Fourier laminated microscope pupil recovery method based on neural network

Abstract

The invention discloses a Fourier laminated microscope pupil recovery method based on a neural network in the field of computer imaging. The problem that the reconstruction precision of the existingFourier laminated imaging model is low under the influence of optical phase difference is solved. A neural network model is established based on a TensorFlow deep learning framework in combination with a forward imaging mode of an FPM system. The problem that the universality of a reconstruction model based on a deep convolutional neural network is poor is solved; a recovery process for a pupil function of the system is introduced, so that the influence of optical aberration in the system on a reconstruction result can be better suppressed, and a better result is obtained. According to the invention, the frequency spectrum and the pupil function of the sample are set as a trainable two-dimensional network layer in the network; complex amplitude information and a pupil function of a sampleare obtained at the same time by minimizing a loss function in the training process. The method has good universality and can still obtain a reconstruction result better than that of a traditional algorithm under the condition that aberration exists in the system.

Description

technical field [0001] The invention relates to the field of computational imaging, in particular to a microscope image reconstruction method based on Fourier stack imaging technology. Background technique [0002] Fourier stack imaging microscopy (FPM) is a newly developed imaging method designed to circumvent the limitation of spatial bandwidth product (SBP) and obtain complex images with wide field of view and high resolution. Since 2013, Fourier stack imaging technology has been applied in optical microscopy, biomedicine, life sciences and other fields to obtain microscopic images with large field of view and high resolution. However, in FPM imaging systems, pupil aberrations of the used optical elements and incoherent light imaging significantly degrade the quality of the reconstruction results. Contents of the invention [0003] In order to solve the problems of low reconstruction accuracy caused by the existing Fourier stack imaging model under the influence of opt...

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Problems solved by technology

[0003] In order to solve the problems of low reconstruction accuracy caused by the existing Fourier stack imaging model under the influence of optical aberration and the poor versatility of the current reconstruction model based on deep convolutional neural network, the present invention provides a neural network-based Pupil Restoration Method of Fourier Stack Microscope

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