Physics-Constrained Cross-Resolution Enhancement Network for Optics-Guided Thermal UAV Image Super-Resolution

2026-01-07

arXiv.org

AI Summary

This work addresses the limitations of existing optical-guided thermal super-resolution methods, which often suffer from physically inconsistent artifacts—such as high-frequency detail loss and edge blurring—due to excessive feature compression and neglect of the inherent physical discrepancies between imaging modalities. To mitigate these issues, we propose a Cross-Resolution Mutual Enhancement (CRME) module coupled with a physics-driven guidance mechanism (PDTM) grounded in the two-dimensional heat conduction equation, along with a temperature consistency loss. This integrated framework jointly optimizes cross-modal super-resolution reconstruction while ensuring adherence to realistic thermal radiation principles. Extensive experiments on the VGTSR2.0 and DroneVehicle datasets demonstrate that our approach significantly outperforms state-of-the-art methods not only in reconstruction fidelity but also in downstream tasks including semantic segmentation and object detection.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Optics-guided thermal UAV image super-resolution has attracted significant research interest due to its potential in all-weather monitoring applications. However, existing methods typically compress optical features to match thermal feature dimensions for cross-modal alignment and fusion, which not only causes the loss of high-frequency information that is beneficial for thermal super-resolution, but also introduces physically inconsistent artifacts such as texture distortions and edge blurring by overlooking differences in the imaging physics between modalities. To address these challenges, we propose PCNet to achieve cross-resolution mutual enhancement between optical and thermal modalities, while physically constraining the optical guidance process via thermal conduction to enable robust thermal UAV image super-resolution. In particular, we design a Cross-Resolution Mutual Enhancement Module (CRME) to jointly optimize thermal image super-resolution and optical-to-thermal modality conversion, facilitating effective bidirectional feature interaction across resolutions while preserving high-frequency optical priors. Moreover, we propose a Physics-Driven Thermal Conduction Module (PDTM) that incorporates two-dimensional heat conduction into optical guidance, modeling spatially-varying heat conduction properties to prevent inconsistent artifacts. In addition, we introduce a temperature consistency loss that enforces regional distribution consistency and boundary gradient smoothness to ensure generated thermal images align with real-world thermal radiation principles. Extensive experiments on VGTSR2.0 and DroneVehicle datasets demonstrate that PCNet significantly outperforms state-of-the-art methods on both reconstruction quality and downstream tasks including semantic segmentation and object detection.

Problem

• thermal super-resolution
• cross-modal fusion
• imaging physics inconsistency
• high-frequency information loss
• artifact generation

Innovation

• cross-resolution mutual enhancement
• physics-constrained guidance
• thermal conduction modeling
• optics-thermal fusion
• temperature consistency loss