Convolutional Neural Network-Based Infrared Image Super Resolution Under Low Light Environment

Convolutional neural networks (CNN) have been successfully applied to visible image super-resolution (SR) methods. In this paper, for up-scaling near-infrared (NIR) image under low light environment, we propose a CNN-based SR algorithm using corresponding visible image. Our algorithm firstly extracts high-frequency (HF) components from low-resolution (LR) NIR image and its corresponding high-resolution (HR) visible image, and then takes them as the multiple inputs of the CNN. Next, the CNN outputs HR HF component of the input NIR image. Finally, HR NIR image is synthesized by adding the HR HF component to the up-scaled LR NIR image. Simulation results show that the proposed algorithm outperforms the state-of- the-art methods in terms of qualitative as well as quantitative metrics.