  Generative Adversarial Networks (GANs) have been employed with certain
success for image translation tasks between optical and real-valued SAR
intensity imagery. Applications include aiding interpretability of SAR scenes
with their optical counterparts by artificial patch generation and automatic
SAR-optical scene matching. The synthesis of artificial complex-valued InSAR
image stacks asks for, besides good perceptual quality, more stringent quality
metrics like phase noise and phase coherence. This paper provides a signal
processing model of generative CNN structures, describes effects influencing
those quality metrics and presents a mapping scheme of complex-valued data to
given CNN structures based on popular Deep Learning frameworks.
