Li Chen

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.

The fusion of multi-polarized petrographic images of rock thin sections involves the fusion of feature information from microscopic images of rock thin sections illuminated under both plane-polarized and orthogonal-polarized light. During the fusion process of rock thin section images, the inherent high resolution and abundant feature information of the images pose substantial challenges in terms of computational complexity when dealing with massive datasets. In engineering applications, to ensure the quality of image fusion while meeting the practical requirements for high-speed processing, this paper proposes a novel fast fusion Transformer. The model leverages a soft matching algorithm based on intuitionistic fuzzy sets to merge redundant tokens, effectively mitigating the negative effects of asymmetric dependencies between tokens. The newly generated artificial tokens serve as brokers for the Query (Q), forming a novel lightweight fusion strategy. Both subjective visual observations and quantitative analyses demonstrate that the Transformer proposed in this paper is comparable to existing fusion methods in terms of performance while achieving a notable enhancement in its inference efficiency. This is made possible by the attention paradigm, which is equivalent to a generalized form of linear attention, and the newly designed loss function. The model has been experimented on with multiple datasets of different rock types and has exhibited robust generalization capabilities. It provides potential for future research in diverse geological conditions and broader application scenarios.