Deep generative adversarial networks for infrared image enhancement

Extracting face images at a distance, in the crowd, or with a lower resolution infrared camera leads to a poor-quality face image that is barely distinguishable. In this work, we present a Deep Convolutional Generative Adversarial Networks (DCGAN) for infrared face image enhancement. The proposed algorithm is used to build a super-resolution face image from its lower resolution counterpart. The resulting images are evaluated in term of qualitative and quantitative metrics on infrared face datasets (NIR and LWIR). The proposed algorithm performs well and preserves important details of the face. The analysis of the resulting images show that the proposed framework is promising and can help improve the performance of image super-resolution generation and enhancement in the infrared spectrum.