显示原始图像、噪声图像和去噪后的图像
images = torchvision.utils.make_grid(images)
noisy_images = torchvision.utils.make_grid(noisy_images)
denoised_images = torchvision.utils.make_grid(denoised_images)

images = images / 2 + 0.5     # 反归一化
noisy_images = noisy_images / 2 + 0.5
denoised_images = denoised_images / 2 + 0.5

plt.imshow(np.transpose(images, (1, 2, 0)))
plt.title('Original Images')
plt.show()

plt.imshow(np.transpose(noisy_images, (1, 2, 0)))
plt.title('Noisy Images')
plt.show()

plt.imshow(np.transpose(denoised_images, (1, 2, 0)))
plt.title('Denoised Images')
plt.show()

这个代码使用 CIFAR10 数据集进行训练和测试，并添加了椒盐噪声。它定义了一个卷积神经网络模型，使用均方差损失函数进行训练，优化器使用 Adam。训练完成后，它使用测试集进行图像去噪，并显示原始图像、噪声图像和去噪后的图像。

### 回答3：
使用PyTorch基于卷积神经网络进行图像去噪的步骤如下所示：

1. 导入所需的库和模块：

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader

2. 定义CNN模型：

class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.fc1 = nn.Linear(128 * 8 * 8, 512)
self.fc2 = nn.Linear(512, 10)

def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = F.relu(self.conv3(x))
x = F.relu(self.conv4(x))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x

3. 定义训练和测试函数：

def train(model, trainloader, criterion, optimizer):
model.train()
for inputs, labels in trainloader:
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()

def test(model, testloader):
model.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in testloader:
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = 100 * correct / total
return accuracy

4. 加载CIFAR10数据集和添加噪声：

transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

def add_salt_pepper_noise(img, noise_level):
img = img.clone()
salt = torch.rand(img.size()) < noise_level / 2
pepper = torch.rand(img.size()) < noise_level / 2
img[salt] = 1.0
img[pepper] = 0.0
return img

noise_level = 0.1
for i in range(len(trainset)):
img, label = trainset[i]
img = add_salt_pepper_noise(img, noise_level)
trainset[i] = img, label

5. 实例化模型和优化器：

model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
num_epochs = 10

6. 执行训练和测试：

for epoch in range(num_epochs):
train(model, trainloader, criterion, optimizer)
acc = test(model, testloader)
print(f"Epoch [{epoch+1}/{num_epochs}], Accuracy: {acc}%")

7. 展示图片：

import matplotlib.pyplot as plt
import numpy as np

def imshow(img):
img = img / 2 + 0.5 # 从[-1,1]范围转换为[0,1]
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()

# 展示噪声图像
dataiter = iter(trainloader)
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))

# 展示去噪后的图像
outputs = model(images)
_, predicted = torch.max(outputs, 1)
imshow(torchvision.utils.make_grid(images[predicted == labels]))

通过以上步骤，我们使用PyTorch基于卷积神经网络对CIFAR10数据集中的图像进行了去噪操作，并展示了噪声图像和去噪后的图像。