Fuzzy license plate reconstruction method based on characteristic learning

A feature learning and blurring technology, applied in the field of image processing, can solve problems such as motion blur, inability to restore license plate images, defocus blur, etc., and achieve good reconstruction effects, improved recognizability, and accurate restoration effects

Active Publication Date: 2015-11-18
GUANGDONG XUNTONG TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Common license plate image restoration methods generally use targeted image restoration methods such as deblurring and contrast enhancement. These methods are less practical for harsh and complex actual surveillance scenes, because real surveillance videos often contain illumination, distance, blur, surveillance, etc. The

Method used

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  • Fuzzy license plate reconstruction method based on characteristic learning
  • Fuzzy license plate reconstruction method based on characteristic learning
  • Fuzzy license plate reconstruction method based on characteristic learning

Examples

Experimental program
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Embodiment 1

[0093] refer to figure 1 , a fuzzy license plate reconstruction method based on feature learning, including:

[0094] S1. Obtain a large number of clear character samples covering all license plate character types and obtain their corresponding fuzzy character samples. After establishing the fuzzy character sample library, perform feature training on the fuzzy character sample library, specifically:

[0095] After obtaining a large number of clear character samples covering all license plate character types, each clear character sample is convolved with various degraded functions that simulate realistic scenes to generate corresponding fuzzy character samples, and then all fuzzy character samples are established after the fuzzy character sample library , to perform feature training on the fuzzy character sample library. Wherein, the step of carrying out feature training to the fuzzy character sample database is as follows: according to the following formula, each class of fuz...

Embodiment 2

[0120] This embodiment is basically similar to Embodiment 1, and the difference is that in step S4, what this embodiment adopts to the segmented characters is a block classification method, and step S4 includes:

[0121] S41. Refer to figure 2 As shown, each segmentation character is divided into six uniform small blocks, and each small block is matched with the corresponding small block of each type of fuzzy character samples one by one to obtain the most likely top three classifications; specific to 34 types license plate character type, each small block is matched one by one with the training results of the small blocks of 34 corresponding positions. Multiply with the feature matrix (training result) of the 34 corresponding small blocks, and then classify according to the result sparsity, and use the three result vectors with the highest sparsity as the first three classifications of the small block.

[0122] S42. According to the prior condition of connectivity between a...

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Abstract

The invention discloses a fuzzy license plate reconstruction method based on characteristic learning. The method comprises the following steps of acquiring a large number of clear character samples covering all license plate character types and acquiring corresponding fuzzy character samples; setting up a fuzzy character sample database and performing characteristic training; acquiring fuzzy license plate images and calibrating four angular points of last five characters of the images; performing geometric correction and character segmentation to acquire five segment characters; acquiring the clear character sample and the fuzzy character sample corresponding to each segment character; according to the clear character sample and the fuzzy character sample corresponding to each segment character , performing character reconstruction on the segment character and then acquiring a reconstruction result; and performing histogram processing on the reconstruction result. The reconstruction result identifiability can be greatly improved. Relatively good reconstruction effects are achieved. License plate images can be recovered in a relatively clear and accurate way. The method can be widely applied in the license plate number identification field.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a fuzzy license plate reconstruction method based on feature learning. Background technique [0002] License plate information is one of the most important information in the field of video surveillance, and often becomes the key factor in case detection. However, in the surveillance video collected in the actual scene, the license plate image information is often lost due to various complicated reasons, and it is difficult for the human eye to recognize the license plate information, which greatly hinders the detection of the case. Therefore, super-resolution reconstruction of license plate images has become an urgent problem to be solved in video surveillance scenarios. [0003] Common license plate image restoration methods generally use targeted image restoration methods such as deblurring and contrast enhancement. These methods are less practical for harsh and complex actual...

Claims

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Application Information

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IPC IPC(8): G06T5/40
Inventor 窦逸辛
Owner GUANGDONG XUNTONG TECH
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