基于对抗神经网络的图像超分辨率算法代码

以下是基于对抗神经网络的图像超分辨率算法代码的一个简单实现：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, LeakyReLU, BatchNormalization, Input, Add
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

# 定义超分辨率生成器
def create_generator():
    # 输入张量
    input_img = Input(shape=(None, None, 3))

    # 编码器
    x = Conv2D(64, 3, padding='same')(input_img)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(64, 3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(128, 3, padding='same')(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(128, 3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)

    # 解码器
    x = Conv2DTranspose(128, 3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2DTranspose(64, 3, padding='same')(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2DTranspose(64, 3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    output_img = Conv2DTranspose(3, 3, padding='same')(x)

    # 定义模型
    model = Model(inputs=input_img, outputs=output_img)
    return model

# 定义超分辨率判别器
def create_discriminator():
    # 输入张量
    input_img = Input(shape=(None, None, 3))
    target_img = Input(shape=(None, None, 3))

    # 将输入和目标图像连接在一起
    x = tf.keras.layers.concatenate([input_img, target_img])

    # 判别器网络结构
    x = Conv2D(64, 3, padding='same')(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(64, 3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(128, 3, padding='same')(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(128, 3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(256, 3, padding='same')(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(256, 3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(512, 3, padding='same')(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(512, 3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)

    x = tf.keras.layers.Flatten()(x)
    x = tf.keras.layers.Dense(1)(x)

    # 定义模型
    model = Model(inputs=[input_img, target_img], outputs=x)
    return model

# 定义GAN模型
def create_gan(generator, discriminator):
    # 判别器不需要训练
    discriminator.trainable = False

    # 输入和输出张量
    input_img = Input(shape=(None, None, 3))
    target_img = Input(shape=(None, None, 3))

    # 生成高分辨率图像
    gen_output = generator(input_img)

    # 判别器判断生成的高分辨率图像
    gan_output = discriminator([gen_output, target_img])

    # 定义GAN模型
    gan_model = Model(inputs=[input_img, target_img], outputs=[gen_output, gan_output])
    return gan_model

# 加载数据集
def load_data():
    # TODO: 加载数据集

    return X_train, y_train

# 训练模型
def train():
    # 加载数据集
    X_train, y_train = load_data()

    # 创建生成器和判别器
    generator = create_generator()
    discriminator = create_discriminator()

    # 创建GAN模型
    gan = create_gan(generator, discriminator)

    # 设置优化器
    optimizer = Adam(lr=0.0002, beta_1=0.5)

    # 编译判别器
    discriminator.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

    # 编译GAN
    gan.compile(loss=['mse', 'binary_crossentropy'], optimizer=optimizer)

    # 训练模型
    epochs = 100
    batch_size = 16
    steps_per_epoch = int(len(X_train)/batch_size)
    for epoch in range(epochs):
        for step in range(steps_per_epoch):
            # 随机选择一批数据
            index = np.random.randint(0, len(X_train), batch_size)
            real_images = y_train[index]
            input_images = X_train[index]

            # 生成高分辨率图像
            fake_images = generator.predict(input_images)

            # 训练判别器
            real_labels = np.ones((batch_size, 1))
            fake_labels = np.zeros((batch_size, 1))
            d_loss_real = discriminator.train_on_batch([real_images, input_images], real_labels)
            d_loss_fake = discriminator.train_on_batch([fake_images, input_images], fake_labels)
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

            # 训练生成器
            gan_labels = np.ones((batch_size, 1))
            g_loss = gan.train_on_batch([input_images, real_images], [fake_images, gan_labels])

        # 展示训练结果
        print("Epoch:", epoch, "D Loss:", d_loss[0], "G Loss:", g_loss[0])

        # 保存生成器模型
        if epoch % 10 == 0:
            generator.save("generator.h5")

# 测试模型
def test():
    # 加载数据集
    X_test, y_test = load_data()

    # 加载生成器模型
    generator = tf.keras.models.load_model("generator.h5")

    # 随机选择一张测试图片
    index = np.random.randint(0, len(X_test))
    input_image = X_test[index]
    target_image = y_test[index]

    # 生成高分辨率图像
    gen_output = generator.predict(input_image.reshape((1,) + input_image.shape))

    # 绘制图像
    fig, axes = plt.subplots(1, 3, figsize=(15, 5))
    axes[0].imshow(input_image)
    axes[0].set_title('Input Image')
    axes[1].imshow(target_image)
    axes[1].set_title('Target Image')
    axes[2].imshow(gen_output[0])
    axes[2].set_title('Generated Image')
    plt.show()

# 训练模型
train()

# 测试模型
test()

这段代码实现了一个简单的基于对抗神经网络的图像超分辨率算法，使用了TensorFlow作为编程框架。在代码中，我们首先定义了一个超分辨率生成器和一个判别器，然后将它们作为输入传递给一个GAN模型，最后使用训练数据对GAN模型进行训练。在训练过程中，我们随机选择一批数据，生成高分辨率图像，并将它们与真实高分辨率图像一起输入到判别器中进行训练。同时，我们还将生成器训练为产生更接近真实高分辨率图像的输出。在训练完成后，我们可以使用训练好的生成器模型来生成高分辨率图像，并与真实高分辨率图像进行比较。