Super-Resolution of UAVs Thermal Images Guided by Visible Images
Hèdi Fkih, Abdelaziz Kallel, Zied Chtourou · 2023
Thermal imaging of unmanned aerial vehicles (UAVs) can sometimes suffer from a lack of information due to their small sizes. Therefore, the ability to understand and analyze such images of drones will be limited. Nevertheless, high-resolution (HR) visible images are often available and could be useful for improving the resolution of thermal images from UAVs. In recent years, Deep learning has been increasingly used in several computer vision tasks such as super-resolution (SR), where it has shown promising results for image resolution enhancement as it allows for creating high-quality detailed images. In this paper, we propose a Guidance Super-Resolution Network (GSRNet), that improves the spatial resolution of thermal UAVs images by taking advantage of the textures of the visible images. We adapt a Convolutional Neural Network (CNN) model that has an encoder-decoder architecture to translate visible images into thermal images as well as an auto-attention mechanism to allow the network to selectively focus on relevant structures of the image while ignoring irrelevant parts. Moreover, to preserve the low frequency information such as the brightness level and the body that are present in the low-resolution (LR) thermal image, we propose to merge the letter image with the translated one, such that the obtained HR thermal image when downsampled it equals the original LR one. Experimental results on the custom UAVs image dataset prove the higher performance of the proposed model on both qualitative and quantitative evaluations when compared to several state-of-the-art (SOTA) methods.