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 low-resolution and high-resolution infrared images. We train the network with an external dataset and use meta-transfer learning with an 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. The main contribution of this paper is to adopt a lightweight network and meta-transfer learning method, which obtains infrared super-resolution images with better visual effects. 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.