LVTSR: learning visible image texture network for infrared polarization super-resolution imaging

Abstract

Infrared polarization (IRP) division-of-focal-plane (DoFP) imaging technology has gained attention, but limited resolution due to sensor size hinders its development. High-resolution visible light (VIS) images are easily obtained, making it valuable to use VIS images to enhance IRP super-resolution (SR). However, IRP DoFP SR is more challenging than infrared SR due to the need for accurate polarization reconstruction. Therefore, this paper proposes an effective multi-modal SR network, integrating high-resolution VIS image constraints for IRP DoFP image reconstruction, and incorporating polarization information as a component of the loss function to achieve end-to-end IRP SR. For the multi-modal IRP SR, a benchmark dataset was created, which includes 1559 pairs of registered images. Experiments on this dataset demonstrate that the proposed method effectively utilizes VIS images to restore polarization information in IRP images, achieving a 4x magnification. Results show superior quantitative and visual evaluations compared to other methods.