Using Diffraction Deep Neural Networks for Indirect Phase Recovery Based on Zernike Polynomials

The phase recovery module is dedicated to acquiring phase distribution information within imaging systems, enabling the monitoring and adjustment of a system's performance. Traditional phase inversion techniques exhibit limitations, such as the speed of the sensor and complexity of the system. Therefore, we propose an indirect phase retrieval approach based on a diffraction neural network. By utilizing non-source diffraction through multiple layers of diffraction units, this approach reconstructs coefficients based on Zernike polynomials from incident beams with distorted phases, thereby indirectly synthesizing interference phases. Through network training and simulation testing, we validate the effectiveness of this approach, showcasing the trained network's capacity for single-order phase recognition and multi-order composite phase inversion. We conduct an analysis of the network's generalization and evaluate the impact of the network depth on the restoration accuracy. The test results reveal an average root mean square error of 0.086λ for phase inversion. This research provides new insights and methodologies for the development of the phase recovery component in adaptive optics systems.

Simulation Analysis of a Wavefront Reconstruction of a Large Aperture Laser Beam

In order to solve the problem of atmospheric influence on the far-field measurement of the quality of a laser beam, we proposed a direct wavefront measurement system based on the Hartmann detection principle, which can measure large apertures and high-power laser beams. The measuring system was composed of a lens array and a detector. The wavefront detection of a large aperture laser beam could be realized by controlling the distance between the lenses and the size of the lens. The influence of different duty cycle factors on the accuracy of the wavefront reconstruction under the same arrangement and different arrangement conditions was simulated and analyzed. The simulation results showed that when the sub-lenses of the system were not in close contact, the reconstruction accuracy of the duty factor of 0.8 was close to that of the case of the duty factor of 1. Within a certain detection range, the hexagonal arrangement of 19 lenses and the arrangement of 8 × 8 lens arrays had a high wavefront restoration accuracy; both were lower than 0.10 λ. The system proposed in this paper was suitable for measuring a large aperture laser beam, providing a new idea for measuring and analyzing the quality of large aperture laser beams. It also has an important significance for improving the measurement accuracy of the beam quality.

Direct Visualization and Identification of Membrane Voltage-Gated Sodium Channels from Human iPSC-Derived Neurons by Multiple Imaging and Light Enhanced Spectroscopy

In this study, transmission electron microscopy atomic force microscopy, and surface enhanced Raman spectroscopy are combined through a direct imaging approach, to gather structural and chemical information of complex molecular systems such as ion channels in their original plasma membrane. Customized microfabricated sample holder allows to characterize Na_v channels embedded in the original plasma membrane extracted from neuronal cells that are derived from healthy human induced pluripotent stem cells. The identification of the channels is accomplished by using two different approaches, one of them widely used in cryo-EM (the particle analysis method) and the other based on a novel Zernike Polynomial expansion of the images bitmap. This approach allows to carry out a whole series of investigations, one complementary to the other, on the same sample, preserving its state as close as possible to the original membrane configuration.

Statistical virtual eye model based on wavefront aberration

Wavefront aberration affects the quality of retinal image directly. This paper reviews the representation and reconstruction of wavefront aberration, as well as the construction of virtual eye model based on Zernike polynomial coefficients. In addition, the promising prospect of virtual eye model is emphasized.