基于幸运成像的图像复原方法研究综述OA

Lucky imaging constitutes a pivotal approach for restoring turbulence-degraded imagery.Recent advances have explored method optimizations across diverse application scenarios.However,existing reviews published years ago do not cover breakthroughs from the past decade.This study conducts a thorough investigation of cutting-edge algorithms,first introducing classical methodologies and outlining persistent challenges.We then elaborate on the developments and applications through three dimensions:real-time implementation,multi-target adaptability,and integration with complementary image processing techniques.A dedicated turbulence dataset is released alongside systematic analysis of benchmark datasets,evaluation metrics,and comparative performance of leading methods.Scenario-specific applicability and inherent limitations are analyzed,culminating in six future trajectories:GPU-edge heterogeneous computing,dynamic turbulence modeling with non-stationary compensation,data-driven end-to-end fusion,multi-modal cross-scale restoration,event-camera-based dynamic imaging,and standardized evaluation frameworks with open-source ecosystems.