Intuitionistic Fuzzy Feature Representation Enhanced Attention for Registration and Fusion of Multipolarized Rock Thin Section Images

The intelligent processing of rock data is critical in geological exploitation, where time‐sensitive engineering demands pose significant challenges. In the context of big data, handling high‐resolution and multipolarized imaging requires efficient and adaptive frameworks. To address the shortcomings of traditional linear attention, we propose IFS‐attention, a compact mechanism integrating intuitionistic fuzzy representation and dynamic channel weighting. This approach enhances feature representation by capturing local correlations, improving attention matrix rank, and dynamically prioritizing salient features. Incorporated into transformer architectures, IFS‐attention is applied to multipolarized rock thin‐section image tasks, such as registration and fusion, achieving state‐of‐the‐art performance while preserving computational efficiency. By addressing the dual challenges of high‐resolution imaging and data‐intensive geological analysis, this research not only provides a robust solution for multipolarized imaging tasks but also establishes a foundation for integrating lightweight attention within broader computer engineering applications.