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
  • A distortion correction method for a large field of view display device
  • 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, and belongs to the technical field of intelligent information image processing. The method comprises: establishing an artificial neural network with a dual-layer implicit structure and solving a weight and a threshold of each layer of the artificial neural network with the dual-layer implicit structure by utilizing a particle swarm algorithm; taking a value corresponding to a global extreme value as the weight and the threshold of the neural network and substituting the value into the established artificial neural network to perform training study to form an image distortion correction model; and finally inputting distortion image data into the distortion correction model to perform correction to obtain a corrected image. According to the method, the weight and the threshold of the artificial neural network are trained by adopting the particle swarm algorithm to overcome the shortcomings of local minimum, low convergence speed and the like of a conventional artificial neural network; and the method is easy to implement, good in data processing capability, high in correction precision and suitable for distortion correction of the large-field-of-view display device.

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
CPCG06N3/088G06T3/0012
Inventor 田立坤
Owner LUOYANG INST OF ELECTRO OPTICAL EQUIP OF AVIC
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