WO2016163609A2 - Apparatus for adaptive probability-based low-illuminance image improvement and smear restoration processing in lpr system, and method therefor - Google Patents

Abstract

The present invention relates to adaptive probability-based low-illuminance image improvement and smear restoration processing capable of enhancing the number recognition performance of an LPR system by correcting distortion of an image generated in low-illuminance and high-illuminance areas. The apparatus according to one embodiment of the present invention comprises: a photographing module for photographing an original image containing a number plate of a vehicle using a CCD sensor; and a number recognition module for receiving the original image and recognizing characters on the number plate of the vehicle. The number recognition module further comprises: a determination unit for classifying the original image into one of a low-illuminance image, a high-illuminance image, and an unprocessed image on the basis of a given determination factor; and an image processing unit for generating a corrected image using the original image. If the original image is classified as a low-illuminance image, an advanced clipped histogram equalization method is used, and if the original image is classified as a high-illuminance image, a smear generated in the original image is removed.

Description

Adaptive Probability Based Low Light Image Enhancement and Smear Restoration Apparatus and Method in LPR System

The present invention relates to an adaptive probability-based low light image enhancement and smear reconstruction device and method in an LPR system. More particularly, the number recognition performance of an LPR system is corrected by correcting distortion of an image generated in a low light and high light region. The present invention relates to an adaptive probability-based low light image enhancement and smear reconstruction device and a method thereof that can greatly improve the performance.

The demand for vehicles is rapidly increasing due to economic growth and income growth, while the personnel who manage road conditions and traffic conditions are not. Therefore, a lot of efforts are being made to overcome the current poor traffic management system with limited personnel.

As part of this effort, the development of automatic vehicle recognition (including number recognition) system is in progress. Through vehicle identification or vehicle number (letter) recognition, it can be effectively used in various fields such as traffic control, traffic investigation, stolen vehicle arrest, access vehicle control, and parking facility management, and many studies have been made to date.

In particular, the process for recognizing the character of the license plate, unlike other character recognition, because the distortion caused by the camera noise, lighting changes, weather, etc. due to the environmental effects should be considered robust processing methods. However, in the case of the license plate area of the vehicle, its contents are limited and its structure is simpler than the general character recognition (pattern recognition). For this reason, License Plate Recognition (LPR) is the most common system for the efficient operation of environmental characteristics, increasing vehicle demand, manpower supply and management of parking space resources.

LPR system or number recognition technology was first developed in 1976 in the UK. In the decades since then, as technology has evolved and market demand has increased, LPR systems have grown steadily and have expanded gradually in European countries, including Southeast Asia. As a result, the LPR system market is growing significantly in North America. This led to strong motivation for effective crime suppression and prevention techniques, which in turn led to wider markets.

Past vehicle number recognition (LPR) or automated LPR (ALPR) systems use Optical Character Recognition (OCR) on images acquired from cameras to read license plates. Was applied. Recently, under the name of parking management system, efficient work of parking spaces is being carried out. Currently, the LPR system solves manpower supply, labor cost burden and leak rate by the manager's settlement of parking fee in relation to the parking environment. The demand for LPR system is constantly increasing and technology change and development continues.

However, in using the parking ticket in terms of operation, sudden events such as parking ticket loss, damage, and waste of resources are generated, and embarrassing events such as when there is no cash or only a large ticket is held.

In addition, due to the rapid increase in own vehicles due to changes in quality of life, causes of problems such as parking problems (space), inefficient operation management and user's inconvenience caused by lack of knowledge, unnecessary actions of the manager, etc. Unmanned automation systems are needed.

In general, the unmanned automation system (hereinafter referred to as LPR system) is still mainly used as a means of detecting a vehicle, but a non-embedded detector is required due to many inconveniences and maintenance difficulties of neighboring citizens due to buried construction. . In order to replace this, an ultrasonic sensor or a Doppler sensor is used to detect a vehicle or classify (classify) the type of the vehicle, which is used for expressway toll stations, and collects charges according to vehicle types differently.

As the technology and coverage of the system expands and becomes increasingly widespread, it includes three basic components of a typical LPR system.

The first obtains the image source from the camera or video (image), the second extracts the vehicle number for the information of the camera or video (image) input from the core or engine in the LPR system, and finally the pre-store or learn It consists of recognizing the matching process for the numbered letters, or integrating with other systems.

In the present invention, a method of restoring and processing a smear phenomenon according to a high intensity distribution of a subject reflected from sunlight and an image quality improvement input to a camera to improve number recognition in an LPR system is developed.

In most LPR systems, images input to the camera are acquired in consideration of the camera's aperture and shutter speed, or undesired information is generated due to the effects of illuminance changes or distortions caused by the subject's inherent properties. Recognition performance is adversely affected.

Among them, the weather change when the vehicle is introduced in the environment where the LPR system is installed, the location of the building and the access road, the characteristics of the camera (CCD, CMOS type), the physical distortion state of the license plate, and the color color distribution of the license plate The recognition performance is determined in accordance with this. Among these factors, weather changes, camera characteristics, and color distribution changes can deal with software-distorted information rather than hardware, and can lead to system stabilization.

In general, weather changes are inputted with degraded images due to low or high light area distributions. Since the subject (vehicle) appears dark in the low-input image, the number area itself is not detected or partially detected, which reduces recognition. To overcome this, the amount of light is adjusted by changing the shutter and aperture of the camera. do. However, this has a number of approaches in the technical, ie image processing, signal processing and computer vision fields, rather than the camera's function, since this has an adverse effect on vehicles entering the general weather environment.

The most easily applicable part is increasing the intensity value to an arbitrary value for the entire image, gamma correction method, histogram equalization, and the like. Among these methods, there is a method of correcting the brightness of the low illumination region by simply adding a random value to the pixel value of each pixel, but information distortion occurs due to the addition value in the high illumination region.

As a way to solve this problem, there is a histogram smoothing method that accumulates the brightness of the same pixel so as to exist in uniform values. However, this may result in an undesirable result due to different threshold values depending on the noise level and shape of the image. It does not help much under the influence of (Dynamic Range).

Another method is gamma correction. Gamma correction refers to the nonlinear transformation of the intensity signal of light using a nonlinear transfer function. In other words, human vision reacts nonlinearly to brightness according to Weber's law. For example, if the brightness of light is linear within a limited bit depth, such as 8 bits per channel, dark areas where the human eye responds sensitively do not feel smooth when the brightness changes, but are disconnected. Posterization occurs (distorted). In addition, it is possible to improve the low light area by changing the gamma value, but it is difficult to cope with the environmental change because the values of the applied gamma values are different for each environment.

In addition, the characteristics of cameras generally include blooming as well as smearing caused by light. When using a Charge Coupled Device (CCD) or a Complementary Metal Oxide Semiconductor (CMOS) as an image sensor device, images are stored as electrical signals and digital schemes. Blooming, smear phenomenon.

Blooming occurs in both CCD and CMOS sensors, caused by high brightness objects such as the sun or light sources. Blooming phenomenon refers to the formation of a round border around the light source and around the street light. This phenomenon is caused when the object (object) contains an object such as a light source or a light, and the light of the object is too bright to exceed the capacity that the image sensor can handle and the light bleeds around. will be. On the other hand, the smear phenomenon is similar to the blooming phenomenon, but it is different from the CCD sensor, and when a strong reflected light such as a light source or a light is taken, a line is vertically displayed.

Due to the characteristics of the camera applied to the LPR system, it is composed of a CCD type, which is weak from the light of a strong light source, and in some cases, the degree of distortion may be evenly distributed in the entire image. In particular, this cause is commonly seen in images acquired by a CCD image sensor such as the sun or a car headlight such as a strong light source.

Therefore, the present invention proposes a stabilized technology of a system having robust characteristics against number recognition performance and environmental change by constructing an improved LPR system capable of low illumination area and smear restoration processing for an image acquired from a camera in an LPR system. .

The present invention has been made to solve the above-mentioned problems, and is an adaptive probability based low light image that can greatly improve the number recognition performance of the LPR system by correcting the distortion of the image generated in the low light and high light areas. An object of the present invention is to provide an improvement and smear restoration apparatus and a method thereof.

In addition, the present invention utilizes an improved cutting histogram smoothing (A_CHE) that sets the cutting ratio dynamically according to an image, thereby greatly improving the contrast of a low light image and securing a stable image based on adaptive probability. An object of the present invention is to provide an image improvement and smear restoration processing apparatus and method thereof to a user.

In addition, the present invention solves the smear phenomenon through the post-processing process using the image processing rather than the sensor level unit, the structural complexity is not large, it can be implemented at low cost and adaptive probability-based low light image that can ensure efficient operation An object of the present invention is to provide an improvement and smear restoration apparatus and a method thereof.

On the other hand, the technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems that are not mentioned are clearly to those skilled in the art from the following description. It can be understood.

In the LPR system related to an example of the present invention for realizing the above problem, the adaptive probability-based low light image improvement and smear restoration processing apparatus is equipped with a CCD sensor and includes a license plate of a vehicle using the CCD sensor. A shooting module for shooting an image; And a number recognition module for receiving the original image photographed by the photographing module to recognize a character of the license plate of the vehicle, wherein the number recognition module comprises: the original image based on a discrimination factor associated with the original image; A discriminating unit classifying the image into one of a low light image, a high light image, and an unprocessed image; And an image processor configured to generate a corrected image using the original image, wherein the image processor includes an improved clipped histogram smoothing when the original image is classified as the low light image by the determination unit. A low light image processor for generating the corrected image from the original image by using a histogram equalization method; And a high illumination image processor for generating the corrected image by removing the smear generated in the original image when the original image is classified into the high illumination image by the determination unit. When the target image is used for character recognition of the license plate of the vehicle, and the original image is classified into the low light image or the high light image by the determination unit, the target image is the correction image, and by the determination unit When the original image is classified as the raw image, the target image is the original image.

The determining factor may be the intensity of light of the original image converted to gray scale, and when the change of the intensity of light of the original image is higher than a predetermined threshold, the determination unit may determine the original image. Is classified into the raw image.

In addition, when the change in the intensity of light of the original image is lower than the threshold, the determination unit classifies the original image as the low light image when the intensity of light of the original image is less than or equal to a predetermined first value. If the intensity of light of the image is higher than the first value, the original image is classified into the high illumination image.

In addition, the improved cutting histogram smoothing method determines an adaptive cutting ratio for the original image, and generates a cutting histogram in which an upper region of the histogram of the original image is removed according to the determined adaptive cutting ratio. At least a portion of the upper region is cut and reallocated to the cut histogram to generate the corrected image.

In addition, the adaptive cutting ratio is

Is determined by. In the above equation, adaptive_α_ratio is the adaptive truncation ratio, f (x, y) is a gray value of the original image,

to be.

Further, the cut portion of the upper region includes a cutting range for the low illumination distribution area and a cutting range for the high illumination distribution area.

In addition, the low light distribution area is expressed by

And the high illuminance distribution region is

It depends on. In the above equation, GL is the low light distribution area in the upper region, GH is the high illuminance distribution region in the upper region, T is a value set arbitrarily to distinguish low and high illuminance, and GlobalLevel is the original image. It is a global level.

In addition, the cutting range for the low illumination distribution area is determined according to Equation 3 below, and the cutting range for the high illumination distribution area is determined according to Equation 4 below.

Equation 3

Equation 4

In Equation 3 and Equation 4, GLL is a cutting range for the low light distribution area, GHH is a cutting range for the high light distribution area, C _p is the cutting histogram, and k _low is a gray scale. The low illuminance distribution region, k _high is the _high illuminance distribution region at gray scale, and k is the number of sums of gray scales.

The high illuminance image processor may further include: a detector configured to detect a position of the first column where the smear is generated among the columns constituting the original image; And a remover configured to remove the smear from the original image based on the detected position information of the first column.

The detection unit may include: an extraction unit extracting a signal distribution curve for each column constituting the original image by using the original image input to the number recognition module; And a converting unit converting the signal distribution curve into a normal distribution curve, wherein the signal distribution curve represents a sum of gray values of a plurality of pixels constituting each column constituting the original image.

In addition, the detection unit generates a binary pattern map by comparing the normal distribution curve with a preset threshold value, the binary pattern map has a value of 0 in an area where the normal distribution curve is smaller than the threshold value, and the normal distribution curve is In the region larger than the threshold, the binary pattern map has a value of 1, and the region in which the binary pattern map has a value of 1 corresponds to the first column of the original image.

The detection unit may extend a width of the first column by adding a part of a column adjacent to the first column to the first column, and the removal unit may include the binary pattern at gray values of a plurality of pixels constituting the original image. The gray value of each pixel of the map may be subtracted, and the smear generated in the first column may be removed using the subtracted result.

The apparatus may further include a restoration unit for restoring the original image of the first column from which the smear is removed by using a predetermined interpolation method.

The interpolation method of the reconstruction unit may calculate a priority for each of a plurality of pixels in the patch, determine a pixel having the highest priority among the calculated priorities, and determine the pixel of the highest priority and the patch. Reconstruction is performed by comparing the similarity of pixels which do not constitute the first column among a plurality of pixels in the pixel, and the patch includes at least a part of the pixels which constitute the first column.

The number recognition module may further include an image restoring unit generating a restored image by applying a focus degradation method to the target image, wherein the restored image is a license plate of the vehicle of the number recognition module. The focus deterioration method is a combination of a high resolution image generation method and a detailing method, and the image reconstruction unit presets a target image having deterioration of focus in order to perform the high resolution image generation method. Image estimation means for up-scaling according to up-scale coefficients; And image generating means for generating a high resolution image from which at least a portion of the focal deterioration is removed by applying bicubic interpolation to the target image and the up-scaled image.

The image reconstructing unit may further include image restoring means for removing at least a portion of the focal deterioration from the generated high resolution image by using a directional adaptive guided filter. can do.

On the other hand, in the LPR system related to an example of the present invention for realizing the above-described problem, the adaptive probability-based low light image improvement and smear restoration processing method, the image pickup module equipped with a CCD sensor using the CCD sensor vehicle license plate Shooting step of shooting the original image including the; An input step of inputting the original image photographed by the photographing module to a number recognition module to recognize a character of the license plate of the vehicle; A discriminating step of classifying, by the discrimination unit of the number recognition module, the original image into one of a low light image, a high light image, and an unprocessed image based on a determination factor related to the original image; An image processing step of generating, by the image processing unit of the number recognition module, a corrected image using the original image; And a step of recognizing, by the number recognition module, a character of the license plate of the vehicle using a target image, wherein the image processor is further improved when the original image is classified as the low light image by the determination unit. When the original image is classified as the high illumination image by the low light image processor and the discriminator to generate the corrected image from the original image by using an advanced clipped histogram equalization method, the original image is classified into the high illumination image. And a high illuminance image processor to generate the corrected image by removing the generated smear. When the original image is classified into the low illuminance image or the high illuminance image by the determination unit, the target image is the corrected image. And when the original image is classified as the raw image by the discriminating unit, The image is the original image.

The present invention has been made to solve the above-mentioned problems, and is an adaptive probability based low light image that can greatly improve the number recognition performance of the LPR system by correcting the distortion of the image generated in the low light and high light areas. The improvement and smear restoration processing apparatus and method can be provided to a user.

In addition, the present invention utilizes an improved cutting histogram smoothing (A_CHE) that sets the cutting ratio dynamically according to an image, thereby greatly improving the contrast of a low light image and securing a stable image based on adaptive probability. An image improvement and smear restoration processing apparatus and method can be provided to a user.

In addition, the present invention solves the smear phenomenon through the post-processing process using the image processing rather than the sensor level unit, the structural complexity is not large, it can be implemented at low cost and adaptive probability-based low light image that can ensure efficient operation The improvement and smear restoration processing apparatus and method can be provided to a user.

On the other hand, the effect obtained in the present invention is not limited to the above-mentioned effects, other effects that are not mentioned will be clearly understood by those skilled in the art from the following description. Could be.

The following drawings, which are attached to this specification, illustrate one preferred embodiment of the present invention, and together with the detailed description thereof, serve to further understand the technical idea of the present invention. It should not be construed as limited.

1 is an embodiment of a typical LPR system in accordance with the present invention.

2 shows an example of a block diagram of an apparatus for adaptive low-light image enhancement and smear restoration according to the present invention.

3 and 4 diagrammatically show histogram truncation of an image related to the present invention.

5 is a schematic diagram for explaining an improved cut histogram smoothing that can be applied to the present invention.

6 illustrates an example of a signal distribution curve according to each column of an image acquired by the photographing module.

FIG. 7 shows a normal distribution curve with the signal distribution curve of FIG. 6 as an input.

8 is a flowchart of a method for adaptive low-light image enhancement and smear reconstruction according to an example of the present invention.

9 to 11 and 12 to 14 are examples showing the results of processing the low light image according to the present invention.

15 to 17 and 18 to 20 are examples showing the result of restoring the smear generated in the high illuminance image according to the present invention.

<Description of the code>

10: shooting module

20: number recognition module

30: determination unit

40: image processing unit

50: low light image processing unit

60: high illumination image processing unit

70: image restoration unit

100: Adaptive Probability Based Low Light Image Enhancement and Smear Reconstruction

The adaptive probability based low light image improvement and smear restoration processing apparatus 100 according to the present invention includes a photographing module and a number recognition module. The imaging module is equipped with a CCD sensor and photographs the original image including the license plate of the vehicle using the CCD sensor. The number recognition module receives an original image photographed by the photographing module, and recognizes a character of the license plate of the vehicle using the original image.

The number recognition module includes a discrimination unit and an image processor, and the image processor includes a low light image processor and a high light image processor. The discriminator classifies the original image into one of a low light image, a high light image, and an unprocessed image based on a discrimination factor related to the original image.

The image processor generates a corrected image using the original image. When the original image is classified as a low light image, the low light image processor generates a corrected image from the original image by using an advanced clipped histogram equalization method. When the original image is classified as the high illuminance image, the high illuminance image processor generates a corrected image by removing the smear generated in the original image.

When the original image is classified as the low light image or the high light image, the number recognition module uses the corrected image for character recognition of the license plate of the vehicle. If the original image is classified as the raw image, the number recognition module uses the original image for character recognition of the license plate of the vehicle.

Hereinafter, with reference to the drawings will be described a preferred embodiment of the present invention. In addition, the Example described below does not unduly limit the content of this invention described in the claim, and the whole structure demonstrated by this embodiment is not necessarily required as a solution of this invention.

The general LPR system is used to collect charges according to the operating tariff system with the vehicle's entry / exit information, or is used for collecting and discharging status and information of the unspecified vehicle without the operation tariff system. Furthermore, it is suggested as an integrated direction for observing vehicle movement in geographically dispersed organizations. 1 is an embodiment of a typical LPR system in accordance with the present invention.

As shown in FIG. 1, the LPR system detects a license plate area of a vehicle input by a camera and recognizes a license plate character of the vehicle using a number and character detection method, and transmits the license plate character to a local PC or a server. Can manage and supervise

The vehicle's number information is used for entering, leaving, and collecting charges according to the fare system, vehicle traffic analysis, regional congestion analysis, and time-of-day vehicle access analysis, thereby promoting smooth operation management and maximizing user convenience. There is a purpose.

In the following, we propose an LPR system that is robust to number recognition performance and environmental change by constructing an improved LPR system capable of low-light region and smear recovery.

<Configuration of LPR System>

Hereinafter, a configuration of an apparatus for adaptive low-light image enhancement and smear restoration according to the present invention will be described in detail with reference to the accompanying drawings.

2 shows an example of a block diagram of an apparatus for adaptive low-light image enhancement and smear restoration according to the present invention. Referring to FIG. 2, the adaptive probability-based low light image enhancement and smear restoration processing apparatus 100 of the present invention includes a photographing module 10, a number recognition module 20, a discriminator 30, and an image processor 40. And an image restoring unit 70.

However, since the components illustrated in FIG. 2 are not essential, the adaptive probability-based low light image enhancement and smear reconstruction apparatus 100 having more or fewer components may be implemented. In addition, the components shown in FIG. 2 are mutually connected, and it is possible for each component to be implemented separately or in combination with each other, as shown in FIG. 2. Hereinafter, each configuration will be described.

The photographing module 10 is installed in the LPR system to photograph the vehicle, and photographs the vehicle positioned in the preset section to generate the original image. The license plate of the vehicle is photographed in the original image photographed by the photographing module 10, and the original image is transmitted to the number recognition module 20 and used to recognize the license plate of the vehicle.

The photographing module 10 includes a tilting device capable of tilting in the x-, y-, and z-axis directions, respectively. The photographing module 10 has a configuration capable of photographing a zoom-in image or a zoom-out image of the vehicle by rotating the camera at a designated x and y coordinate.

The photographing module 10 may be implemented using a camera equipped with a fisheye lens. If a fisheye lens having a wide angle of view is used, an image of an omnidirectional (360 °) region may be captured based on the imaging module 10.

The imaging module 10 is equipped with a CCD sensor or a CMOS sensor, preferably using a CCD sensor. The CCD image sensor and the CMOS image sensor have a light receiving unit which commonly receives light and converts it into an electric signal. CCD-type image sensor transfers electric signal through CCD and converts to voltage at last stage. CMOS image sensors, on the other hand, convert the signal from each pixel to a voltage and pass it outside. That is, the CCD-type image sensor moves electrons generated by light to the output unit by using a gate pulse as it is, and the CMOS-type image sensor converts electrons generated by light into voltage within each pixel and then converts the electrons into light by the number of pixels. There is a difference between the output through the switch.

In the CCD image sensor, a smear phenomenon may occur due to its signal processing method. The smear phenomenon refers to a phenomenon in which a single line appears vertically on the screen when a strong reflected light such as a light source or an illumination is photographed. This is often seen when using high-speed shutter, and is often seen when shooting very bright objects such as light sources. CCD-type image sensor has a structure in which only one light exists in one cell, and smear phenomenon occurs when the amount of charge that can be stored in one cell flows due to reflection and interference between cells.

Smear is easily generated in the buffer area for storing or transmitting to the image sensor in response to light exposure in the case of a high-speed shutter setting. In the high-speed shutter of the CCD, the exposure time of the CCD is achieved through the shutter of the camera body, and the exposure is controlled by directly controlling the CCD at a shutter speed higher than the synchronization speed. If the shutter of the camera body is open when the image is acquired by using the electronic shutter of the CCD, the light is continuously incident on the photodiode, and the charge is overflowed in the stored space, and if the charge of the vertical array of CCD is read out, the overcharge Because of this, bright streaks are produced, which is why smearing occurs.

The smear phenomenon generated may distort the photographed image and cause problems such as vehicle shape identification and number recognition of the vehicle in a system for detecting or controlling a vehicle.

On the other hand, the number recognition module 20 of the adaptive low-light image enhancement and smear decompression processing apparatus 100 of the present invention uses the determination unit 30, the image processing unit 40, the image restoration unit 70, and the like. It may include.

The determination unit 30 classifies the original image generated by the photographing module 10 into one of a low light image, a high light image, and an unprocessed image based on a determination factor related to the original image. Here, the discriminating factor related to the original image is the intensity of light of the original image converted to gray scale.

In detail, when the change in the intensity of light of the original image is higher than a predetermined threshold, the determination unit 30 classifies the original image as an unprocessed image. When the change in the light intensity of the original image is lower than the threshold value, if the light intensity of the original image is less than or equal to the first predetermined value, the original image is classified as a low illumination image, and when the light intensity of the original image is higher than the first value, The original image is classified as a high illuminance image.

On the other hand, the image processor 40 improves the image of the original image classified as a low light image or a high light image. The image processor 40 may further include a low light image processor 50 for an original image classified as a low light image and a high light image processor 60 for an original image classified as a high light image.

When the original image is classified as a low light image, the low light image processor 50 generates a corrected image from the original image using an improved clipped histogram equalization (A_CHE) method.

Specifically, according to the improved cutting histogram smoothing method, an adaptive cutting ratio for the original image is determined, and according to the determined adaptive cutting ratio, a cutting histogram in which the upper region of the histogram of the original image is removed is generated, At least a portion of the region is cut and reallocated to the cut histogram.

In this regard, FIGS. 3 and 4 diagrammatically illustrate histogram truncation of an image related to the present invention. 3 is a diagram showing the removal of the histogram top region of the original image according to the conventional CHE method, Figure 4 is a histogram top region of the original image according to the improved cutting histogram smoothing (A_CHE) method applied to the present invention It is shown graphically as being removed.

According to the conventional CHE method, as shown in FIG. 3, the upper region of the histogram is removed according to a fixed cutting ratio. The CHE method disclosed in Korean Patent No. 10-0756318 (Patent Document 2) also uses a fixed cutting ratio.

However, according to the improved cutting histogram smoothing (A_CHE) method of the present invention, as shown in FIG. 4, the cutting ratio is fluidly determined according to the original image, and the top region removed according to the cutting ratio is cut histogram. Will be assigned to. Here, the adaptive truncation ratio adaptive_α_ratio is determined by Equation 1 below.

In Equation 1, f (x, y) is a gray value of the original image,

to be.

In addition, Figure 5 is a schematic diagram for explaining the improved cutting histogram smoothing that can be applied to the present invention. The upper region removed according to the adaptive cutting ratio is reassigned to the cutting histogram by clipping the low and high illumination distribution areas. As shown in FIG. 4, the cut portion of the upper region includes a cutting range for the low illumination distribution area and a cutting range for the high illumination distribution area.

Here, the low light distribution area GL and the high light distribution area GH may be expressed as Equation 2 and Equation 3, respectively.

In Equations 2 and 3, T is a value arbitrarily set to distinguish low and high illumination, for example, T is 128. GlobalLevel means the global level of the original image.

The cut range GLL for the low light distribution area may be expressed as Equation 4, and the cut range GHH for the high light distribution area may be expressed as Equation 5.

In Equations 4 and 5, C _p is the truncated histogram, k _low is the low light distribution area in gray scale, k _high is the _high light distribution area in gray scale, and k is the number of sums of gray scales.

Meanwhile, referring back to FIG. 2, when a smear is generated in the original image classified as the high illumination image, the high illumination image processor 60 generates a correction image by removing the smear generated in the original image. The high illuminance image processor 60 may further include a detector 62, a remover 64, a restorer 66, and the like.

The detector 62 may determine whether a smear has occurred in the input original image, and if it is determined that the smear has occurred, the detector 62 may detect the position of the column (first column) where the smear has occurred.

The detector 62 may further include an extractor and a converter. The extractor extracts a signal distribution curve for each column constituting the original image using the original image. The converting unit converts the signal distribution curve generated by the extracting unit into a normal distribution curve.

In this regard, FIG. 6 illustrates an example of a signal distribution curve according to each column of an image acquired by the photographing module, and FIG. 7 illustrates a normal distribution curve input to the signal distribution curve of FIG. 6.

As illustrated in FIG. 6, the extractor of the detector 62 may curve the input original image into a signal distribution with respect to a column unit signal. The signal distribution curve represents a sum of gray values of a plurality of pixels constituting each column constituting the original image.

In addition, as illustrated in FIG. 7, the converter of the detector 62 may convert the signal distribution curve of the input original image into a normal distribution curve.

Referring back to FIG. 2, the remover 64 may remove smears generated in the original image based on the position information of the first column detected by the detector 62.

The reconstructor 66 may reconstruct the original image of the first column from which the smear is removed by using an interpolation method based on the priority of the patches. Specifically, the reconstructor 66 calculates a priority for each of the plurality of pixels in the patch, determines the pixel of the highest priority having the highest priority among the calculated priorities, and the pixel of the highest priority and the patch. The reconstruction may be performed by comparing the similarity of the pixels that do not constitute the first column among the plurality of pixels within the pixels.

The image reconstruction unit 70 generates a reconstructed image by applying a focus degradation method to the target image. The image restoring unit 70 may include an image estimating means 72, an image generating means 74, an image restoring means 76, and the like.

Here, the target image may be an original image or a corrected image. If the original image is classified into a low light image or a high light image by the discriminating unit 30, the corrected image is a target image.

The image estimating means 72 is used when a high resolution image generation method is used as the focus deterioration method.

The image estimating unit 72 may generate a super-resolution image by up-scaling the low resolution degraded image according to an up-scale coefficient.

When deterioration of focus occurs due to shaking or error in the target image, the image estimating unit 72 may predict the focused image from the image of the target image.

In the case of an image having a deterioration of focus, an edge portion of a subject is blurred, and various algorithms may be used to predict actual edge information. Such algorithms are well known to those skilled in the art to which the present invention pertains, and detailed descriptions thereof will be omitted.

The image estimating unit 72 obtains the super resolution image SR from the low resolution image in the intermittent image having the deterioration of focus by using the algorithm, and estimates the focused image using the algorithm.

The image generating means 74 is used when using a high resolution image generating method as an improved focus degradation method.

When the super resolution image is generated from the low resolution image input by the image estimating means 74 according to an up-scale coefficient, the image generating means 74 focuses using the super resolution image and the target image. A high resolution image from which at least a portion of the degradation is removed may be generated, and the high resolution image is calculated by interpolation. In this case, the interpolation method may preferably improve the deterioration of the focus image by using bicubic interpolation.

The relationship between the deteriorated image and the high resolution image may be represented by Equation 6 below.

here,

Is a generated high resolution image, L is a focal deterioration input as a low resolution image, and νH is a super resolution image to which interpolation is applied according to an up-scale factor in the low resolution image.

The image generating means 74 may generate a high resolution image focused in accordance with Equation 6 in the target image having deterioration of focus, and details of the generation will be omitted since it is obvious to a person skilled in the art.

The image restoring means 76 is used in the case of using the detailing method as an improved focus deterioration method.

The image restoring unit 76 receives a high resolution image that is in focus from an image having deterioration of focus, and uses a deterring method to improve sharpness.

When the image generating means 74 receiving the target image generates a high resolution image, the image restoring means 76 may remove a part of the focus deterioration from the target image using the generated high resolution image.

The removal of such focal deterioration may be made in plural. That is, the image generating means 74 calculates a high resolution image using the super resolution image estimated by the image estimating means 72, and the image restoring means 76 is used to improve the sharpness by the deterring method.

The image of which the image restoring means 76 partially removes the focus deterioration is input to the image estimating means 72 again to estimate the super resolution image, and the high resolution image is calculated again by the image generating means 74. The image restoring means 76 uses this to remove the deterioration of focus. This repeated process may be repeated according to the set parameter value.

In addition, a direction adaptive guided filter may be used as an example of the deterring method that may be used in the image restoring means 76. The guided filter is a local linear filter and has a property of smoothing while preserving edge components like the bilateral filter. This feature prevents the edges of the image from crushing and maintains the base layer.

According to the image generating means 74, the image quality is improved, but there may be local smoothing and edge defects, that is, artifact defects about the object or feature information. In order to further improve this and obtain precise results, a clear quality image can be obtained by using a directional adaptive guided filter.

The guided filter as a deterring method applicable to the present invention performs an operation as shown in Equation 7 below.

In Equation 7, i and j denote pixel positions, w _ij denotes a filter kernel, p _j denotes an input image, and I denotes a linear transformation image.

The filter kernel of Equation 7 may be expressed as Equation 8 below.

In Equation 8 I is a linear conversion image, w _ij denotes a filter kernel, and σ is a dispersion, ε is a normalization parameter, μ _k is the average of the I transform image of w _k, k is w _k Kernel center pixel location.

<How to operate LPR system>

Hereinafter, an adaptive probability based low light image enhancement and smear restoration processing method according to the present invention will be described in detail with reference to the accompanying drawings.

8 is a flowchart of a method for adaptive low-light image enhancement and smear reconstruction according to an example of the present invention.

First, the photographing module 10 equipped with the CCD sensor photographs the original image including the license plate of the vehicle by using the CCD sensor, and the original image photographed by the photographing module 10 is input to the number recognition module 20. (S10). In the original image, the vehicle is photographed, and in general, the license plate of the vehicle is located under the original image.

Next, the determination unit 30 determines whether there is a change in the intensity of light in the original image (S20). In the step S20, after the original image is converted into a gray scale image, the feature change is observed for a region of interest ROI which is a part of the entire original image.

Subsequently, the determination unit 30 determines whether the light intensity of the original image is a low illumination component (S30). The present invention can largely correct two kinds of distortion information according to the determination of step S30. The first suggests an image improvement method by extending the dynamic range of low and high illumination areas, and the second method proposes a method of detecting and restoring smear phenomenon due to the charge of light in the high illumination area. .

Subsequently, the image processor 40 generates a corrected image using the original image. Specifically, when the original image is classified as a low light image, the low light image processor 50 generates a corrected image using an improved clipped histogram equalization method (S40), and the original image is a high light image. If it is classified as the high intensity image processing unit 60 removes the smear generated in the original image to generate a corrected image (S42).

In the step S40, an improved truncated histogram smoothing method, which is one of histogram smoothing methods, is used as a technique for improving image quality of the original image.

In general, histogram equalization improves an image by uniformizing the distribution of contrast values by processing an image in which the distribution of contrast values is biased or uneven to one side. The ultimate goal of histogram smoothing is to produce a histogram with a uniform distribution, and to uniformize the distribution of the histogram during processing. At this time, the brightness can be significantly changed according to the input image, and unwanted noise can be amplified. Therefore, the contrast can be increased while maintaining the average brightness.

As such, the histogram processing method is a simple way to solve the deterioration of the image quality, so there are various methods. Typical examples include Bi Histogram Equalization, Recursive Mean-Separate Histogram Equalization, and Clipped Histogram Equalization.

Among them, the Clipped Histogram Equalization (CHE) method is the most effective and maintains the amount of information in the image without distortion of the image. This method controls the maximum value of the histogram by setting an arbitrary maximum value, cutting off the top portion of the histogram above the maximum value and resetting it over the entire area of the threshold. It should be set to have a minimum range after histogram conversion, and dynamic thresholds according to image feature changes can be set by assigning thresholds to initial settings according to images. In this case, because the upper part of the histogram is reallocated for the entire area, it is noise-resistant, but in normal video, contrast improvement results in inefficiency compared to other methods.

Therefore, the present invention does not reset the top portion of the histogram to the entire region, and divides the histogram section into several sections and evenly distributes the histogram section to the peripheral section of the histogram section by distance ratio, while maintaining a strong noise point. As a method to improve the contrast of the image, we propose an improved A_CHE method of CHE.

As a result, the low illuminance region can be improved from the dynamic range and can be processed as an improved image that is robust to noise, and the high illuminance region is processed as a suppressed enhancement image rather than an extended region distribution.

On the other hand, in step S42 is to detect and remove the smear from the original image using the image processing.

After receiving the original image from the photographing module 10, the input original image is analyzed statistically. As illustrated in FIG. 6, the extractor of the detector 62 may curve the input original image into a signal distribution with respect to a column unit signal. The signal distribution curve represents a sum of gray values of a plurality of pixels constituting each column constituting the original image.

In addition, as illustrated in FIG. 7, the converter of the detector 62 may convert the signal distribution curve of the input original image into a normal distribution curve. In other words, if a smear is generally generated at a specific place from the source of sunlight or passive light by a vehicle, it can be expressed as a normal distribution.

After the original image is expressed in a normal distribution, it is determined whether a smear is present. Due to the characteristics of the smear, it is generated as a column in the image. In particular, since it has a white and bright shape, it can be determined that smear is generated in sections of the heat having a white and bright shape.

Accordingly, the sum of the gray values and the column distribution curve, that is, the sum of the smears along the direction of the distribution occurring in the smear and other sections of the signal distribution curve, is found and the signal distribution In the curve, when there is a part that has a specific and significantly higher frequency than other parts in the normal distribution, it may be determined that the smear has occurred in the original image.

After it is determined that there is a smear in the original image, the position of the smear is determined. The location of the smear may be determined by determining that the smear has occurred in the signal distribution curve of the original image having a specific and significantly higher frequency than the other parts.

After determining that the smear area exists and determining the location, the smear is removed and a binary pattern map (alpha map) for restoring is generated.

The smear removal method is performed by estimating smear strength and accurate pure background strength. A binary pattern map is generated by applying an average filter to signal strength in each column of the original image.

In this case, the binary pattern map has a value of 1 when the signal intensity on the normal distribution is greater than a preset threshold, and when the value is small, the column has a value of 0.

After the binary pattern map is generated, the smear positions are rearranged using the binary pattern map. When analyzing each pixel inverse, it consists of vehicle, noise, background and smear signal. By reconstructing the smear area search size to align the gray values of pixels in a column using an applied filter, an operation of estimating the intensity of the smear signal and determining and relocating the exact position is performed.

After repositioning the smear, the smear is removed. Using the determined smear area and intensity, the smear can be removed from the entire image.

After removing the smear, the original image is restored. There are various methods for restoring the original image, but in the present invention, inpainting is applied. In particular, the original image may be restored using interpolation, but is not suitable for an area, and thus the original image may be restored using a patch method having a predetermined size in the vicinity.

Subsequently, the image reconstruction unit generates a reconstructed image by applying a focus degradation method combined with a high resolution image generation method and a deterring method to the target image (S50).

In detail, in the step S50, a super-resolution image is generated by up-scaling according to an up-scale coefficient in a target image having deterioration of focus, and generating a super-resolution image. High resolution images are calculated by applying bicubic interpolation to the generated super resolution images. The process can be repeated according to the value of the predetermined coefficients, preferably until the deterioration of focus is no longer improved.

After repeatedly performing the above process, a high resolution image obtained by removing a part of the focus deterioration can be obtained, and deterring using a direction adaptive guided filter to restore the generated high resolution image to a clear image having a visually good quality. Use the method.

However, the step S50 is not an essential step, it is also possible to proceed to step S60 with the step S50 omitted.

Subsequently, the number recognition module 20 recognizes a character of the license plate of the vehicle by using the target image (S60).

For character recognition, one of three detection methods of vehicle license plate position may be executed first. The first is to detect the feature region of the license plate using vertical and horizontal edge information from the captured image. The second is to detect the position of the license plate by scanning data analysis. The third is to search the numbers and letters directly to detect the correct license plate.

When the location of the license plate is detected, the character recognition (Korean consonant + number) by template matching, the character recognition (Hangul vowel) based on structural features, and the internal recognition algorithm uses numbers, letters (consonants, vowels) to reduce misunderstanding. By classifying features in detail and reconfirming the recognized character, errors in character decryption can be minimized.

Hereinafter, the smear processing of the high illuminance image processor 60 will be described in detail.

The smear is present in the CCD camera due to its characteristics and is obtained from the same position or strong light (light source) due to the reflector from the light (light source) according to the position of the camera and the subject (vehicle), resulting in a distorted image.

The proposed method finds the smeared column in the still image to remove smears, and the high intensity component only for the lowest region of the ROI to reduce the amount of computation. When the component distribution has a certain value or more, it is determined that a smear exists.

In reality, however, it is difficult to find the exact location. This is because the distribution of photons reaching from the sun to the subject occurs irregularly in the object, background, noise and smear areas, and exists and occurs more than one.

Therefore, in order to analyze the characteristics of the smear, it is determined that the smear area exists in the sections of the rows having white and bright shapes transmitted to the supersaturated object, and the gray characteristics are analyzed and detected. In addition to the smear, the sum of gray values and a distribution curve of the distribution along the direction of the distribution generated in other sections, that is, the peak value of the sum of the smears are found. Other areas of the curve show relatively similar or even distributions of the background and vehicle components.

First, the smear intensity intensity curve model may be expressed as Equation 9 below.

Where r × c is the size of the original image, and r and c represent rows and columns, respectively. C _S (j) is the gray value in the column j _th , and f _{i, j} (x, y) represents the gray value for the pixel in (i, j).

On the other hand, the threshold set for the smear area, T _hs is as shown in Equation 10 below.

Where μ is the mean according to statistical analysis in the whole image, σ is the standard deviation, and ω is the weight.

Regarding the correlations of Equations 9 and 10, as described above, since the smear phenomenon occurs at the highest estimate of the gray sum curve, the smear area vector I _mask can be expressed as Equation 11 below. have.

In other words, it is determined that the smear brightness intensity is greater than the threshold value as the smear area.

After finding the smear position coordinates, we can obtain the starting coordinates p _l (n) and the ending coordinates p _r (n), which are associated with the variable n.

However, the smear generation area can be grabbed by an area wider than the start and end coordinates thus obtained. For example, as shown in Equation 12 below, up to 2 pixels before the start coordinate and 2 pixels after the end coordinate can be determined as the smear generation area.

here,

and

Means the result for the smear area.

In addition, the smear may be detected and removed by estimating the intensity of the smear in the smeared image and the background intensity of the smear removed state.

The average filter is applied to the signal strength in each column, and the method of reconstructing the smear area search size to align the gray values of the pixels in the column using the applied filter is used.

Equation 13 is an equation relating to an average filter applied to the signal strength of each column.

Here, p is an intermediate position of the image data corresponding to the coordinate in the search size, d is a radius of the selected location area, and I 'is a vector aligned at the selected search size.

In Equation 13 above, when analyzing each pixel column, the gray values of the pixels are aligned, including the vehicle, noise, background, and smear signal.

According to the above method, the intensity of the I _S (i) smear and the I _b (i) background component can be accurately obtained.

It is possible to estimate the smear intensity, to determine the difference from the pure background intensity, and to determine the location and area of the smear.

After determining the intensity and area of the smear, the smear is removed from the entire image.

here,

Is the i _th th image from which the smear is removed. That is, to remove the smear I _S (i) from the entire image.

Through the above method, the smear may be detected in the image including the smear, the smear position may be detected, and the smear may be removed from the image.

The removed smear result and the area (position) are detected to restore an image that is not distorted from the binary pattern map (binary pattern map or alpha map) generated. To deal with this, inpainting is applied as a technique of reconstructing the lost part of the image among various restoration methods.

In general, the simplest method is interpolation using surrounding values. This is simply applicable if the missing area is not large, but interpolation for the area is not suitable because of the propagation of the error. Therefore, the process is performed until all the occlusion areas on each layer are interpolated. In addition, for the pixels on the boundary of the region to be interpolated, the interpolation priority is calculated, and the texture and the structure interpolation are performed from the priorities. It is determined by the product of the confidence of the center point of the patch centered on the pixel on the boundary and a value related to the structure within the patch.

In Equation 15, P (p) denotes the priority of the pixel p to be interpolated, C (q) denotes a reliability value of the pixel in the patch, and O denotes an area to be interpolated. W _p means patch,

Is the size of the patch,

Denotes a direction unit vector of the structure in the image, and n _p denotes a normal unit vector with respect to a contour at the pixel p. α is a normalization constant, and the reliability of the pixel is set to 1 for pixels included in the source region S, and to 0 otherwise.

Simply summarizing the meaning of the P (p) value, the higher priority is given to pixels in the direction that match the direction of the structure in the image, so that the structure can be reconstructed more correctly. This is a priority value that enables more accurate interpolation by allowing interpolation from the most distributed position.

When the highest priority is determined, blending with the patch area on the target pixel is made by borrowing an area where the similarity is maximum by comparing the gray intensity with a patch within a certain range through template matching for the pixel. )

In the present invention, the sum of absolute difference (SAD) in gray-scale space is used as a template matching method. Finally, process C recalculates the highest priority among pixels on the boundary and repeats process A to C until all target pixels are interpolated.

Hereinafter, the result of actually applying the present invention will be described with reference to the drawings. 9 to 11 and 12 to 14 are examples showing a result of processing a low light image according to the present invention, Figures 15 to 17 and 18 to 20 are smear generated in the high light image according to the present invention It is an example showing the result of the restoration process.

Here, the vehicle image input from the 1.3M camera (PointGray) in the LPR system was used and converted to gray scale for processing. The experimental environment was implemented to enable real-time processing using Visual Studio 2010 compiler in Windows 7, CPU 2.8GHz, 4G memory.

9 to 11 and 12 to 14 are experimental results for comparative analysis between the conventional HE method and the proposed A_CHE method. 9 is an actual input image, FIG. 10 is a result image by the conventional HE method, and FIG. 11 is a result image by the proposed A_CHE method. 12 is an actual input image, FIG. 13 is a result image by the conventional HE method, and FIG. 14 is a result image by the proposed A_CHE method.

9 to 11 and 12 to 14, although the HE result image is obtained with respect to the actual input image, all of the results are improved, but the processing for the contrast and the low light region still exists in a distorted state. . This is because when the distribution is reconstructed into equal regions in the still image, both low light and high light areas are processed, especially because amplified and expanded values occur in the low light area and the high light area. However, in the A_CHE method, which is an improvement over the conventional HE method, the reconstructed state of the dynamic range is stabilized in the low light and high light areas, and the distortion information is significantly reduced even in the still image. Although an objective component is required, the object of the present invention is that the vehicle number recognition is important in the LPR system, so it is a low light environment in which the license plate number is not visible in the input image when viewed with the naked eye. The numbers are clear and sharp enough to be distinguished.

15 to 17 and 18 to 20 are experimental results for analyzing the smear treatment. 15 is an actual input image, FIG. 16 is a smear detection result, and FIG. 17 is a smear restoration result. 18 is an actual input image, FIG. 19 is a smear detection result, and FIG. 20 is a smear restoration result.

15 to 17 and 18 to 20, since smear is generated in a region where the light of the light source is strong from the vehicle or the reflector of the vehicle, the smear region is detected after calculating the distribution of the strong region. The detected area was set to be detected wider than the smear distribution, and the area of the smear area was reconstructed using the inpainting technique. As a result, in the smear area, some areas remain in the strong area, that is, the widely distributed area, but the smear decreased significantly.

In the above-described present invention, information such as low light distribution image, color distortion, image blurring, noise, smear, etc., which are one of the existing problems in the LPR system, is set in advance by adjusting the shutter or aperture value of the camera or finding an optimized value. Operated. Because of this, it was a major issue that would be a problem in the performance of the system without considering the LPR installation place or the surrounding environment, but by utilizing the low light image improvement and smear restoration processing proposed by the present invention, the stability and number recognition performance of the system can be improved. It is expected to contribute greatly.

On the other hand, the present invention can also be embodied as computer readable codes on a computer readable recording medium. Computer-readable recording media include all kinds of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disks, optical data storage devices, and the like, which are also implemented in the form of carrier waves (for example, transmission over the Internet). Include.

The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. In addition, functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers in the art to which the present invention belongs.

In addition, the above-described apparatus and method may not be limitedly applied to the configuration and method of the above-described embodiments, and the embodiments may be selectively combined in whole or in part in each of the embodiments so that various modifications may be made. It may be configured.

Claims (17)

1. A photographing module equipped with a CCD sensor and photographing an original image including a license plate of a vehicle using the CCD sensor; And

   And a number recognition module for receiving the original image photographed by the photographing module in order to recognize a character of the license plate of the vehicle.

   The number recognition module,

   A discriminating unit classifying the original image into one of a low illumination image, a high illumination image, and an unprocessed image based on a discrimination factor associated with the original image; And

   And an image processor configured to generate a corrected image by using the original image.

   The image processor,

   A low light image processor configured to generate the corrected image from the original image by using an improved clipped histogram equalization method when the original image is classified into the low light image by the determination unit; And

   And a high illuminance image processor for generating the corrected image by removing a smear generated in the original image when the original image is classified into the high illuminance image by the determination unit.

   The number recognition module uses the target image for character recognition of the license plate of the vehicle,

   When the original image is classified into the low illumination image or the high illumination image by the determination unit, the target image is the correction image,

   And the target image is the original image when the original image is classified as the unprocessed image by the discriminating unit. The apparatus for processing low-light image enhancement and smear restoration based on adaptive probability in an LPR system.
2. The method of claim 1,

   The discrimination factor is the intensity of light of the original image converted to gray scale,

   When the change in the intensity of light of the original image is higher than a predetermined threshold, the determination unit classifies the original image as the unprocessed image. Device.
3. The method of claim 2,

   When the change in the intensity of light of the original image is lower than the threshold,

   The determining unit may classify the original image as the low light image when the intensity of light of the original image is less than or equal to a predetermined first value, and when the intensity of light of the original image is higher than the first value, increase the height of the original image. Apparatus for adaptive low-light image enhancement and smear restoration in the LPR system, characterized in that it is classified into a degree image.
4. The method of claim 1,

   The improved cutting histogram smoothing scheme,

   Determine an adaptive cutting ratio for the original image, generate a cutting histogram from which an upper region of the histogram of the original image is removed according to the determined adaptive cutting ratio, and cut at least a portion of the upper region Adaptive probability-based low light image enhancement and smear reconstruction processing apparatus, characterized in that to generate the corrected image by reallocating to a cutting histogram.
5. The method of claim 4, wherein

   The adaptive cutting ratio is determined by the following equation, low probability image enhancement and smear restoration processing apparatus based on adaptive probability in the LPR system.

   Equation

   

   In the above equation, adaptive_α_ratio is the adaptive truncation ratio, f (x, y) is a gray value of the original image,

   

   to be.
6. The method of claim 4, wherein

   The cut portion of the upper region,

   Apparatus for adaptive low-light image enhancement and smear reconstruction processing in the LPR system, characterized in that it comprises a cropping range for the low light distribution area and a cropping range for the high light distribution area.
7. The method of claim 6,

   The low light distribution area is determined according to Equation 1 below,

   The high illuminance distribution region is determined according to Equation 2 below, Apparatus for adaptive low-light image enhancement and smear reconstruction processing based on the probability in the LPR system.

   Equation 1

   

   Equation 2

   

   In Equations 1 and 2, GL is the low light distribution area in the upper region, GH is the high illuminance distribution region in the upper region, and T is a value set arbitrarily to distinguish low and high illuminance. , GlobalLevel is the global level of the original image.
8. The method of claim 7, wherein

   The cutting range for the low light distribution area is determined according to Equation 3 below.

   The cropping range for the high illuminance distribution region is determined according to Equation 4 below. Apparatus for adaptive low illuminance image enhancement and smear restoration in an LPR system.

   Equation 3

   

   Equation 4

   

   In Equation 3 and Equation 4, GLL is a cutting range for the low light distribution area, GHH is a cutting range for the high light distribution area, C _P is the cutting histogram, and k _low is a gray scale. The low illuminance distribution region, k _high is the _high illuminance distribution region at gray scale, and k is the number of sums of gray scales.
9. The method of claim 1,

   The high illuminance image processor,

   A detector for detecting a position of a first column in which a smear is generated among columns constituting the original image; And

   And a removal unit for removing the smear from the original image based on the detected position information of the first column. The apparatus of claim 1, further comprising an adaptive probability-based low light image improvement and smear restoration apparatus in an LPR system.
10. The method of claim 9,

    The detection unit,

    An extraction unit for extracting a signal distribution curve for each column constituting the original image by using the original image input to the number recognition module; And

    A conversion unit for converting the signal distribution curve into a normal distribution curve; further comprising:

    And the signal distribution curve represents a sum of gray values of a plurality of pixels constituting each column constituting the original image, wherein the low probability image enhancement and smear reconstruction processing is adaptive based on LPR system.
11. The method of claim 10,

    The detection unit generates a binary pattern map by comparing the normal distribution curve with a preset threshold,

    In the region where the normal distribution curve is smaller than the threshold, the binary pattern map has a value of 0, and in the region where the normal distribution curve is larger than the threshold, the binary pattern map has a value of 1.

    And an area in which the binary pattern map has a value of 1 corresponds to the first column of the original image.
12. The method of claim 11,

    The detection unit,

    Expanding the width of the first column by adding a portion of the column adjacent to the first column to the first column,

    The removal unit,

    Subtracting gray values of each of the plurality of pixels constituting the binary pattern map from gray values of the plurality of pixels constituting the original image, and removing the smear generated in the first column using the subtracted result. Low probability image improvement and smear restoration processing apparatus based on adaptive probability in an LPR system.
13. The method of claim 9,

    And a reconstruction unit for reconstructing the original image of the first column from which the smear is removed using a predetermined interpolation method.
14. The method of claim 13,

    The interpolation method of the restoration unit,

    Calculating a priority for each of the plurality of pixels in the patch, determining a pixel of the highest priority having the highest priority among the calculated priorities, wherein the first of the pixels of the highest priority and the plurality of pixels in the patch Reconstruction is performed by comparing the similarity of pixels that do not constitute a column.

    The patch includes at least a portion of the pixels constituting the first column, adaptive probability-based low light image enhancement and smear restoration processing apparatus in the LPR system.
15. The method of claim 1,

    The number recognition module,

    And an image restoring unit configured to generate a restored image by applying a focus degradation method to the target image.

    The reconstructed image is used for character recognition of the license plate of the vehicle of the number recognition module,

    The focus deterioration method is a combination of a high resolution image generation method and a detailing method,

    The image reconstruction unit for performing the high resolution image generation method,

    Image estimating means for up-scaling a target image having deterioration in focus according to a predetermined up-scale coefficient; And

    And image generating means for generating a high resolution image from which at least a portion of the focal deterioration is removed by applying bicubic interpolation to the target image and the up-scaled image. Adaptive Probability Based Low Light Image Enhancement and Smear Reconstruction System
16. The method of claim 15,

    The image reconstruction unit for performing the deterring method,

    And image restoring means for removing at least a portion of the deterioration of the focus from the generated high resolution image using a direction-adapted guided filter. Image improvement and smear restoration processing device.
17. A photographing step of photographing, by a photographing module equipped with a CCD sensor, an original image including a license plate of a vehicle using the CCD sensor;

    An input step of inputting the original image photographed by the photographing module to a number recognition module to recognize a character of the license plate of the vehicle;

    A discriminating step of classifying, by the discrimination unit of the number recognition module, the original image into one of a low light image, a high light image, and an unprocessed image based on a determination factor related to the original image;

    An image processing step of generating, by the image processing unit of the number recognition module, a corrected image using the original image; And

    And recognizing, by the number recognition module, a character of the license plate of the vehicle using a target image.

    The image processor,

    When the original image is classified into the low light image by the determination unit, the low light image processor and the determination unit to generate the corrected image from the original image by using an advanced clipped histogram equalization method. If the original image is classified as the high illuminance image, the high image processing unit for removing the smear generated in the original image to generate the correction image, further comprising:

    When the original image is classified into the low illumination image or the high illumination image by the determination unit, the target image is the correction image,

    And if the original image is classified into the unprocessed image by the discriminating unit, the target image is the original image. An adaptive probability based low light image improvement and smear restoration processing method in an LPR system.

Images