Unsupervised Super-Resolution for UAV Thermal Imagery via Diffusion Models with Emissivity-Guided Texture Transfer.
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| Title: | Unsupervised Super-Resolution for UAV Thermal Imagery via Diffusion Models with Emissivity-Guided Texture Transfer. |
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| Authors: | Liu, Dong1 (AUTHOR), Sun, Min1 (AUTHOR) sunmin@pku.edu.cn, Wang, Xinyi1 (AUTHOR), Ke, Kelly Chen1 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p815. 39p. |
| Subjects: | Spatial resolution, Image processing, Probabilistic generative models, Feature extraction, Infrared imaging, Infrared cameras |
| Abstract: | Highlights: What are the main findings? An unsupervised super-resolution method is proposed that eliminates the need for supervision from high-resolution thermal infrared ground-truth images, integrating diffusion models with visible texture transfer to enhance the generalization capability of the model. A thermal emissivity-guided strategy is proposed to mitigate the impact of visible texture transfer on the temperature information of thermal infrared images, thereby keeping as much temperature fidelity as possible in super-resolved images. What are the implications of the main findings? This method frees the thermal infrared super-resolution process from its dependence on high-resolution ground-truth images, reduces the entry barrier of thermal infrared super-resolution technology, and enhances the applicability of the model. The balanced strategy for temperature consistency and detail enhancement expands the practical value of thermal images in heat-sensitive fields and provides support for precise temperature analysis scenarios, such as public security, emergency rescue, and building maintenance. Due to hardware limitations of Thermal InfraRed (TIR) cameras, TIR images captured by Unmanned Aerial Vehicles (UAVs) suffer from Low Resolutions (LRs) and blurred textures. Improving the spatial resolution of TIR images is of great significance for subsequent applications. Existing image Super-Resolution (SR) methods rely on High-Resolution (HR) ground truth for supervised training, resulting in limited generalization and a lack of mechanisms to preserve the physical consistency of thermal radiation. To address these two issues, this paper proposes an unsupervised super-resolution framework for UAV TIR imagery that integrates diffusion modeling with cross-modal texture transfer. The diffusion model enables stable reconstruction of the fundamental TIR structure without requiring high-resolution supervision, while multi-scale textures extracted from visible (VIS) imagery via Multi-Stage Decomposition based on Latent Low-Rank Representation (MS-DLatLRR) compensate for missing details. To suppress temperature distortions introduced by cross-modal texture transfer, a physics-guided constraint termed Prior-Informed Emissivity-Guided Coefficient Mapping (PI-EGCM) is incorporated. Emissivity-aware guidance maps constructed via semantic classification regulate texture transfer and preserve thermal radiation consistency. Experimental results demonstrate that the proposed method improves spatial resolution and perceptual quality while effectively maintaining temperature fidelity, achieving a balanced enhancement of structural detail and physical consistency. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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