NIR to RGB Domain Translation Using Asymmetric Cycle Generative Adversarial Networks

Abstract

Near infrared (NIR) images have clear textures but do not contain color. In this paper, we propose NIR to RGB domain translation using asymmetric cycle generative adversarial networks (ACGANs). The RGB image (3 channels) has richer information than the NIR image (1 channel), which makes NIR-RGB domain translation asymmetric in information. We adopt asymmetric cycle GANs that have different network capacities according to the translation direction. We combine UNet and ResNet in generator and use the feature pyramid networks (FPNs) in discriminator. With the help of a $128\times128$ large receptive field, we capture rich spatial context information with a multiscale architecture. Experimental results show that the proposed method achieves natural looking NIR colorization results with high generalization ability, i.e. feasible in category unaware cases, and outperforms state-of-the-art ones in realistic colorization and resistance to unregistration.