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A distortion correction method for a large field of view display device

A distortion correction and display device technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as low distortion processing efficiency and poor accuracy, and achieve strong data processing capabilities, easy implementation, and high correction accuracy. Effect

Active Publication Date: 2020-02-18
LUOYANG INST OF ELECTRO OPTICAL EQUIP OF AVIC
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Problems solved by technology

[0003] The purpose of the present invention is to provide a distortion correction method for a large field of view display device to solve the problems of low image distortion processing efficiency and poor precision caused by the use of traditional artificial neural networks for image distortion correction

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  • A distortion correction method for a large field of view display device
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  • A distortion correction method for a large field of view display device

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Embodiment Construction

[0034] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0035] The present invention establishes an artificial neural network with a double-layer implicit structure, and uses the particle swarm algorithm to solve the weights and thresholds of each layer of the artificial neural network with the double-layered implicit structure, and obtains the value corresponding to the global extreme value as the neural network. The weights and thresholds are then substituted into the established artificial neural network for training and learning to form an image distortion correction model. Finally, the distorted image data is input into the distortion correction model for correction, and the result is the corrected image. The implementation process of this method is as follows figure 2 The specific implementation steps are as follows:

[0036] 1. Analyze the digital image source through the optical engineeri...

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Abstract

The invention relates to a distortion correction method for a large field of view display device, belonging to the technical field of intelligent information image processing. The present invention establishes an artificial neural network containing a double-layer hidden structure, and uses the particle swarm algorithm to solve the weights and thresholds of each layer of the artificial neural network in the double-layer hidden structure, and obtains the value corresponding to the global extreme value as the neural network. The weights and thresholds are then substituted into the established artificial neural network for training and learning to form an image distortion correction model. Finally, the distorted image data is input into the distortion correction model for correction, and the result is the corrected image. The present invention uses particle swarm algorithm to train the weights and thresholds of artificial neural networks to overcome the shortcomings of traditional artificial neural networks such as slow convergence speed of local minimum values. The present invention is easy to implement, has strong data processing capabilities and high correction accuracy, and is suitable for large-scale applications. Distortion correction of field of view display devices.

Description

technical field [0001] The invention relates to a distortion correction method for a display device with a large field of view, and belongs to the technical field of intelligent information image processing. Background technique [0002] The nonlinear dynamic mechanism of the prominent phenomenon shows that there is a complex nonlinear mapping relationship between the distorted image data and the ideal image data, which is difficult to describe by explicit functions. To deal with such a complex nonlinear problem, traditional mathematical statistics and fuzzy mathematics methods are However, the artificial neural network based on nonlinear parallel computing has high modeling ability and good fitting ability when dealing with such complex nonlinear problems. However, the traditional artificial neural network has shortcomings such as slow convergence speed of local minima, which leads to the problems of low efficiency and poor accuracy of image distortion processing. SUMMARY...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/00G06N3/08
CPCG06N3/088G06T3/04
Inventor 田立坤
Owner LUOYANG INST OF ELECTRO OPTICAL EQUIP OF AVIC
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