基于卷积神经网络的图像去噪python代码
时间: 2023-06-30 08:21:09 浏览: 159
以下是基于卷积神经网络的图像去噪的Python代码示例,使用Tensorflow和Keras框架实现:
```python
import tensorflow as tf
from tensorflow import keras
# 定义去噪卷积神经网络模型
def denoise_model():
input_img = keras.layers.Input(shape=(None, None, 1))
x = keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = keras.layers.MaxPooling2D((2, 2), padding='same')(x)
x = keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(x)
encoded = keras.layers.MaxPooling2D((2, 2), padding='same')(x)
x = keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(encoded)
x = keras.layers.UpSampling2D((2, 2))(x)
x = keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = keras.layers.UpSampling2D((2, 2))(x)
decoded = keras.layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
model = keras.models.Model(input_img, decoded)
model.compile(optimizer='adam', loss='binary_crossentropy')
return model
# 加载训练数据和测试数据
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = tf.expand_dims(x_train, axis=-1)
x_test = tf.expand_dims(x_test, axis=-1)
# 添加噪声到训练数据和测试数据
noise_factor = 0.5
x_train_noisy = x_train + noise_factor * tf.random.normal(shape=x_train.shape)
x_test_noisy = x_test + noise_factor * tf.random.normal(shape=x_test.shape)
x_train_noisy = tf.clip_by_value(x_train_noisy, clip_value_min=0., clip_value_max=1.)
x_test_noisy = tf.clip_by_value(x_test_noisy, clip_value_min=0., clip_value_max=1.)
# 训练模型
model = denoise_model()
model.fit(x_train_noisy, x_train, epochs=10, batch_size=128, shuffle=True, validation_data=(x_test_noisy, x_test))
# 使用模型对测试数据进行去噪
denoised_imgs = model.predict(x_test_noisy)
```
该代码示例实现了一个简单的去噪卷积神经网络模型,并使用MNIST数据集进行训练和测试。在测试数据上添加了高斯噪声,然后使用训练好的模型对其进行去噪。