INFRARED IMAGE SUPER RESOLUTION WITH DEEP NEURAL NETWORKS

Recent studies have shown that Deep Learning (DL) algorithms can significantly improve Super Resolution (SR) performance. Single image SR is useful in producing High Resolution (HR) images from their Low Resolution (LR) counterparts. The motivation for SR is the potential to assist algorithms such as object detection, localization, and classification. Insufficient work has been conducted using Generative Adversarial Networks (GANs) for SR on infrared (IR) images despite its promising ability to increase object detection accuracy by extracting more precise features from a given image. This work adopts the idea of a relativistic GAN that utilizes Residual in Residual Dense blocks (RRDBs) for feature extraction, a novel residual image addition, and a Pixel Transposed Convolutional Layer (PixelTCL) for up-sampling. Recent work has validated the use of GANs for Visible Light (VL) images, making them a strong candidate. The inclusion of these components produce more realistic and natural features while also receiving superior metric values.