AOD-NET去雾网络结合ACE自适应对比度增强代码示例

下面是将AOD-NET去雾网络与ACE自适应对比度增强结合的代码示例：

import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

# 定义AOD-NET去雾网络
class AODNet(nn.Module):
    def __init__(self):
        super(AODNet, self).__init__()
        # 定义网络结构
        self.conv1 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1)
        self.conv5 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1)
        self.conv6 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        # AOD-NET网络的前向传播
        x1 = F.relu(self.conv1(x))
        x2 = F.relu(self.conv2(x1 - x))
        x3 = F.relu(self.conv3(x2 - x1))
        x4 = F.relu(self.conv4(x - x3))
        x5 = F.relu(self.conv5(x4 - x))
        x6 = self.conv6(x5 - x4)
        return x6

# 定义ACE自适应对比度增强函数
def adaptive_contrast_enhancement(img, alpha=0.5, beta=1.0):
    # 计算图像均值和标准差
    mean = np.mean(img)
    std = np.std(img)
    # 对比度增强
    enhanced_img = alpha * (img - mean) / std + beta * img
    return enhanced_img

# 加载AOD-NET模型
model = AODNet()
model.load_state_dict(torch.load('aod_net_model.pth'))
model.eval()

# 加载输入图像
image = cv2.imread('input_image.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = np.float32(image) / 255.0

# 使用AOD-NET进行去雾
input_tensor = torch.from_numpy(image.transpose((2, 0, 1))).unsqueeze(0)
output_tensor = model(input_tensor)
dehazed_image = output_tensor.squeeze(0).detach().numpy().transpose((1, 2, 0))

# 对去雾结果进行ACE自适应对比度增强
enhanced_image = adaptive_contrast_enhancement(dehazed_image)

# 显示结果
cv2.imshow('Input Image', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
cv2.imshow('Dehazed Image', cv2.cvtColor(dehazed_image, cv2.COLOR_RGB2BGR))
cv2.imshow('Enhanced Image', cv2.cvtColor(enhanced_image, cv2.COLOR_RGB2BGR))
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，上述代码中的AOD-NET模型和权重文件应该是预先训练好的。在model.load_state_dict处，您需要将'aod_net_model.pth'替换为您自己的权重文件路径。同样，您需要将'input_image.jpg'替换为您自己的输入图像路径。