Research On Imaging Technology Of Meta-lens

Metamaterials is a new type of composite material with excellent performance,which can realize new physical properties that natural materials cannot match through artificially designed special structures,accomplishing wavefront shaping,polarization modulation,dispersion control of optical signals and other functions.As its twodimensional realization,metasurface plays an increasingly important role in the field of planar optics with its ultra-thin,light-weight,ultra-compact geometric properties and superior performance beyond traditional optical components,and metalens is one of its most successful applications.Metalens is compact,easy to integrate into the optical imaging system,and can achieve flexible focusing capabilities through precise design.However,metalens imaging currently faces two problems.One is that metalens cannot be made into a large size,which results in low imaging resolution and limites the field of view.The other is polarization dependence,which leads to low light efficiency and complex optical path of the imaging system,not conducive to further integration.In response to the above two issues,the main research content and innovations are as follows:1.Propose metalens array imaging: Inspired by the structure of insects' compound eyes,a method of metalens array imaging is proposed to solve the problem of low resolution and limited field of view in metalens imaging,which is based on the principle of light field imaging.2.Model the polarization characteristics of metalens: The existing metalens imaging system generally has polarization dependence.It is necessary to use wave plates and polarizers to obtain circularly polarized light as illumination,which often results in low light efficiency and complex optical path of the system.This article innovatively models the influence of polarization dependence on imaging performance as a classic image blur problem,and explores to obtain highperformance imaging results without using additional optical devices in the case of non-polarized light.3.Use convolutional neural network to solve the problem of imaging deblurring under non-polarized light: The existing artificially designed priori algorithm(total variation constraint,natural image prior and other regularizers)is aimed at the deblurring of natural images and is difficult to deal with the case of metalens imaging with polarization dependence under non-polarized light.This paper utilizes the powerful feature extraction capabilities of convolutional neural networks,using the iteratively generated parameters of the network as a priori constraint for the ill-posed optimization problem,so as to solve the problem of insufficient and inappropriate prior conditions in the deblurring of non-polarized imaging in a unique way.