  Signal representation in Time-Frequency (TF) domain is valuable in many
applications including radar imaging and inverse synthetic aparture radar. TF
representation allows us to identify signal components or features in a mixed
time and frequency plane. There are several well-known tools, such as
Wigner-Ville Distribution (WVD), Short-Time Fourier Transform (STFT) and
various other variants for such a purpose. The main requirement for a TF
representation tool is to give a high-resolution view of the signal such that
the signal components or features are identifiable. A commonly used method is
the reassignment process which reduces the cross-terms by artificially moving
smoothed WVD values from their actual location to the center of the gravity for
that region. In this article, we propose a novel reassignment method using the
Conditional Generative Adversarial Network (CGAN). We train a CGAN to perform
the reassignment process. Through examples, it is shown that the method
generates high-resolution TF representations which are better than the current
reassignment methods.
