Digital holographic compression transmission method of quantum backward propagation nerve network

Abstract

The invention discloses a digital holographic compression transmission method of a quantum backpropagation neural network. The original image is recorded as a digital hologram through a Fresnel off-axis holographic calculation method, a quantum backpropagation (QBP) neural network is constructed, and a digital hologram is used. Train the QBP neural network, use the trained QBP neural network to compress, transmit and decompress the digital hologram to obtain a reconstructed hologram, and reproduce the reconstructed hologram to obtain a reconstructed image. The method provided by the invention has faster parallel processing speed and stronger ability to store data, and is suitable for the calculation and processing of digital holograms with a large amount of data; it can use fewer training times to complete the training of the compressed transmission network structure, and improve the digital hologram. The compression transmission speed of the hologram; the image compression rate is adjustable, and the reproduced image quality is better.

Description

technical field [0001] The invention belongs to the field of digital image processing, and relates to a digital holographic compression transmission method using a quantum back-propagation (Quantum Back-Propagation, QBP) neural network, in particular to methods such as the production and reproduction of digital holographic images, and QBP neural network image compression. Background technique [0002] In 1947, British physicist Dennis Gabor invented holography while studying how to improve the resolution of electron microscopes. Holography is a technology capable of recording and reproducing real-world three-dimensional (Three-Dimensional, 3D) objects. The light waves reflected by the object coherently superimpose with the reference light waves to produce interference fringes, and these recorded interference fringes are called holograms. When the hologram is reproduced under certain conditions, the realistic three-dimensional image of the original object can be reproduced. ...

AI Technical Summary

Problems solved by technology

However, due to the defects of the traditional BP neural network such as slow processing speed and limited memory capacity in the case of a large amount of information, these defects limit the effect and application value of this technology in digital holographic image compression transmission

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