DnCnn代码讲解详细步骤‘

DnCNN（Denoising Convolutional Neural Network）是一种用于图像去噪的深度学习模型，它采用了卷积神经网络结构，可以自动学习图像中的噪声分布，从而将噪声去除。

下面是DnCNN的代码讲解详细步骤：

1.导入必要的库和模块

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

2.定义DnCNN模型

class DnCNN(nn.Module):
    def __init__(self):
        super(DnCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.conv_layers = nn.Sequential(*[
            nn.Conv2d(64, 64, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 3, kernel_size=3, padding=1, bias=False)
        ])

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv_layers(out)
        return out

DnCNN模型的输入是带有噪声的图像，输出是去噪后的图像。

3.定义损失函数和优化器

criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

DnCNN模型的损失函数采用均方误差（MSE）损失函数，优化器采用Adam优化器。

4.训练DnCNN模型

for epoch in range(num_epochs):
    for i, (images, _) in enumerate(train_loader):
        images = images.cuda()
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, images)
        loss.backward()
        optimizer.step()
        if (i+1) % 10 == 0:
            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

训练DnCNN模型的过程中，首先将输入数据送入模型进行前向传播，计算模型输出和真实标签之间的损失，然后进行反向传播，更新模型参数。

5.测试DnCNN模型

model.eval()
with torch.no_grad():
    for images, _ in test_loader:
        images = images.cuda()
        outputs = model(images)
        outputs = outputs.cpu()
        outputs = outputs.detach().numpy()
        outputs = np.transpose(outputs, (0, 2, 3, 1))
        for j in range(batch_size):
            img = outputs[j,:,:,:]
            img = np.clip(img, 0, 1)
            plt.imshow(img)
            plt.show()

在测试DnCNN模型时，将模型设置为评估模式（eval），不进行梯度计算，直接将测试集送入模型进行前向传播，得到模型输出，将输出转化为numpy数组，并进行后处理，最终输出去噪后的图像。