Infrared thermal imaging presents significant potential in various domains. However, the widespread development of this technology is hindered by the high cost associated with acquiring high-quality thermal imaging sensors. To overcome this challenge, super-resolution techniques have emerged as a viable solution for extracting valuable information from low-resolution thermal images. While generative adversarial networks (GANs) have been widely adopted for thermal imaging super-resolution, their performance is limited by the inherent lack of detail in low-resolution training images, resulting in reduced fidelity and accuracy in generating high-resolution reconstructions. To tackle this challenge, this letter introduces FEGAN, a novel approach that enhances the performance of GANs by incorporating a feature-oriented enhanced (FE) mechanism within the generative network (GN). The FE plays a pivotal role in extracting high-frequency texture and edge details from lowresolution inputs and reconstructing them into enhanced images. This process substantially improves textures and edges within the training set of thermal images. Furthermore, refinements have been applied to both the GN and the discriminative network (DN) to enhance feature extraction efficiency. The experimental findings unequivocally demonstrate the superior performance of FEGAN compared to state-of-the-art methods. FEGAN achieves impressive performance metrics, including PSNR of 27.18, SSIM of 0.6523, FSIM of 0.5500, and LPIPS of 0.1221, highlighting its remarkable capabilities in the realm of thermal image superresolution.

This study aimed to achieve the accurate and real-time geographic positioning of UAV aerial image targets. We verified a method of registering UAV camera images on a map (with the geographic location) through feature matching. The UAV is usually in rapid motion and involves changes in the camera head, and the map is high-resolution and has sparse features. These reasons make it difficult for the current feature-matching algorithm to accurately register the two (camera image and map) in real time, meaning that there will be a large number of mismatches. To solve this problem, we used the SuperGlue algorithm, which has a better performance, to match the features. The layer and block strategy, combined with the prior data of the UAV, was introduced to improve the accuracy and speed of feature matching, and the matching information obtained between frames was introduced to solve the problem of uneven registration. Here, we propose the concept of updating map features with UAV image features to enhance the robustness and applicability of UAV aerial image and map registration. After numerous experiments, it was proved that the proposed method is feasible and can adapt to the changes in the camera head, environment, etc. The UAV aerial image is stably and accurately registered on the map, and the frame rate reaches 12 frames per second, which provides a basis for the geo-positioning of UAV aerial image targets.

Indoor high-precision maps are harder to obtain. Numerous researchers considered the extendibility of indoor plane features to constrain the poses and improve mapping consistency. However, the usage of parametric planar or line features requires complex modeling, and threshold-based matching methods are eager to lead to degradation. Indeed, planes can be more simply represented as line projections, with the projection direction perpendicular to the plane’s normal vector. In this article, we proposed a novel mapping optimization framework that incorporates building outline features specifically designed for multistory buildings. The framework consists of three main components: LiDAR bundle adjustment (LBA), global constraints based on building outline features, and factor graph optimization. The method extracts the ground only for horizontal correction, while the core idea is based on the assumption of vertical structure, projecting the point cloud contours of multistory buildings to the top view perspective for independent spatial pose correlation, which is a strong constraint and very effective. Finally, constraints are constructed based on keyframes and intermediate frames are refined by a factor graph. Through extensive experiments conducted on multistory buildings, we demonstrate that our algorithm significantly enhances mapping consistency and local accuracy compared to existing methods.