A lensless LED matrix-based ptychographic microscopy imaging method using loss correction and adaptive step size. (May 2022)

• Record Type: Journal Article
• Authors: Wu, Xiaohui; Zhao, Jufeng; Cui, Guangmang; Mao, Haifeng
• Is Part Of: Optics and lasers in engineering. Volume 152(2022)
• Publication Date: 2022-05
• Subjects: Phase measurement -- Phase retrieval -- Microscopy -- Computational imaging; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2022.106980
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: A novel and valid algorithm of improving anti-noise ability by added a loss correction term and adjust the step size according to the value of this term. Discuss the parameters in the imaging system in detail, and get an optimal result. To make full use of dynamic range of the camera and facilitate a better reconstruction and extra diversity in the data also allows cleaner separation of the constituent modes in the illumination, we set up a diffuser on top of the aperture to introduce a range of random angles into the beam. Abstract: Lensless microscopy is an emerging technology that does not rely on the lens to focus and record the measured value. In conventional ptychography, the incremental gradient methods are widely used because of the superior computing efficiency and flexibility. However, because there are usually various noises collected by the sensor, which causes the reconstruction result to degrade sharply. The key point is how to control and design the step size. Therefore, we propose a loss correction algorithm with adaptive step size for lensless ptychography via multi-angle LED illumination. We add a loss correction term and adjust the step size according to the value of this term, which can effectively improve the convergence performance of the algorithm and can significantly improve the algorithm's anti-noise ability. Comparing with classical methods, both simulations and experiments prove that our method is better in efficiency and quality. Meanwhile, the results indicate the initial step size can be appropriately increased to improve the convergence speed without reducing the reconstruction quality.