卷积神经网络去雾代码

卷积神经网络去雾是一种利用深度学习技术实现图像去雾的方法。具体来说，它通过训练神经网络模型来学习图像的特征，从而实现对雾霾的自动消除。目前，许多研究人员已经开发了不少基于卷积神经网络的去雾算法，例如DehazeNet、AOD-Net等。

以下是一个基于Python的卷积神经网络去雾代码示例：

import cv2
import numpy as np
import tensorflow as tf

def build_model(input_shape):
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=input_shape))
    model.add(tf.keras.layers.MaxPooling2D((2, 2)))
    model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu', padding='same'))
    model.add(tf.keras.layers.MaxPooling2D((2, 2)))
    model.add(tf.keras.layers.Conv2D(256, (3, 3), activation='relu', padding='same'))
    model.add(tf.keras.layers.MaxPooling2D((2, 2)))
    model.add(tf.keras.layers.Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(tf.keras.layers.Conv2DTranspose(256, (3, 3), strides=(2, 2), padding='same'))
    model.add(tf.keras.layers.Conv2DTranspose(128, (3, 3), strides=(2, 2), padding='same'))
    model.add(tf.keras.layers.Conv2DTranspose(64, (3, 3), strides=(2, 2), padding='same'))
    model.add(tf.keras.layers.Conv2D(3, (3, 3), activation='sigmoid', padding='same'))
    return model

def predict(image_path):
    input_shape = (None, None, 3)
    image = cv2.imread(image_path)
    image = cv2.resize(image, (512, 512))
    image = np.expand_dims(image, axis=0)
    model = build_model(input_shape)
    model.load_weights('model.h5')
    output = model.predict(image)
    output = np.squeeze(output, axis=0)
    output = np.clip(output*255, 0, 255).astype(np.uint8)
    return output

image_path = 'input.jpg'
output_image = predict(image_path)
cv2.imwrite('output.jpg', output_image)

以上代码仅供参考，实际应用中还需根据具体情况进行适当修改。