Datasets:
HenryAI
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KerasCodeExamples.txt

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We prepare a dataset of test image paths that we will use for visual evaluation at the end of this example.
dataset = os.path.join(root_dir, \"images\")
test_path = os.path.join(dataset, \"test\")
test_img_paths = sorted(
[
os.path.join(test_path, fname)
for fname in os.listdir(test_path)
if fname.endswith(\".jpg\")
]
)
Crop and resize images
Let's process image data. First, we convert our images from the RGB color space to the YUV colour space.
For the input data (low-resolution images), we crop the image, retrieve the y channel (luninance), and resize it with the area method (use BICUBIC if you use PIL). We only consider the luminance channel in the YUV color space because humans are more sensitive to luminance change.
For the target data (high-resolution images), we just crop the image and retrieve the y channel.
# Use TF Ops to process.
def process_input(input, input_size, upscale_factor):
input = tf.image.rgb_to_yuv(input)
last_dimension_axis = len(input.shape) - 1
y, u, v = tf.split(input, 3, axis=last_dimension_axis)
return tf.image.resize(y, [input_size, input_size], method=\"area\")
def process_target(input):
input = tf.image.rgb_to_yuv(input)
last_dimension_axis = len(input.shape) - 1
y, u, v = tf.split(input, 3, axis=last_dimension_axis)
return y
train_ds = train_ds.map(
lambda x: (process_input(x, input_size, upscale_factor), process_target(x))
)
train_ds = train_ds.prefetch(buffer_size=32)
valid_ds = valid_ds.map(
lambda x: (process_input(x, input_size, upscale_factor), process_target(x))
)
valid_ds = valid_ds.prefetch(buffer_size=32)
Let's take a look at the input and target data.
for batch in train_ds.take(1):
for img in batch[0]:
display(array_to_img(img))
for img in batch[1]:
display(array_to_img(img))
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Build a model
Compared to the paper, we add one more layer and we use the relu activation function instead of tanh. It achieves better performance even though we train the model for fewer epochs.