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Fast autofocusing strategy for phase retrieval based on statistical gradient optimization
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AI
DOI：10.1016/j.optlaseng.2024.108636.png)
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Abstract
En 中文
Axial error arises from the error inherent in man-made measurement of distance between object and detector for in-line holography, resulting in image blurring, defocusing and low reconstruction quality. Nevertheless, existing methods for autofocusing and axial correction requires computing parameters with several distances neighboring current distance, which incorporates additional loops into the iterative algorithms. In this study, we propose an approach that updates diffraction distance directly based on statistical information of reconstructed object, thus eliminating the need for additional loops and reducing reconstruction time. Besides, our methods could achieve high reconstruction accuracy, while maintaining high convergence rate, stability and wide initialization space. The effectiveness of our proposed method has been validated through both simulation and experiments.
Keywords:
Phase retrieval
Autofocus
Optimization
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