CN108765305A - A kind of defocus QR code method for blindly restoring image - Google Patents
A kind of defocus QR code method for blindly restoring image Download PDFInfo
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- 238000003708 edge detection Methods 0.000 claims abstract description 18
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- 238000009795 derivation Methods 0.000 claims description 19
- 239000011159 matrix material Substances 0.000 claims description 17
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
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- G06K19/06—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
- G06K19/06009—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking
- G06K19/06037—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking multi-dimensional coding
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The invention discloses a kind of defocus QR code method for blindly restoring image, edge image is obtained after first carrying out gray processing processing and edge detection process to QR code images, then edge image is handled to obtain edge line and the maximum point of derivative value change rate, out-of-focus radius can be calculated according to edge line and the maximum point of derivative value change rate, and then point spread function can be obtained, finally fuzzy QR code images are restored according to point spread function, while restoring picture quality as far as possible, calculation amount is reduced, and reduces the time of recovery.
Description
Technical field
The present invention relates to technical field of image processing, especially a kind of defocus QR code method for blindly restoring image.
Background technology
With the rapid development of technology of Internet of things, the application of QR code images is more and more extensive.Due to QR codes image obtain,
It is easy to be influenced to thicken by various factors during compression, storage, transmission and reconstruction, is unfavorable for answering extensively for bar code
With so research obscures QR code image-recovery techniques, increasing bar code image clarity has highly important theoretical and actually answer
With value.
Defocus blur image is to carry out convolution again by clear QR codes image and point spread function (or being referred to as fuzzy core)
In addition what noise obtained, and image restoration process is then image deconvolution process or is referred to as deconvolution process, is expanded according to
Whether scattered function is it is known that image restoration can be divided into two classes：One kind is the non-blind deconvolution techniques of blurred picture, it is assumed that fuzzy
The point spread function of image to blurred picture it is known that directly carry out operation of deconvoluting；Another kind of is the blind deconvolution of blurred picture
Technology needs the priori in conjunction with blurred picture itself to restore in the case where the point spread function of blurred picture is unknown
Go out clear image, this is an indefinite sex chromosome mosaicism.
In actual application, the point spread function of QR codes acquisition system is unknown, needs to estimate an expansion simultaneously
Dissipate function and original clear bar code image, a kind of effective blind restoration method is first to estimate point spread function, by blind deconvolution
Problem is converted into non-blind deconvolution problem, to reduce image restoration difficulty, in existing traditional images blind restoration method, generally
It is that point spread function parameter is estimated by Cepstrum Method, differential correlation method and frequency domain method, it is multiple then to carry out image according to parameter
Original, these types of method is general for the smaller image effect of fog-level, if there are noises during fog-level is larger or image
When, estimation effect is bad, or even cannot use, and needs image transforming to frequency domain and analyze, computationally intensive and when restoring
Between long, the extensive use being unfavorable in actual production.
Invention content
To solve the above problems, the purpose of the present invention is to provide a kind of defocus QR code method for blindly restoring image, ensureing
While restoring picture quality, the calculation amount that can be effectively reduced in bar code image recovery process and recovery time.
Technical solution is used by the present invention solves the problems, such as it：
A kind of defocus QR code method for blindly restoring image, includes the following steps：
A, gray processing processing is carried out to the QR code images of input；
B, to gray processing, treated that QR code images intercept, and obtains edge image；
C, edge detection is carried out to edge image, obtains matrix of edge；
D, matrix of edge is scanned by column, obtains the position of edge line；
E, derivation is carried out to edge image, and the maximum point of derivative value change rate is obtained by calculation；
F, the distance between edge line and the maximum point of derivative value change rate, the out-of-focus radius estimated are calculated；
G, point spread function is calculated according to out-of-focus radius, according to point spread function to the QR codes image of defocusing blurring into
Row restores.
Further, the step A carries out gray processing processing to the QR code images of input, wherein carries out ash to QR code images
After degreeization processing, picture element matrix is expressed as：
Wherein a (i, j) be in QR code images position be (i, j) pixel value, 1<i<M, 1<j<N, N are the width of QR code images
Degree, M are the height of QR code images.
Further, to gray processing, treated that QR code images intercept in the step B, the specific steps are：To QR codes
Image is pre-processed, and intercepts 1/4 region in the QR code images upper left corner.Due to the position characteristic of QR code postings, and not
With the posting size of the QR codes of data volume, it is used as detection object by intercepting 1/4 region of the upper left corner, calculation amount can be being reduced
While do not influence the estimation of blur radius.
Further, the step C carries out in edge detection edge image, using Canny detective operators to edge image
Carry out edge detection.Compared to a variety of edge detection operators, Canny operators are preferable to the detection result of edge image.
Further, during the step D scans by column matrix of edge, matrix of edge is looked into using lookup algorithm
It looks for, by comparing until obtaining the row that serial number is 1, the as position where edge line for the first time.
Further, the step E to edge image carry out derivation the specific steps are：Secondary ask is carried out to edge image
It leads, the formula of wherein first time derivation is：
Dx (i, j)=I (i+1, j)-I (i, j)；
Dy (i, j)=I (i, j+1)-I (i, j)；
Wherein I is edge image, and I (i, j) is the value that position is at (i, j) in edge image I, acquires first derivative
Afterwards, derivation again is carried out to first derivative according to following formula：
G (x, y)=dx (i, j)-dy (i, j).
Further, the maximum point of derivative value change rate is obtained by calculation in the step E, according to the second order of edge image I
The normal direction of derivative and edge line obtains the maximum point of derivative value change rate.Derivative value variation is found in order to efficient
The maximum point of rate, it is necessary to which derivation is carried out again to the first derivative of edge image.
Further, the step G is calculated according to out-of-focus radius in point spread function, and out-of-focus radius is brought into defocus
In degradation model, point spread function is obtained, wherein defocus degradation model is：
Wherein h (x, y) is point spread function, and R is out-of-focus radius.
Further, during the step G restores the QR code images of defocusing blurring according to point spread function, expand according to
Function combination RL algorithms are dissipated to restore the QR code images of defocusing blurring.
The beneficial effects of the invention are as follows：A kind of defocus QR code method for blindly restoring image that the present invention uses, to QR code images
Edge image is obtained after carrying out gray processing processing and edge detection process, then edge image is being handled to obtain edge
Straight line and the maximum point of derivative value change rate, according to edge line and the maximum point of derivative value change rate can be calculated from
Focal radius, and then point spread function can be obtained, finally fuzzy QR code images are restored according to point spread function, are calculated
It is fast to measure small and resume speed.
Description of the drawings
The invention will be further described with example below in conjunction with the accompanying drawings.
Fig. 1 is a kind of flow diagram of defocus QR code method for blindly restoring image of the present invention；
Fig. 2 is the QR code images before not restoring；
Fig. 3 is the QR code images after edge detection；
Fig. 4 is to the relational graph after the secondary derivation of edge image；
Fig. 5 is the QR code images after restoring.
Specific implementation mode
Referring to Fig.1, a kind of defocus QR code method for blindly restoring image of the invention, includes the following steps：
A, gray processing processing is carried out to the QR code images of input；
Since the QR code images of input are different, there is certain difference in color, state and other everyways, be
Keep the recovery effect of last QR codes image preferable, it is necessary to gray processing processing is carried out to QR code images, to QR codes image into
After the processing of row gray processing, picture element matrix is expressed as：
Wherein a (i, j) be in QR code images position be (i, j) pixel value, 1<i<M, 1<j<N, N are the width of QR code images
Degree, M are the height of QR code images.
B, to gray processing, treated that QR code images intercept, and obtains edge image；
The acquiring way for being primarily due to QR code images is different, is generally obtained by shooting or scanning, so the QR codes obtained
Image can have certain redundant information, so just needing to pre-process the QR code images after gray processing, eliminate QR codes figure
The extra irrelevant information as in, and the true useful information of recovered part, to ensure the effect restored.
After pre-processing QR code images, need to intercept QR code images, due to the position of QR code postings
The posting size of the QR codes of characteristic and different data amount be used as by intercepting 1/4 region of the upper left corner and detects object, can be with
The estimation of blur radius is not influenced while reducing calculation amount.
C, edge detection is carried out to edge image, obtains matrix of edge；
After effect by comparing different edge detection operators, the present invention chooses the preferable Canny detective operators of effect
Edge detection is carried out to edge image, the target of Canny is to find an optimal edge detection algorithm, includes three steps
Suddenly：Denoising；Find the brightness step in image；Edge is tracked in the picture.
Canny detective operators are suitable for different occasions, his parameter allows root as a kind of multistage edge detection algorithm
It is adjusted according to the particular requirement of different realizations to identify different local edges, so Canny detective operators are relative to other
For edge detection operator, detection result is preferable.
D, matrix of edge is scanned by column, obtains the position of edge line L；
Specifically, during the step D scans by column matrix of edge, matrix of edge is looked into using lookup algorithm
It looks for, by comparing until obtaining the row that serial number is 1, the as position where edge line L for the first time.
E, derivation is carried out to edge image, and the maximum point Q of derivative value change rate is obtained by calculation；
Specifically, the step E to edge image carry out derivation the specific steps are：Secondary ask is carried out to edge image
It leads, the formula of wherein first time derivation is：
Dx (i, j)=I (i+1, j)-I (i, j)；
Dy (i, j)=I (i, j+1)-I (i, j)；
Wherein I is edge image, and I (i, j) is the value that position is at (i, j) in edge image I, acquires first derivative
Afterwards, the maximum point Q of derivative value change rate is found in order to efficient, it is necessary to asked again the first derivative of edge image
It leads, derivation again is carried out to first derivative according to following formula：
G (x, y)=dx (i, j)-dy (i, j).
After the second dervative for acquiring edge image I, according to the second dervative of edge image I and the normal of edge line
Direction obtains the maximum point Q of derivative value change rate.
F, the distance between edge line L and the maximum point Q of derivative value change rate are calculated, that is, correspond between columns away from
From the out-of-focus radius R estimated；
G, point spread function is calculated according to out-of-focus radius R, according to point spread function to the QR code images of defocusing blurring
It is restored.
Specifically, the step G is calculated according to out-of-focus radius in point spread function, and out-of-focus radius is brought into defocus
In degradation model, point spread function is obtained, wherein defocus degradation model is：
Wherein h (x, y) is point spread function.
After obtaining point spread function, the QR code images of defocusing blurring are answered according to point spread function combination RL algorithms
It is former.
In order to verify recovery effect of the present invention for fuzzy QR code images, the fuzzy QR codes figure of a width is inputted first
Picture, as shown in Fig. 2, then gray processing processing is carried out to it, to ensure subsequent recovery effect, then to the QR code images of input
It carries out edge detection and obtains matrix of edge, be illustrated in figure 3 the QR code images after edge detection, then sought using lookup algorithm
Find edge line, the row that first numerical value is all 1 in matrix of edge are the row where edge line, while to through edge graph
The first derivative of picture carries out derivation again, the relational graph after secondary derivation as shown in Figure 4, and ordinate is Grad (i.e. two
Result after secondary derivation), abscissa is the position of image column, and wherein ordinate can be regarded as the location of edge line,
And abscissa can regard the normal of edge line as, be started to find derivative change to its normal direction from the origin of edge line
The maximum point of rate, you can to determine the position of Q points, by Fig. 4 it can be seen that the abscissa of the maximum point of derivative change rate is
10, you can with determine the maximum point Q of derivative change rate position coordinate for 10, and edge line is due to being set as starting point, therefore
The coordinate of edge line is 0, it is possible to which the out-of-focus radius for obtaining estimation is 10, and out-of-focus radius, which is then brought into defocus, degenerates
In model, point spread function is calculatedIt is final to utilize RL algorithm combination point spread functions to fuzzy QR
Code image is restored, and obtains the image after recovery as shown in Figure 5, comparison diagram 2 and Fig. 5, it can be seen that of the invention answers
The recovery effect of original method is preferable, ensures the quality of image while restored image clarity.
Specifically, since the size of out-of-focus radius is determined by the distance between edge line and Q points, so for side
Specific coordinate residing for edge straight line need not obtain, it is only necessary to its position be known, after obtaining the position of edge line, with edge
The location of straight line is used as ordinate, coordinate system is established using the normal direction of edge line as abscissa, then according to two
The result of secondary derivation looks for the maximum point of derivative change rate, since the interval between each point in edge image is consistent
, so the maximum point of derivative change rate can be determined as the size of out-of-focus radius R, that is, Fig. 4 to the distance of edge line
The image column position on abscissa is not the practical specific location in edge image in the middle, but a relative position.
The restored method of the present invention and other several methods being currently known are compared simultaneously, select size for 512*
The time spent in 512 image, which is used as, to be inputted, more entire recuperation,
| Method | Document 1 | Document 2 | Document 3 | Document 4 | The method of the present invention |
| Recovery time | 21.58s | 20.87s | 24.77s | 24.81s | 0.45s |
The time spent in each method, is as shown in table 1:
Table 1 (the recovery time data of the present invention and other methods)
The recovery time spent by the method that the data in table 1 can be seen that the present invention is extremely short, more multiple than others
Original method wants fast 50-60 times.
So not only recovery effect is good for the invention of the present invention, the calculating time of entire recuperation is also relatively short, calculates
Amount is also smaller.
Bibliography：
Document 1：Pan J,Hu Z,Su Z,et al.Deblurring Text Images via L0-Regularized
Intensity and Gradient Prior[C]//Computer Vision and Pattern
Recognition.IEEE,2014:2901-2908.
Document 2：Krishnan D,Tay T,Fergus R.Blind deconvolution using a
normalized sparsity measure[C]//Computer Vision and Pattern Recognition.IEEE,
2011:233-240.
Document 3：Perrone D,Favaro P.A Clearer Picture of Total Variation Blind
Deconvolution[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,
2016,38(6):1041-1055.
Document 4：Gao K,Zhu Z,Dou Z,et al.Variable Exponent Regularization
Approach for Blur Kernel Estimation of Remote Sensing Image Blind Restoration
[J].IEEE Access,2018,6(99):4352-4374.
The above, only presently preferred embodiments of the present invention, the invention is not limited in the above embodiments, as long as
It reaches the technique effect of the present invention with identical means, should all belong to the scope of protection of the present invention.
Claims (9)
1. a kind of defocus QR code method for blindly restoring image, it is characterised in that：Include the following steps：
A, gray processing processing is carried out to the QR code images of input；
B, to gray processing, treated that QR code images intercept, and obtains edge image；
C, edge detection is carried out to edge image, obtains matrix of edge；
D, matrix of edge is scanned by column, obtains the position of edge line；
E, derivation is carried out to edge image, and the maximum point of derivative value change rate is obtained by calculation；
F, the distance between edge line and the maximum point of derivative value change rate, the out-of-focus radius estimated are calculated；
G, point spread function is calculated according to out-of-focus radius, the QR code images of defocusing blurring is answered according to point spread function
It is former.
2. a kind of defocus QR code method for blindly restoring image according to claim 1, it is characterised in that：The step A is to defeated
The QR code images entered carry out gray processing processing, wherein after carrying out gray processing processing to QR code images, picture element matrix is expressed as：
Wherein a (i, j) be in QR code images position be (i, j) pixel value, 1<i<M, 1<j<N, N are the width of QR code images, M
For the height of QR code images.
3. a kind of defocus QR code method for blindly restoring image according to claim 1, it is characterised in that：It is right in the step B
Treated that QR code images are intercepted for gray processing, the specific steps are：QR code images are pre-processed, and intercept QR code images
1/4 region in the upper left corner.
4. a kind of defocus QR code method for blindly restoring image according to claim 1, it is characterised in that：The step C opposite side
Edge image carries out in edge detection, and edge detection is carried out to edge image using Canny detective operators.
5. a kind of defocus QR code method for blindly restoring image according to claim 1, it is characterised in that：The step D opposite side
During edge matrix is scanned by column, matrix of edge is searched using lookup algorithm, by comparing until being connected for the first time
The row that continuous numerical value is 1, the as position where edge line.
6. a kind of defocus QR code method for blindly restoring image according to claim 1, it is characterised in that：The step E opposite side
Edge image carry out derivation the specific steps are：Secondary derivation is carried out to edge image, the formula of wherein first time derivation is：
Dx (i, j)=I (i+1, j)-I (i, j)；
Dy (i, j)=I (i, j+1)-I (i, j)；
Wherein I is edge image, and I (i, j) is the value that position is at (i, j) in edge image I, after acquiring first derivative, root
Derivation again is carried out to first derivative according to following formula：
G (x, y)=dx (i, j)-dy (i, j).
7. a kind of defocus QR code method for blindly restoring image according to claim 6, it is characterised in that：The step E passes through
The maximum point of derivative value change rate is calculated, is obtained according to the normal direction of the second dervative of edge image I and edge line
To the maximum point of derivative value change rate.
8. a kind of defocus QR code method for blindly restoring image according to claim 1, it is characterised in that：The step G according to
Out-of-focus radius is calculated in point spread function, and out-of-focus radius is brought into defocus degradation model, point spread function is obtained,
Middle defocus degradation model is：
Wherein h (x, y) is point spread function, and R is out-of-focus radius.
9. a kind of defocus QR code method for blindly restoring image according to claim 1, it is characterised in that：The step G according to
During point spread function restores the QR code images of defocusing blurring, according to point spread function combination RL algorithms to defocusing blurring
QR code images are restored.
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| CN113743147A (en) * | 2021-07-16 | 2021-12-03 | 广东技术师范大学 | Fuzzy imaging mechanism-based anti-fuzzy two-dimensional code image and blind restoration method thereof |
| CN113743147B (en) * | 2021-07-16 | 2023-08-29 | 广东技术师范大学 | Anti-fuzzy two-dimensional code image based on fuzzy imaging mechanism and blind restoration method thereof |
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