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A defect recovery method of a QR code image-seeking pattern based on CNN

A technology of image-finding graphics and recovery method, applied in the field of QR codes, can solve the problems of difficult operation, complicated processing process, unrecognizable terminal, etc., and achieve the effects of easy operation, good recovery effect and strong adaptability

Inactive Publication Date: 2019-03-08
FOSHAN SHUNDE SUN YAT SEN UNIV RES INST +2
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Problems solved by technology

[0002] At present, other terminals such as mobile phones are still unable to recognize the situation where one of the image-finding graphics of the QR code is missing. For the current public patents related to the recovery of the missing one of the image-finding graphics, they are all based on the QR code. In-depth analysis of the structure of the code and the algorithm carefully designed according to the version number of the QR code
This process is complex, difficult to operate, and the recovery effect is not good enough

Method used

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  • A defect recovery method of a QR code image-seeking pattern based on CNN
  • A defect recovery method of a QR code image-seeking pattern based on CNN
  • A defect recovery method of a QR code image-seeking pattern based on CNN

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Embodiment Construction

[0019] refer to figure 1 , figure 2 , image 3 , a CNN-based QR code imaging defect recovery method, comprising the following steps:

[0020] A CNN-based method for recovering defects in QR code image-finding graphics, comprising the following steps: S1: Input a QR code grayscale image with missing or defective image-finding graphics, use the lossless QR code grayscale image as a label, and train convolution Neural Networks;

[0021] S2: Obtain a six-layer convolutional neural network;

[0022] S3: Put the QR grayscale image to be restored into the six-layer convolutional neural network;

[0023] S4: Process the output results of each layer;

[0024] S5: Obtain a grayscale image of the QR code without defects.

[0025] CNN is the abbreviation of convolutional neural network. Convolutional neural network is a feedforward neural network. Its artificial neurons can respond to surrounding units within a part of the coverage area, and it has excellent performance for large-s...

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Abstract

The invention discloses a defect recovery method of a QR code image-seeking pattern based on CNN, which comprises the following steps of inputting a QR code gray image of a missing or defective position of the image-seeking pattern, using a lossless QR code gray image as a label, training a convolutional neural network; obtaining the convolution neural network with six layers; inputting the pictures to be recovered into the trained convolution neural network; after processing, obtaining the complete QR code of the image-seeking pattern. The method does not need additional picture preprocessingand does not need elaborate design algorithm according to the version number of QR cod, is simple and convenient, is easy to operate, and is good in restoration effect and strong in adaptability.

Description

technical field [0001] The invention relates to the field of QR codes, in particular to a CNN-based QR code imaging defect recovery method. Background technique [0002] At present, other terminals such as mobile phones are still unable to recognize the situation where one of the image-finding graphics of the QR code is missing. For the current public patents related to the recovery of the missing one of the image-finding graphics, they are all based on the QR code. In-depth analysis of the structure of the code and a well-designed algorithm based on the QR code version number. This process is complicated, difficult to operate, and the recovery effect is not good enough. Contents of the invention [0003] In order to overcome the deficiencies of the prior art, the present invention provides a simple, convenient, easy-to-operate, and good recovery effect recovery method based on CNN-based QR code image-seeking graphic defects. [0004] The technical solution adopted by th...

Claims

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Application Information

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IPC IPC(8): G06K7/14G06N3/04G06N3/08
CPCG06K7/1417G06K7/146G06N3/08G06N3/045
Inventor 吴小龙陈星光张东
Owner FOSHAN SHUNDE SUN YAT SEN UNIV RES INST
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