import tensorflow as tf

Define the input and output sizes

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

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()

Load the dataset

inputs = tf.placeholder(tf.float32, [None, None, None, 3]) outputs = tf.placeholder(tf.float32, [None, 720, 1280, 3])

Load the data

train_data = ... test_data = ...

Create the optimizer

optimizer = tf.keras.optimizers.Adam()

Define the loss function

loss_fn = tf.keras.losses.MeanSquaredError()

Define the metrics

metrics = ['mean_squared_error']

Compile the model

model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)

Train the model

model.fit(train_data, test_data, epochs=10)

Save the trained model

model.save('superresolution_model.h5')

Print the model summary

model.summary()