tensorflow图像去雾算法代码

TensorFlow中常用的图像去雾算法包括Retinex去雾算法和DeepDehazing深度去雾算法。以下是一个简单的TensorFlow图像去雾算法代码示例：

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import Model

# 定义Retinex去雾模型
class RetinexDehazing(Model):
    def __init__(self):
        super(RetinexDehazing, self).__init__()
        # 定义卷积层
        self.conv1 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')
        self.conv2 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')
        self.conv3 = layers.Conv2D(3, (3, 3), activation='linear', padding='same')
    
    def call(self, inputs):
        # 进行图像去雾处理
        x = self.conv1(inputs)
        x = self.conv2(x)
        x = self.conv3(x)
        return x
    
# 定义深度去雾模型
class DeepDehazing(Model):
    def __init__(self):
        super(DeepDehazing, self).__init__()
        # 定义卷积层和残差连接
        self.conv1 = layers.Conv2D(32, (3, 3), activation='relu', padding='same')
        self.conv2 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')
        self.conv3 = layers.Conv2D(3, (3, 3), activation='linear', padding='same')
    
    def call(self, inputs):
        # 进行图像去雾处理
        x = self.conv1(inputs)
        x = self.conv2(x)
        x = tf.add(x, inputs)  # 残差连接
        x = self.conv3(x)
        return x
    
# 读取输入图像
input_image = tf.io.read_file('input.jpg')
input_image = tf.image.decode_jpeg(input_image, channels=3)
input_image = tf.image.convert_image_dtype(input_image, tf.float32)
input_image = tf.expand_dims(input_image, 0)

# 使用Retinex去雾模型
retinex_model = RetinexDehazing()
output_image_retinex = retinex_model(input_image)

# 使用DeepDehazing去雾模型
deep_model = DeepDehazing()
output_image_deep = deep_model(input_image)

# 显示处理后的图像
import matplotlib.pyplot as plt
plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.title('Retinex Dehazing')
plt.imshow(output_image_retinex[0].numpy())
plt.axis('off')
plt.subplot(1, 2, 2)
plt.title('Deep Dehazing')
plt.imshow(output_image_deep[0].numpy())
plt.axis('off')
plt.show()