CN106485182B - A kind of fuzzy Q R code restored methods based on affine transformation - Google Patents
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Abstract
The invention discloses provide a kind of to be combined traditional image recovery method with the method that QR code features are calibrated, traditional images restored method is utilized and obtains a preferable image of quality, the characteristics of QR codes are utilized again obtains the QR codes of a standard, more quickness and high efficiency is that automatic guide vehicle provides accurate QR codes positioning image, and obtains the fuzzy Q R code motion blur image restoration methods based on affine transformation of the posture information in automatic guide vehicle operational process.The present invention utilizes multi-resolution hierarchy, obtains ROI image, the image restoration time can be greatly lowered.QR codes by calibration are very high with the QR code similarities of standard, it is ensured that identification.
Description
Technical field
The invention belongs to image processing fields, and in particular to a kind of AGV locating module motion blurs recovery based on QR codes
Method.
Background technology
With the development of industrial automation technology, automatic guide vehicle (AGV) is widely used, and automatic guided vehicle can be by
It according to the route automatic running of instruction, need not artificially interfere, therefore industrial automation can be effectively improved, improve conevying efficiency.
Existing automatic guide vehicle air navigation aid is mainly the methods of magnetic stripe guiding, laser aiming, but these methods are all
There is the limitation of itself, in recent years, with the development of science and technology, also there are some new location and navigation technologies, is based on Quick Response Code
The automatic guide vehicle air navigation aid of positioning, the visual guidance method can effectively improve the soft of the path design of automatic guide vehicle
Property, but since two-dimension code structure is more complicated, automatic guided vehicle during the motion the image collected be it is fuzzy, can not
Direct Recognition needs motion blur image restoration, common motion blur image restoration algorithm.
Image restoration is that a hot spot technology, its purpose are to restore from the observed image to degrade really in recent years
Image, mathematical model can be described as formula (1)：
G (x, y)=f (x, y) * h (x, y)+n (x, y) (1)；
Wherein, x, y are image space coordinates, and the image upper left corner is origin, and g (x, y) is the observed image to degrade, f (x, y)
It is true picture, h (x, y) is degradation model, and n (x, y) is additive noise.By Fourier transformation, which can be in frequency domain table
It is shown as formula (2)：
G (u, v)=F (u, v) H (u, v)+N (u, v) (2)；
Wherein, u, v indicate discrete frequency coordinate, G (u, v), F (u, v), H (u, v), N (u, v) be respectively g (x, y), f (x,
Y), the Fourier transformation of h (x, y), n (x, y), H (u, v) be also known as point spread function (point spread function,
PSF), most important two parameters are blur direction and fuzzy distance respectively wherein in PSF.Image restoration is exactly to find one again
Former filter obtains the estimation of f (x, y)
It is restored to solve motion blur, also occurs some image recovery methods in recent years, but automatic guided vehicle exists
It is linear uniform motion in the course of work, i.e. the direction of motion is all known with speed, such as China Patent No.
Double iterative mixing blind restoration methods that CN201510445795.1 is announced, iterative algorithm processing time is long, recovery effect
It is bad, two parameters of known PSF are not made full use of, it cannot be guaranteed that the absolutely identification of Quick Response Code, discomfort share
The image recovery method for making automatic guided vehicle will make Quick Response Code None- identified, lead if the speed of automatic guided vehicle is slightly fast
Cause cannot be positioned effectively, and the scheduling for influencing automatic guided vehicle is realized, or even will produce derailing, causes inevitably to lose.
Invention content
In view of the above-mentioned drawbacks of the prior art, the purpose of the present invention is to provide a kind of by traditional image restoration side
Method is combined with the method that QR code features are calibrated, that is, traditional images restored method is utilized and obtains a preferable image of quality, again
The characteristics of QR codes are utilized obtains the QR codes of a standard, and more quickness and high efficiency is that automatic guide vehicle provides accurate QR codes calmly
Bit image, and obtain the fuzzy Q R codes movement mould based on affine transformation of the posture information in automatic guide vehicle operational process
Paste image recovery method.
For achieving the above object, the technical scheme is that：A kind of fuzzy Q R codes recovery based on affine transformation
Method includes the following steps：
Step (1) calculates the region comprising QR codes in the motion blur image of parked using the constant principle of moments of Hu
ROI image；Step includes：
Step (1.1), it is down-sampled three times downwards to motion blur image using Gaussian image pyramid；
Step (1.2) detects the marginal information of down-sampled image downwards, calculates the Hu of all marginal informations not bending moment, root
According to Hu, bending moment does not obtain the edge that size meets, the edge as QR codes；
Step (1.3) includes the edge obtained in step (1.2) with minimum rectangle, obtains four angle points of minimum rectangle,
It is multiplied by 3 respectively, the minimum for obtaining QR codes in motion blur image includes rectangle, and segmentation image obtains ROI image；
Step (2), it is fuzzy multiple that the ROI image obtained to step (1) with traditional image recovery method carries out preliminary motion
It is former；Step includes：
Step (2.1) obtains the motion blur direction of ROI image according to the direction of motion of automatic guided vehicle；
Step (2.2) obtains the motion blur distance of ROI image according to the movement velocity of automatic guided vehicle；
Step (2.3), the db4 wavelet transformations that the number using vanishing moment is 4, obtains 4 sub- band diagrams of ROI image
Picture, be respectively the low frequency part LL of original image, the vertical low frequency part HL of horizontal high-frequent of original image, original image horizontal low frequencies hang down
The diagonal high frequency section HH of straight high frequency section LH, original image；According to step (2.1), step motion blur side is obtained in (2.2)
To with motion blur distance, using Wiener filtering to LL motion blurs restore, using Laplace operator to the edge LH, HL, HH
It keeps；
4 sub- band images are reconstructed into the ROI image of recovery by step (2.4) using wavelet inverse transformation；
Step (3), using run-length encoding, four angle points of QR codes, step include in the ROI image restored：
Step (3.1), according to grey level histogram, using Otsu threshold method, by the ROI image binaryzation of recovery；
Step (3.2) dispels the salt-pepper noise and ringing effect in binaryzation ROI image using median filter；
Step (3.3) calculates the run-length encoding of binaryzation ROI image, and the characteristics of according to QR codes, binaryzation is calculated
Four angle points of QR codes in ROI image；
Step (4) is calculated the binaryzation just put and restores QR codes using Homography matrixes and Principle of Affine Transformation
Image, step include：
Step (4.1) obtains QR codes in blurred picture and compares true QR codes according to size of the QR codes in blurred picture
Four angle points of amplification are calculated in amplification factor；
Step (4.2) utilizes four obtained in four angle points and step (4.1) of the QR codes obtained in step (3.3)
The linear solution of Homography matrixes is calculated in angle point；
The recovery QR codes just put are calculated using Principle of Affine Transformation in step (4.3)；
The recovery QR codes just put are divided into N*N module by step (5), the characteristics of according to QR codes, traverse N*N mould
Current block is assigned a value of black or white by block according to the feature of each module.
The beneficial effects of the invention are as follows：
The advantageous effect of the present invention compared with prior art is：
1, the present invention utilizes multi-resolution hierarchy, obtains ROI image, the image restoration time can be greatly lowered.
2, the present invention utilizes wavelet transformation, can improve the robustness of image restoration, improves image restoration effect.
3, the QR codes by calibration are very high with the QR code similarities of standard, it is ensured that identification.
4, algorithm is simple, it is easy to accomplish, it is real-time.
5, present invention is particularly suitable for use QR codes to do the motion blur in the automatic guided vehicle course of work of locating module
Image restoration.
Term is explained：
QR codes are one kind of two-dimensional bar code, and QR comes from the abbreviation of English " Quick Response ", the i.e. meaning of fast reaction
Think, wishes that QR codes can allow its content quickly to be decoded from inventor.QR codes can store additional information than common bar code, also be not necessarily to
As common bar code adjusting to a line scanner is needed in scanning.QR codes can quickly be read, compared with bar code before, QR codes
More rich information can be stored, including to word, the addresses URL and other kinds of data encryption.
ROI is region of interest abbreviations, refers to that sample determination is used for selecting in the data set of special-purpose
Subset.The concept of one ROI is common in many application fields, is exactly some use of given image in image processing field
Data in the region of special-purpose.
Description of the drawings
When considered in conjunction with the accompanying drawings, by referring to following detailed description, can more completely more fully understand the present invention with
And be easy to learn the advantage that many of which is adjoint, but attached drawing described herein is used to provide further understanding of the present invention,
The part of the present invention is constituted, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, does not constitute to this hair
Bright improper restriction, wherein：
Fig. 1 is gaussian pyramid down-sampled image downwards three times in embodiment step (1)；
Fig. 2 is in embodiment step (1) according to the Hu satisfactory profiles that bending moment does not obtain；
Fig. 3 is the ROI image that segmentation obtains in embodiment step (1)；
Fig. 4 is that motion blur restores ROI image in embodiment step (2)；
Fig. 5 is affine transformation schematic diagram in embodiment step (4)；
Fig. 6 is the recovery QR codes just put in embodiment step (4)；
Fig. 7 be Image Segmentation Methods Based on Features in embodiment step (5) according to QR codes at module map；
Fig. 8 is the restoration result to blurred picture in embodiment step (5).
Specific implementation mode
Below in conjunction with attached drawing to the present invention, technical scheme in the embodiment of the invention is clearly and completely described.
The present invention includes five steps, and concrete methods of realizing is as follows：
Step (1), using the constant principle of moments of Hu, calculate the region comprising QR codes in the motion blur image of parked
ROI (region of interest) image.Concrete methods of realizing is as follows：
Step (1.1) utilizes Gaussian image pyramid, is sampled three times downwards to motion blur image, downwards down-sampled knot
Fruit as shown in Figure 1, gaussian pyramid down-sampled formula (1) is downwards：
Wherein x, y are image space coordinates, and the image upper left corner is origin (0,0), Gn+1(x, y) is (n+1)th grade of down-sampled figure
Picture, Gn(x, y) is n-th grade of down-sampled image, and W (a, b)=W (a) * W (b) are the Gaussian convolution core that length is 5, and W (a) is x=a
The value of Gaussian convolution core at point, W (b) are the value that x=b points out Gaussian convolution core.
Step (1.2) detects the marginal information of down-sampled image downwards using Canny operators, calculates the Hu at all edges not
The formula (4) of bending moment, Hu not bending moments is as follows：
Wherein p=0,1,2......, q=0,1,2......, m00For the area of geometric moment.
The area for calculating all edges that Canny operator edge detections obtain, obtains wherein satisfactory edge, regards
It is the edge of QR codes, the edge of QR codes is as shown in Figure 2.
Step (1.3) includes the edge obtained in step (1.2) with minimum rectangle, obtains four angle points of minimum rectangle,
It is multiplied by 3 respectively, the minimum for obtaining QR codes in motion blur image includes rectangle, and it is as shown in Figure 3 that segmentation image obtains ROI image.
Step (2) carries out preliminary motion smear restoration with traditional image recovery method to ROI image.It implemented
Journey is as follows：
Step (2.1), automatic guided vehicle are moved along the directions y always, so the motion blur direction of ROI image is 90 °.
The movement velocity of step (2.2), automatic guided vehicle is v, and the time for exposure of industrial camera is t, so ROI image
Fuzzy distance be vt.
Step (2.3), " db4 " small echo that the number using vanishing moment is 4, scale coefficient are：
{0.325803,1.010946,0.892200,-0.039575,-0.264507,0.043616,0.023252,-
0.014987}
Wavelet transformation is provided by formula (5) and (6)：
WhereinIt is the of original image respectively
j0Value, value, the level of the vertical low frequency part of horizontal high-frequent (HL) of x=m and y=n low frequency parts (LL) in four frequency sub-band of grade
The value of low frequency vertical high frequency section (LH), the value of diagonal high frequency section (HH), H, V, D are respectively intended to three high frequency frequencies of mark
Section.M and N is the width and height of image resolution ratio,
For jth0Grade scaling function andValue of the j-th stage wavelet function at x=m, y=n,
Calculating process is as follows：
X, y are indicated with t, whereinFor scaling function,
For wavelet function.
H (n) is scale coefficient, and " db4 " wavelet scale coefficient that the number of vanishing moment is 4 is：
{0.325803,1.010946,0.892200,-0.039575,-0.264507,0.043616,0.023252,-
0.014987}
LL frequency sub-band is restored using Wiener filtering, Wiener filtering is given by：
WhereinIt isFourier transformation, H*(u, v) is the conjugate matrices of h (x, y) Fourier transformation, and K is
Special constant usually takes less than 1.
HL, LH, HH frequency sub-band are kept using Laplace operator edge respectively, Laplace operator is as follows：
According to treated LL, HL, LH, HH frequency sub-band, reconstruction image, as shown in Figure 4.
Step (3), using run-length encoding, four angle points of QR codes in the ROI image restored.Specific implementation process
For：
Step (3.1), according to grey level histogram, using Otsu threshold method, by the ROI image binaryzation of recovery.
Step (3.2) dispels the salt-pepper noise and ringing effect in binaryzation ROI image using median filter.
Step (3.3) calculates the run-length encoding of binaryzation ROI image, and the characteristics of according to QR codes, binaryzation is calculated
Four angle points of QR codes in ROI image, such as A in Fig. 51、A2、A3、A4, wherein XS-axis、YS- axis is binaryzation ROI image
Coordinate system, coordinate origin are O (xs,ys)。
Step (4) is calculated the binaryzation just put and restores QR codes using Homography matrixes and Principle of Affine Transformation
Image.Wherein affine transformation as shown in figure 5, wherein XN-axis、YN- axis is the coordinate system after transformation, coordinate origin O'
(0,0), A1'、A'2、A'3、A'4For four angle points of the QR codes after transformation.
Step (4.1) obtains QR codes in blurred picture and compares true QR codes according to size of the QR codes in blurred picture
Four angle points of amplification are calculated in amplification factor；
Step (4.2) utilizes four obtained in four angle points and step (4.1) of the QR codes obtained in step (3.3)
The linear solution of Homography matrixes is calculated in angle point；
The recovery QR codes just put are calculated using Principle of Affine Transformation in step (4.3)；
Affine transformation is given by：
Wherein H is Homography homography matrixs, it is known that Homography homography matrixs are the invertible matrix of 3*3, are to use
It calculates projected position of the point in different two dimensional images on same three-dimensional planar, is an one-to-one mapping, and H
For each point in input picture, there are correct mapping relations.Calculate 4 pairs of the linear solution needs of Homography not
Conllinear point.4 pairs of not conllinear points are provided by step (3.3) and step (4.1).
According to Principle of Affine Transformation, it is as shown in Figure 6 that the recovery QR codes just put are calculated.
The recovery QR codes just put are divided into N*N module by step (5), and the QR codes for the Class1 that the present embodiment is selected are
21*21 module, as shown in Figure 7.The each module for traversing QR codes, calculates the average gray of all the points in each module, if flat
Equal gray scale thinks that this module is white more than 150, is otherwise black.Obtain fuzzy Q R code restoration results as shown in Figure 8.It can
It is seen compared to other restoration algorithms, obtains being a QR code close to standard.
Described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on the present invention
In embodiment, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
Claims (1)
1. a kind of fuzzy Q R code restored methods based on affine transformation, which is characterized in that include the following steps：
Step (1) calculates the ROI figures in the region comprising QR codes in the motion blur image of parked using the constant principle of moments of Hu
Picture；Step includes：
Step (1.1), it is down-sampled three times downwards to motion blur image using Gaussian image pyramid；
Step (1.2) detects the marginal information of down-sampled image downwards, the Hu of all marginal informations not bending moment is calculated, according to Hu
Bending moment does not obtain the edge that size meets, the edge as QR codes；
Step (1.3) includes the edge obtained in step (1.2) with minimum rectangle, obtains four angle points of minimum rectangle, respectively
3 are multiplied by, the minimum for obtaining QR codes in motion blur image includes rectangle, and segmentation image obtains ROI image；
Step (2), the ROI image obtained to step (1) with traditional image recovery method carry out preliminary motion smear restoration；Step
Suddenly include：
Step (2.1) obtains the motion blur direction of ROI image according to the direction of motion of automatic guided vehicle；
Step (2.2) obtains the motion blur distance of ROI image according to the movement velocity of automatic guided vehicle；
Step (2.3), the db4 wavelet transformations that the number using vanishing moment is 4, obtains 4 sub- band images of ROI image, point
Be not original image low frequency part LL, the vertical low frequency part HL of horizontal high-frequent, the horizontal low frequencies of original image of original image it is vertically high
The diagonal high frequency section HH of frequency part LH, original image；According to step (2.1), step motion blur direction and fortune are obtained in (2.2)
Dynamic fuzzy distance, restores LL motion blurs using Wiener filtering, is kept to the edge LH, HL, HH using Laplace operator；
4 sub- band images are reconstructed into the ROI image of recovery by step (2.4) using wavelet inverse transformation；
Step (3), using run-length encoding, four angle points of QR codes, step include in the ROI image restored：
Step (3.1), according to grey level histogram, using Otsu threshold method, by the ROI image binaryzation of recovery；
Step (3.2) dispels the salt-pepper noise and ringing effect in binaryzation ROI image using median filter；
Step (3.3) calculates the run-length encoding of binaryzation ROI image, the characteristics of according to QR codes, binaryzation ROI is calculated and schemes
Four angle points of QR codes as in；
Step (4) is calculated the binaryzation just put and restores QR codes figure using Homography matrixes and Principle of Affine Transformation
Picture, step include：
Step (4.1) obtains the amplification that QR codes in blurred picture compare true QR codes according to size of the QR codes in blurred picture
Four angle points of amplification are calculated in multiple；
Step (4.2), using four angle points obtained in four angle points and step (4.1) of the QR codes obtained in step (3.3),
The linear solution of Homography matrixes is calculated；
The recovery QR codes just put are calculated using Principle of Affine Transformation in step (4.3)；
The recovery QR codes just put are divided into N*N module by step (5), the characteristics of according to QR codes, traverse N*N module, according to
The feature of each module, black or white are assigned a value of by current block.
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| CN107944321A (en) * | 2017-11-28 | 2018-04-20 | 努比亚技术有限公司 | A kind of image-recognizing method, terminal and computer-readable recording medium |
| CN107885217A (en) * | 2017-12-14 | 2018-04-06 | 中国计量大学 | Visual guidance method and visual guidance car based on optimal iterations |
| US10582147B2 (en) * | 2017-12-28 | 2020-03-03 | Ademco Inc. | Systems and methods for intelligently recording video data streams |
| CN108647550B (en) * | 2018-04-11 | 2021-07-16 | 中山大学 | Machine learning-based two-dimensional code fuzzy clustering identification method and system |
| CN110400282A (en) * | 2018-04-24 | 2019-11-01 | 中国科学院沈阳自动化研究所 | A kind of high-resolution terahertz image processing method |
| CN110475079A (en) * | 2019-09-10 | 2019-11-19 | 上海快仓智能科技有限公司 | A kind of image exposure luminance regulating method, system and associated component |
| US11558548B2 (en) | 2020-05-04 | 2023-01-17 | Ademco Inc. | Systems and methods for encoding regions containing an element of interest in a sequence of images with a high resolution |
| CN111428707B (en) * | 2020-06-08 | 2020-11-10 | 北京三快在线科技有限公司 | Method and device for identifying pattern identification code, storage medium and electronic equipment |
| CN112101058B (en) * | 2020-08-17 | 2023-05-09 | 武汉诺必答科技有限公司 | Automatic identification method and device for test paper bar code |
| CN113468905B (en) * | 2021-07-12 | 2024-03-26 | 深圳思谋信息科技有限公司 | Graphic code identification method, graphic code identification device, computer equipment and storage medium |
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