请给出基于cnn的图像去雾的python代码案例

基于CNN（卷积神经网络）的图像去雾通常涉及使用深度学习模型来恢复清晰度，去除图像中的模糊和雾气效果。以下是一个简单的Python代码示例，使用Keras库构建了一个轻量级的U-Net模型来进行去雾处理。请注意，这只是一个基础框架，实际应用中可能需要大量的数据预处理、模型训练以及后处理步骤。

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, UpSampling2D, concatenate, Input, Dropout
from tensorflow.keras.models import Model

def unet_model(input_shape=(256, 256, 3)):
    inputs = Input(shape=input_shape)

    # 下采样层
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    # 中间层
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    # 上采样层
    upsample2 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv2)
    merge2 = concatenate([upsample2, conv1], axis=3)
    conv3 = Conv2D(64, (3, 3), activation='relu', padding='same')(merge2)
    conv3 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv3)

    upsample1 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv3)
    merge1 = concatenate([upsample1, conv1], axis=3)
    conv4 = Conv2D(32, (3, 3), activation='relu', padding='same')(merge1)
    conv4 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv4)

    outputs = Conv2D(3, (1, 1), activation='sigmoid')(conv4)  # 输出通道数应与输入图像通道数一致

    model = Model(inputs=[inputs], outputs=[outputs])
    return model

# 假设已经准备好的训练数据和验证数据
train_dataset, val_dataset = ...  # 加载并预处理数据

model = unet_model()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_dataset, validation_data=val_dataset, epochs=10)

# 使用模型对新图像去雾
new_image = ...  # 读取待处理的图像
dehazed_image = model.predict(new_image)