9. 使用Super-Resolution增强图像

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Input
from tensorflow.keras.models import Model

def build_super_resolution_model():
    inputs = Input(shape=(64, 64, 3))
    x = Conv2D(64, 5, padding='same', activation='relu')(inputs)
    x = Conv2D(64, 5, padding='same', activation='relu')(x)
    x = Conv2D(64, 5, padding='same', activation='relu')(x)
    x = Conv2DTranspose(64, 5, padding='same', activation='relu')(x)
    x = Conv2DTranspose(64, 5, padding='same', activation='relu')(x)
    x = Conv2D(3, 5, padding='same', activation='sigmoid')(x)
    return Model(inputs, x)

# Hyperparameters
epochs = 10
batch_size = 1

# Load dataset (example)
def load_data():
    # Placeholder function to load dataset
    return np.random.rand(10, 64, 64, 3)

# Initialize model
model = build_super_resolution_model()
model.compile(optimizer=tf.keras.optimizers.Adam(1e-4), loss='mean_squared_error')

# Training loop
for epoch in range(epochs):
    low_res_images = load_data()
    high_res_images = np.random.rand(batch_size, 256, 256, 3)  # Placeholder high-res images
    
    loss = model.train_on_batch(low_res_images, high_res_images)
    print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss}')
    
    if (epoch + 1) % 5 == 0:
        output_images = model.predict(low_res_images)
        for i in range(batch_size):
            plt.imshow(output_images[i])
            plt.axis('off')
            plt.savefig(f'super_resolution_image_{epoch+1}_{i}.png')
            plt.close()