Adaptive multi-feature fusion for visible-infrared image registration and character enhancement of bamboo slips

Ancient bamboo/wooden slips suffer severe character degradation after millennia of burial, requiring infrared imaging for text identification. This work proposes a multimodal coarse-to-fine registration method to fuse visible and infrared images while preserving texture/color and restoring degraded inscriptions. The approach comprises: (1) Coarse registration using edge-feature-priority strategy, leveraging stable slip contours for global alignment via downsampling; (2) Fine registration with improved ICP algorithm incorporating weighted features and dynamic weight adjustment, transitioning from edge-dominance to corner-dominance for precise local registration; (3) Multi-stage hybrid optimization combining gradient methods with multi-restart simulated annealing, maximizing mutual information for optimal transformation matrices. The method addresses weak texture, modal differences, and severe character degradation by selecting appropriate registration strategies and feature weights at different stages. Experiments demonstrate superior performance over existing methods in visual quality and quantitative metrics. Difference fusion based on registered multimodal images achieves effective degraded character restoration, significantly improving inscription readability.