Learning Spatial–Parallax Prior Based on Array Thermal Camera for Infrared Image Enhancement

Abstract

In this article, an array thermal camera equipment is developed to capture multiple infrared images with spatial and parallax information. Based on the captured images, an end-to-end method called spatial–parallax prior network (SPPN) is proposed. Specifically, we design a spatial–parallax prior block with two symmetric branches to extract spatial and parallax features in an interactive guidance manner. Then, to effectively integrate spatial and parallax features, we introduce a channel attention mechanism to enable the network to focus on and fuse the most useful information adaptively. In this way, spatial and parallax information can be fully utilized without any explicit alignment operation. Finally, considering the scarcity and poor quality of infrared training data, we leverage transfer learning to better train the network. Extensive experimental results demonstrate that the proposed SPPN consistently outperforms the current state-of-the-art methods, providing a highly effective and scalable solution for the improvement of infrared image quality.