卷积神经网络去噪代码

根据提供的引用内容，以下是一个使用深度卷积神经网络（DnCNN）进行图像去噪的代码示例：

import torch
import torch.nn as nn

# 定义DnCNN模型
class DnCNN(nn.Module):
    def __init__(self, num_layers=17, num_channels=64):
        super(DnCNN, self).__init__()
        layers = []
        layers.append(nn.Conv2d(1, num_channels, kernel_size=3, padding=1))
        layers.append(nn.ReLU(inplace=True))
        for _ in range(num_layers - 2):
            layers.append(nn.Conv2d(num_channels, num_channels, kernel_size=3, padding=1))
            layers.append(nn.BatchNorm2d(num_channels))
            layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Conv2d(num_channels, 1, kernel_size=3, padding=1))
        self.dncnn = nn.Sequential(*layers)

    def forward(self, x):
        out = self.dncnn(x)
        return out

# 加载模型
model = DnCNN()
model.load_state_dict(torch.load('dncnn_model.pth'))

# 图像去噪
def denoise_image(image):
    image = torch.unsqueeze(torch.Tensor(image), 0)
    denoised_image = model(image)
    denoised_image = denoised_image.squeeze().detach().numpy()
    return denoised_image

请注意，上述代码仅为示例，实际使用时需要根据具体的数据集和需求进行适当的修改和调整。