基于卷积神经网络的图像修复系统设计与实现举例代码
时间: 2023-10-09 16:06:45 浏览: 72
以下是一个基于卷积神经网络的图像修复系统的样例代码,包括训练和测试代码:
训练代码:
```python
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
# 定义卷积神经网络模型
def create_model(input_shape):
input_layer = Input(shape=input_shape)
# 编码器部分
x = Conv2D(32, (3, 3), padding='same', activation='relu')(input_layer)
x = Conv2D(32, (3, 3), padding='same', activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = Conv2D(64, (3, 3), padding='same', activation='relu')(x)
x = Conv2D(64, (3, 3), padding='same', activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = Conv2D(128, (3, 3), padding='same', activation='relu')(x)
x = Conv2D(128, (3, 3), padding='same', activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
# 解码器部分
x = Conv2D(128, (3, 3), padding='same', activation='relu')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(64, (3, 3), padding='same', activation='relu')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), padding='same', activation='relu')(x)
x = UpSampling2D((2, 2))(x)
output_layer = Conv2D(3, (3, 3), padding='same', activation='sigmoid')(x)
model = Model(input_layer, output_layer)
model.compile(optimizer='adam', loss='binary_crossentropy')
return model
# 加载训练数据
train_images = load_train_images()
# 创建模型
model = create_model(train_images[0].shape)
# 训练模型
model.fit(train_images, train_images, batch_size=32, epochs=100)
# 保存模型
model.save('image_restoration_model.h5')
```
测试代码:
```python
import tensorflow as tf
from tensorflow.keras.models import load_model
# 加载测试图片
test_image = load_test_image()
# 加载模型
model = load_model('image_restoration_model.h5')
# 进行预测
restored_image = model.predict(test_image)
# 显示结果
show_image(test_image, 'Original Image')
show_image(restored_image, 'Restored Image')
```
需要注意的是,这只是一个简单的示例,实际上需要根据具体情况进行调整和优化。
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