基于深度学习的图像修复代码

基于深度学习的图像修复通常涉及到使用神经网络模型来恢复损坏、缺失或低分辨率的图像。这类任务常用于去噪、图像增强、老照片修复等场景。常见的深度学习模型有Pix2Pix、CycleGAN、U-Net等。以下是简单介绍一种基于深度学习的图像修复代码示例，以U-Net为例：

# 导入必要的库
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, UpSampling2D, Concatenate

# 定义U-Net结构
def unet_model(input_shape):
    inputs = Input(shape=input_shape)
    
    # 编码部分
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D((2, 2))(conv1)
    
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D((2, 2))(conv2)
    
    # ...继续添加更多的卷积层和池化层
    
    # 底部编码层
    conv_encoder = conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
    conv_encoder = Conv2D(512, (3, 3), activation='relu', padding='same')(conv_encoder)

    # 解码部分
    up6 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv_encoder)
    merge6 = Concatenate()([up6, conv5])
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(merge6)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)

    # 同样的步骤，逐步上采样并合并特征图，直到达到原始图像尺寸

    # 输出层
    output = Conv2D(1, (1, 1), activation='sigmoid')(conv6)  # 对于二值修复任务，可能需要Sigmoid激活

    return tf.keras.Model(inputs=inputs, outputs=output)