Phase recovery improved algorithm based on MSE optimization

Abstract

The invention discloses a phase recovery improved algorithm based on MSE optimization, and the algorithm comprises the following steps of A1, taking a random Fourier phase as an input, obtaining a reconstruction result of a phase recovery algorithm, and calculating a mean square error between a Fourier amplitude of the reconstruction result and an initial Fourier amplitude of a target object; A2,taking a plurality of different random Fourier phases as input, and repeating the step A1 to obtain a plurality of corresponding reconstruction results and a plurality of corresponding mean square errors; and A3, taking the mean square error as an evaluation index of the quality of a reconstruction result, and selecting an optimal reconstruction result from a plurality of reconstruction results asa final output to realize the phase recovery of the target object. According to the invention, the stable phase recovery can be realized.

Description

technical field [0001] The invention relates to the fields of computer vision and digital image processing, in particular to an improved phase recovery algorithm based on MSE selection. Background technique [0002] The traditional phase recovery algorithm is used to iteratively restore the real Fourier phase of the target object with a random phase as the initial guess of the Fourier phase when the Fourier amplitude of the target object is known but the Fourier phase is unknown. Lie phase and spatial distribution of reconstructed objects. However, due to the randomness of the initial phase guessing, the spatial distribution of the target object reconstructed by the traditional phase recovery algorithm and the real Fourier phase of the target object recovered are very unstable, which makes it difficult to obtain from a single phase recovery result. Accurate and effective target airspace information limits the applicability of traditional phase recovery algorithms. [0003]...

AI Technical Summary

Problems solved by technology

However, due to the randomness of the initial phase guessing, the spatial distribution of the target object reconstructed by the traditional phase recovery algorithm and the real Fourier phase of the target object recovered are very unstable, which makes it difficult to obtain from a single phase recovery result. Accurate and effective target object airspace information limits the applicability of traditional phase recovery algorithms

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