Self-organizing map neural network-based method for eliminating optical scanning holographic defocus noise

A technology of self-organizing mapping and neural network, which is applied in the field of eliminating optical scanning holographic defocus noise, can solve problems such as deformation, rough edges of results, and low timeliness, and achieve the effects of short training time, simple implementation, and easy operation

Inactive Publication Date: 2018-07-10
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0004] The document "Sectional image reconstruction in optical scanning holography using a random-phase pupil" proposes an algorithm for encrypting optical scanning holograms by using random phase plates, and proposes a method to eliminate defocus noise by using multi-frame averaging. The method can eliminate defocus noise very well, but due to the need for multi-frame averaging processi

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  • Self-organizing map neural network-based method for eliminating optical scanning holographic defocus noise
  • Self-organizing map neural network-based method for eliminating optical scanning holographic defocus noise
  • Self-organizing map neural network-based method for eliminating optical scanning holographic defocus noise

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[0061] Such as Figure 1 to Figure 6 As shown, the method for eliminating optical scanning holographic defocus noise based on self-organizing map neural network, the specific steps are as follows:

[0062] (S100) Obtaining an encrypted hologram by scanning the object with an optical holographic scanning device:

[0063] The basic structure of the optical holographic scanning device used is as follows: figure 1 As shown, it includes: the laser Laser, the first beam splitter BS1 corresponding to the laser Laser, the acousto-optic modulator AOFS, the first mirror M1, the first pupil p 1 (x, y) and the first optical path formed by the first convex lens L1 and corresponding to the first beam splitter BS1, the second mirror M2, the second pupil p 2 (x, y) and the second convex lens L2 constitute the second optical path corresponding to the first beam splitter BS1, the second beam splitter BS2 that combines the first optical path and the second optical path by interference, and the...

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Abstract

The invention discloses a self-organizing map neural network-based method for eliminating optical scanning holographic defocus noise, belongs to the fields of optical scanning holography and image noise elimination, and mainly solves the problem of defocus noise in the optical scanning holography. By virtue of the clustering analysis capability of the self-organizing map neural network, the defocus noise in reestablished images which are subjected to random encryption treatment can be eliminated. By virtue of the method, the problem of defocus noise in optical scanning holography can be solvedeffectively, and the noise eliminating method is applicable to each field.

Description

technical field [0001] The present invention relates to the fields of optical scanning holography and image denoising, in particular to a method for eliminating defocus noise of optical scanning holography based on a self-organizing mapping neural network and a device for realizing the method. Background technique [0002] Optical scanning holography, or OSH for short, is a recording technology that can record 3-D holographic information by using a single 2-D optical heterodyne scan. Since the technology was proposed, it has been applied in many fields, such as 3-D microscopy, 3-D pattern recognition, and holographic image encryption. [0003] Eliminating defocus noise has always been a hot research issue in optical scanning holography, and it is of great significance for holograms of multi-layer slices. When the reconstructed hologram is focused on a certain layer, the other layers will affect the clarity of the image as defocus noise. Therefore, in order to obtain a high-...

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Application Information

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IPC IPC(8): G03H1/00G03H1/12G06T5/00G06K9/62G06N3/04G06N3/08
CPCG03H1/0005G03H1/12G06N3/088G06T5/002G06T2207/20081G06T2207/20084G06N3/045G06F18/23
Inventor 欧海燕刘柯邵维王秉中
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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