Meta Transfer Learning-Based Super-Resolution Infrared Imaging

We propose an infrared image super-resolution method with meta transfer learning and a lightweight network. We design a lightweight network to learn the map between the low-resolution and high-resolution infrared images. We train the network with an external dataset and use meta transfer learning with internal dataset that makes the network drop to a sensitive and transferable point. We build an infrared imaging system with an infrared module. The designed network is implemented on a personal computer and the SR image is reconstructed by the trained network. Both numerical and experimental results show that the proposed method achieves the infrared image super-resolution, and the performance of the proposed method is superior to four state-of-art image super-resolution methods. The proposed method has practical application in the image super-resolution of mobile infrared devices