Enhancing Autofocus in Non-Mydriatic Fundus Photography: A Fast and Robust Approach with Adaptive Window and Path-Optimized Search

Abstract

Non-mydriatic fundus photography (NMFP) plays a vital role in diagnosing eye diseases, with its performance primarily dependent on the autofocus process. However, even minor maloperations or eye micro-movements can compromise fundus imaging quality, leading to autofocus inaccuracy and a heightened risk of misdiagnosis. To enhance the autofocus performance in NMFP, a fast and robust fundus autofocus method with adaptive window and path-optimized search is proposed. In this method, the adaptive focus window is used to suppress irrelevant image contents and correct the sharpness curve, and the path-optimized search is constructed to overcome the curve’s local extrema, in order to achieve rapid focus position convergence. This method was simulated and clinically studied with the self-developed autofocus system for NMFP. The results of 80 cases of human eye imaging show that, compared with similar autofocus methods, this method achieves a focus success rate of 90% with the least axial scanning, and can adapt to non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus.