Diff-GTISR: Guided Thermal Image Super-Resolution via Diffusion Model and Refinement

This paper presents Diff-GTISR, a novel diffusion-based model for achieving super-resolution in thermal images guided by a high-resolution visible image. Thermal sensors are widely used in surveillance, safety, and industrial inspection; however, their limited spatial resolution constrains thermal image quality because of the low resolution. Thermal image super-resolution is thus critical to compensate for this limitation. The increasing prevalence of multisensor platforms has resulted in the availability of high-resolution visible images, providing effective guidance to enhance thermal image resolution. Recently, diffusion-based super-resolution has demonstrated strong capability in recovering perceptually plausible details; however, such models often underperform in distortion-oriented metrics compared with transformer-based approaches. To address this gap, the proposed Diff-GTISR method employs a modality-specific dual encoder to extract multiscale features and a cross-modal guidance attention module to transfer structural information from visible images into low-resolution thermal images. Also, a refinement network is employed to improve the method further. The experimental results indicate that Diff-GTISR consistently enhances perceptual quality in comparison to state-of-the-art diffusion-based methods. Furthermore, it is superior to transformer-based methods in terms of distortion performance.