This is the implementation of an ECCV'18 paper, 'Image Super-Resolution Using Very Deep Residual channel Attention Networks' in Tensorflow (link).

For this implementation, I designed new layers and a model by subclassing, which was a good practice of this tensorflow code guide on 'Making new Layers and Models via subclassing'. (link)

• Language: Python3
• Required Packages: numpy, cv2 (for read image input), and tensorflow
• To install the required packages, type the following command:

1. Using tensorflow-gpu==2.0.0

pip3 install numpy opencv-python tensorflow-gpu==2.0.0

2. Using higher version of tensorflow

pip3 install numpy opencv-python tensorflow

By subclassing layers and models, we can easily design RCAN model simply specifying a few parameters.

• Parameters for CARN model:

1. initial_num_filters: represents the number of filters for the inital convolution layer
2. num_group: represents the ResGroup
3. num_block: represents the number of resnet blocks in a ResGroup
4. kernel_size: represents the number of convolution layer in a resnet block
5. scale_factor: represnets the scale factor in a resnet block
6. up_scale_factor: represents the upscale factor
7. reduction_rate: represents the reduction rate in a channel attention block
8. squeeze_factor: represent the channel squeeze factor in a upsample block

• How to create the RCAN model

#DEFINE THE PARAMETERS
initial_num_filters = 64
num_group = 3
num_block = 3
kernel_size = 3
scale_factor = 0.1
up_scale_factor = 2
reduction_rate = 3
squeeze_factor = 2

#LOAD THE LOW RESOLUTION AND HIGH RESOLUTION IMAGE DATA SETS
data = cv2.imread('img.jpg').astype(float)
small_data = cv2.resize(data, None, fx = 1/up_scale_factor, fy = 1/up_scale_factor)

#CREATE AN RCAN MODEL
model = RCAN(initial_num_filters, num_group, num_block, kernel_size, scale_factor, up_scale_factor, reduction_rate, squeeze_factor)

optimizer = tf.keras.optimizers.Adam(learning_rate = 1e-4)
model.compile(optimizer = optimizer, loss = tf.keras.losses.MeanAbsoluteError())

#TRAIN
model.fit(small_data[np.newaxis, ...], data[np.newaxis, ...], epochs = 100)
model.build_graph().summary()
tf.keras.utils.plot_model(model.build_graph(), show_shapes=True, dpi=256)

#PREDICTION
result = model.predict(small_data[np.newaxis, ...])
result = tf.cast(result, tf.uint8)

cv2.imshow('img', tf.squeeze(result).numpy())
cv2.waitKey(0)
'''

• Model Structure