TVA-GAN: Attention Guided Generative Adversarial Network For Thermal To
Visible Face Transformations
Abstract
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.