Closed-loop wavefront sensing and correction in the mouse brain with computed optical coherence microscopy

Computational adaptive holographic fluorescence microscopy based on the stochastic parallel gradient descent algorithm

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.