Implicit Disparity-Blur Alignment for Fast and Precise Autofocus in Robotic Microsurgical Imaging

Abstract

Creating an intelligent surgical environment requires not only advanced robotic systems but also optimized microscopic imaging. However, autofocus remains a fundamental challenge, with current methods suffering from slow iterative processes or directional ambiguity, which compromises real-time performance. This paper presents an implicit disparity-blur alignment approach for robotic microsurgical autofocus, integrating stereo geometry’s monotonic depth cues with de-focus characteristics for rapid convergence. A novel physics-guided dual-stream network is developed to encode implicit depth representations through hierarchical cross-pathway feature fusion, enabling reliable focus prediction without explicit stereo matching in blur-degraded regions. An ROI-aware attention module is proposed to dynamically optimize focus-critical regions, coupled with learnable physics-guided kernel learning for precise Z-offset estimation. The approach achieves a top directional accuracy of 94.85% and a single-pass focus error of 0.20 mm with an inference time of 53 ms on a surgical dataset, which outperforms state-of-the-art methods in reducing iteration count by 22.8% and inference time by 51.8%. An intelligent robotic microscope prototype is developed, with validation through ex vivo tests demonstrating its ability to enable fast and precise multi-region focusing for microsurgeries.