  Contrary to optical images, Synthetic Aperture Radar (SAR) images are in
different electromagnetic spectrum where the human visual system is not
accustomed to. Thus, with more and more SAR applications, the demand for
enhanced high-quality SAR images has increased considerably. However,
high-quality SAR images entail high costs due to the limitations of current SAR
devices and their image processing resources. To improve the quality of SAR
images and to reduce the costs of their generation, we propose a Dialectical
Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR
images. This method is based on the analysis of hierarchical SAR information
and the "dialectical" structure of GAN frameworks. As a demonstration, a
typical example will be shown where a low-resolution SAR image (e.g., a
Sentinel-1 image) with large ground coverage is translated into a
high-resolution SAR image (e.g., a TerraSAR-X image). Three traditional
algorithms are compared, and a new algorithm is proposed based on a network
framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial
Network - Gradient Penalty) loss functions and Spatial Gram matrices under the
rule of dialectics. Experimental results show that the SAR image translation
works very well when we compare the results of our proposed method with the
selected traditional methods.
