TVA_GAN (3).pdf (11.84 MB)
Download fileTVA-GAN: Attention Guided Generative Adversarial Network For Thermal To Visible Image Transformations
In the recent advancement of machine learning
methods for realistic image generation and image translation,
Generative Adversarial Networks (GANs) play a vital role. GAN
generates novel samples that look indistinguishable from the real
images. The image translation using a generative adversarial
network refers to unsupervised learning. In this paper, we
translate the thermal images into visible images. Thermal to
Visible image translation is challenging due to the non-availability
of accurate semantic information and smooth textures. The
thermal images contain only single-channel, holding only the
images’ luminance with less feature. We develop a new Cyclic
Attention-based Generative Adversarial Network for Thermal
to Visible Face transformation (TVA-GAN) by incorporating a
new attention-based network. We use attention guidance with
a recurrent block through an Inception module to reduce the
learning space towards the optimum solution.