import tensorflow as tf

Define the input and output sizes

input_size = (None, None, 3) output_size = (720, 1280, 3)

Load the dataset

train_dataset = tf.keras.preprocessing.image_dataset_from_directory( 'train_directory', batch_size=16, image_size=(256, 256), label_mode='same' )

Define the network architecture

def build_model(): # Define the input layer inputs = tf.keras.layers.Input(shape=input_size)

# Convolutional Layers
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)

# Upsampling Layers
x = tf.keras.layers.UpSampling2D(size=(2, 2))(x)

# Resampling Layers
x = tf.keras.layers.Conv2D(3, (3, 3), activation='linear', padding='same')(x)  # This layer performs the Lanczos resampling or bicubic interpolation.

# Reconstruction Layers
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = tf.keras.layers.Conv2D(3, (3, 3), activation='linear', padding='same')(x)  # This layer enhances the image quality.

# Output Layer
outputs = tf.keras.layers.experimental.preprocessing.Resizing(
height=output_size[0], 
width=output_size[1], 
interpolation='bicubic'

)(x)

# Create the model
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
return model

Build the model

model = build_model()

Compile the model

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss=tf.keras.losses.MeanSquaredError(), metrics=['accuracy'])

Train the model

history = model.fit(train_dataset, epochs=10)

Save the trained model

model.save('superresolution_model.h5')

Print the model summary

model.summary()

Print the training history

print(history.history)