The architecture of the model is symmetric, with n encoding units and n decoding units. The contracting path consists of 4 × 4 convolution layers with stride 2 for downsampling, each followed by batch normalization and Leaky-ReLU activation function with the slope of 0.2. The number of channels are doubled after each step. Each unit in the expansive path consists of a 4 × 4 transposed convolutional layer with stride 2 for upsampling, concatenation with the activation map of the mirroring layer in the contracting path, followed by batch normalization and ReLU activation function. The last layer of the network is a 1 × 1 convolution which is equivalent to cross-channel parametric pooling layer. We use tanh function for the last layer. The number of channels in the output layer is 3 with L * a * b * color space.
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In this work we utilize the L * a * b * color space for the colorization task. This is because L * a * b * color space contains dedicated channel to depict the brightness of the image and the color information is fully encoded in the remaining two channels. As a result, this prevents any sudden variations in both color and brightness through small perturbations in intensity values that are experienced through RGB. So the input of the network is L-channel and the output is the a * b channels. To show the image, we concatenate the L and the a * b channels.
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