Phase Compensation Algorithm for Off-Axis Digital Holography Based on a Radial Basis Function Neural Network(Invited)

Digital holographic microscopy allows numerical reconstruction of the complex wavefront of biological samples, but the wavefront of the object has quadratic phase distortion and high-order aberration, which gives the imaging object a certain phase aberration. In this study, a phase distortion compensation algorithm based on a radial basis function (RBF) neural network is proposed. The RBF network is used as the interpolation function to estimate the actual phase of the object by minimizing the loss function. The loss function takes into account the output of the holographic surface and RBF network. In the simulation, the global mean square error is calculated based on the original model. The results using the RBF network, principal component analysis, and the spectrum centroid method are 0. 0374, 0. 0470, and 0. 3303, respectively. We set up a DHM system to observe the imaging amplitude and phase contrast of HL60 cells. The results show that the RBF method can better eliminate carrier frequency and phase distortion. The proposed method has the advantages of not requiring knowledge of the optical parameters and allowing adjustment of the number of sampling points to control the calculation time and interpolation accuracy. It has potential application prospects in the three-dimensional shape measurement of weak scattering objects or micro-nano structures.