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The specific M=8 layer residual learning CNN architecture used as Dk and DI blocks in the experiments. The 4 shot complex data are the input and output of the network. The first layer concatenates the real and imaginary parts as 8 input features. The numbers on top of each layer represent the number of feature maps learned at that layer. We learn 3 × 3 filters at each layer except the last, where we learn 1 × 1 filter.
