Title: Spectral Image Visualization Using Generative Adversarial Networks,
Abstract: Spectral images captured by satellites and radio-telescopes are analyzed to
obtain information about geological compositions distributions, distant asters
as well as undersea terrain. Spectral images usually contain tens to hundreds
of continuous narrow spectral bands and are widely used in various fields. But
the vast majority of those image signals are beyond the visible range, which
calls for special visualization technique. The visualizations of spectral
images shall convey as much information as possible from the original signal
and facilitate image interpretation. However, most of the existing visualizatio
methods display spectral images in false colors, which contradict with human's
experience and expectation. In this paper, we present a novel visualization
generative adversarial network (GAN) to display spectral images in natural
colors. To achieve our goal, we propose a loss function which consists of an
adversarial loss and a structure loss. The adversarial loss pushes our solution
to the natural image distribution using a discriminator network that is trained
to differentiate between false-color images and natural-color images. We also
use a cycle loss as the structure constraint to guarantee structure
consistency. Experimental results show that our method is able to generate
structure-preserved and natural-looking visualizations.