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MRF (Multi-Reference Frame) license plate denoising algorithm based on combined apriorism of gradient information and block area

A gradient information, gradient prior technology, applied in computing, computer parts, character and pattern recognition, etc., can solve problems such as difficult to adapt to complex noise components, single noise components, etc.

Active Publication Date: 2015-05-13
NAT UNIV OF DEFENSE TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing denoising algorithms often can only solve the situation where the noise component is relatively single, and it is difficult to adapt to the situation where the noise component is complex in low-light environments

Method used

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  • MRF (Multi-Reference Frame) license plate denoising algorithm based on combined apriorism of gradient information and block area
  • MRF (Multi-Reference Frame) license plate denoising algorithm based on combined apriorism of gradient information and block area
  • MRF (Multi-Reference Frame) license plate denoising algorithm based on combined apriorism of gradient information and block area

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Embodiment Construction

[0053] The present invention will be further described below in conjunction with drawings and embodiments.

[0054] The invention provides an MRF license plate denoising algorithm based on gradient information and block region combination prior, which can effectively denoise and restore license plate images in noise video sequences. Such as figure 1 Shown, be flow chart of the present invention, comprise the steps:

[0055]Step 1: Using the richness of license plate digital gradient information and the characteristics of blocky digital areas, construct a license plate image combination prior based on gradients and blocky areas;

[0056] 1. License plate gradient information

[0057] Let I v is the denoised image of the license plate in the form of vector, and the numbers of rows and columns of the image pixels are m and n respectively. define I' c , I' r are the matrix of partial derivatives of pixel value changes in the column direction and the row direction, respective...

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Abstract

The invention belongs to the field of image information processing, and particularly relates to an MRF (Multi-Reference Frame) license plate denoising algorithm based on combined apriorism of gradient information and a block area. The method comprises the specific steps: S1, building license plate image apriorism information including the gradient apriorism and block area apriorism by using a number gradient information of a license plate; S2, carrying out noise estimation on a video image sequence, and determining a density function of noise distribution probability; S3, building a two-value markov random field model of the license plate image, combining the markov random field model with the license plate image apriorism information, and building an optimization problem model and solving. Compared with a traditional license plate denoising method, the SNR (Signal to Noise Ratio) of the license plate image can be effectively improved by making the best of the license plate image characteristics, so as to obtain more accurate digital edge of the license plate, and provide license plate source images with higher quality to the extraction and analysis of the license plate information.

Description

technical field [0001] The invention belongs to the field of image information processing, and in particular relates to an MRF license plate denoising algorithm based on gradient information and block region combination priors. Background technique [0002] License plate image information is of great significance in the fields of security monitoring, intelligent transportation and social security, and plays a very important role in vehicle speeding supervision, car anti-theft, road and parking lot charges, and traffic accident monitoring. However, the license plate images collected under low illumination conditions often have the characteristics of low signal-to-noise ratio, high noise, and complex noise components, which have a great impact on the extraction and analysis of license plate information, resulting in a decline in the accuracy of license plate recognition and tracking. Therefore, it has important practical significance and practical application value to effectiv...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/40G06T5/00
CPCG06V10/457G06V10/267G06V20/625
Inventor 刘煜尹晓晴王炜徐玮熊志辉
Owner NAT UNIV OF DEFENSE TECH