Mitigating the ambiguity problem in the CNN-based wavefront correction
Chenda Lu, Qinghua Tian, Lei Zhu, Ran Gao, Haipeng Yao, Feng Tian, Qi Zhang, Xiangjun Xin
Abstract
In this work, we propose an attention-based adaptive optics method that uses a non-local block to integrate phase diversity with a convolutional neural network (CNN). The simulation results showcase the effectiveness of the proposed method to mitigate the ambiguity problem of phase retrieval and better performance than traditional CNN-based wavefront correction.
Topics & Concepts
WavefrontComputer scienceAdaptive opticsConvolutional neural networkBlock (permutation group theory)AmbiguityOpticsPhase retrievalArtificial intelligenceAlgorithmComputer visionFourier transformPhysicsMathematicsQuantum mechanicsProgramming languageGeometryAdaptive optics and wavefront sensingOptical Systems and Laser TechnologyOptical measurement and interference techniques