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• W4308459046 abstract "Optical aberrations introduced by sample or system elements usually degrade the image quality of a microscopic imaging system. Computational adaptive optics has unique advantages for 3D biological imaging since neither bulky wavefront sensors nor complicated indirect wavefront sensing procedures are required. In this paper, a stochastic parallel gradient descent computational adaptive optics method is proposed for high-efficiency aberration correction in the fluorescent incoherent digital holographic microscope. The proposed algorithm possesses the advantage of parallelly estimating various aberrations with fast convergence during the iteration; thus, the wavefront aberration is corrected quickly, and the original object image is retrieved accurately. Owing to its high-efficiency adaptive optimization, the proposed method exhibits better performances for a 3D sample with complex and anisotropic optical aberration. The proposed method can be a powerful tool for the visualization of dynamic events that happen inside cells or thick tissues." @default.
• W4308459046 created "2022-11-12" @default.
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• W4308459046 date "2022-11-15" @default.
• W4308459046 modified "2023-10-06" @default.
• W4308459046 title "Computational adaptive holographic fluorescence microscopy based on the stochastic parallel gradient descent algorithm" @default.
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• W4308459046 doi "https://doi.org/10.1364/boe.470959" @default.
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