A Lightweight Network With Latent Representations for UAV Thermal Image Super-Resolution

Yu Sang, Tong Liu, Yunan Liu, Tianjiao Ma, Simiao Wang, Xinjun Zhang, Jinguang Sun

Abstract: While thermal imaging technology on unmanned aerial vehicles (UAVs) has made significant progress, the widespread issue of insufficient resolution poses a serious challenge to comprehending the content of thermal images. Moreover, deploying super-resolution (SR) models on resource-limited UAVs presents considerable difficulties. In an effort to address these challenges, we propose a lightweight thermal image super-resolution (LTSR) model that efficiently extracts multiscale features and learns latent representations. First, we construct a multiscale knowledge distillation (MSKD) network to extract discriminative features from low-resolution (LR) inputs. To achieve this, we use convolution with varying dilation rates to extract features from diverse receptive fields and compress these features through knowledge distillation. Second, to effectively establish continuous relationships among features in the latent space, we develop a forward Markovian restoration process involving multiple diffusion iterations. In each iteration, we integrate the lightweight MSKD network and latent neural representation into a unified end-to-end framework. When evaluated on the challenging benchmark dataset, our method not only has fewer parameters but also outperforms state-of-the-art methods in SR accuracy. Extensive ablation analysis validates the effectiveness of each component in our LTSR.