srcnn.py

# import packages
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.core import Activation
from keras import backend as K

class SRCNN:
    @staticmethod
    def build(width, height, depth):
        # initialize the model
        model = Sequential()
        inputShape = (height, width, depth)

        # if we are using "channels_first" update the input shape
        if K.image_data_format() == "channels_first":
            inputShape = (depth, height, width)

        # the entire SRCNN architecture consists of three CONV => RELU
        # layers without any zero-padding
        model.add(Conv2D(64, (9, 9), kernel_initializer = "he_normal",
            input_shape = inputShape))
        model.add(Activation("relu"))
        model.add(Conv2D(32, (1, 1), kernel_initializer = "he_normal"))
        model.add(Activation("relu"))
        model.add(Conv2D(depth, (5, 5), kernel_initializer = "he_normal"))
        model.add(Activation("relu"))

        # return the constructed network architecture
        return model