Integrated Multimodal Image Fusion and Deep Learning for 3D Reconstruction and Intelligent Quantitative Analysis of Wood Microstructure

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Abstract

High-precision three-dimensional (3D) reconstruction and quantitative analysis of wood microstructure are critical in the fields of wood science and materials characterization, with significant academic value and practical applications in wood property improvement and biomass material development.Conventional single-modality imaging techniques have inherent limitations: Micro-Computed Tomography (Micro-CT) can provide 3D spatial structural information but suffers from insufficient axial resolution, whereas Scanning Electron Microscope (SEM) captures nanoscale ultra-fine details but lacks continuous 3D spatial information.Existing multimodal fusion, 3D reconstruction, and quantitative analysis methods generally face challenges such as incomplete modality fusion, inadequate microstructural detail recovery, and strong reliance on manual annotation, making highprecision analysis difficult to achieve.To address these issues, we propose an integrated approach based on multimodal image fusion and deep learning for precise 3D reconstruction and intelligent quantitative analysis of wood microstructure.A dual-stream attention fusion network is designed, where two encoding branches adapt to differences between modalities.By combining channel-spatial hybrid attention with contrastive learning, cross-modal feature fusion and consistency enhancement are achieved.A 3D super-resolution generative adversarial network (GAN) is constructed, incorporating deep residual channel attention modules and a multi-component joint loss function to overcome the axial resolution limitations of Micro-CT, enabling isotropic high-resolution 3D reconstruction.Furthermore, a weakly supervised multi-task segmentation and quantification network is proposed, which integrates self-supervised pretraining with guided attribute regression, reducing annotation requirements while achieving accurate segmentation and simultaneous multi-parameter quantification of wood microstructures.This end-to-end collaborative framework effectively addresses the core limitations of current methods, providing an efficient and precise imaging tool for wood microstructure analysis and enriching the application of multimodal fusion and 3D reconstruction techniques in materials science.

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