CN104504662A - Homomorphic filtering based image processing method and system - Google Patents
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Abstract
The invention provides a homomorphic filtering based image processing method and system. The method includes the steps of acquiring an original code map; subjecting the original code map to homomorphic filtering to obtain a code map subjected to homomorphic filtering; based on a preset partitioning algorithm, partitioning the code map subjected to homomorphic filtering into a plurality of code map sub-units; subjecting each code map sub-unit to binarization by an OTSU algorithm to obtain a binary code map sub-unit; combining the binary code map sub-units to generate a final binary code map. The homomorphic filtering based image processing method and system has the advantages that before binarization of the code map, homomorphic filtering is performed first, the images are effectively enhanced by homomorphic filtering, image quality decline caused by insufficiency of light is effectively avoided, the advantages of the OTSU algorithm are given to maximum play, a foreground and a background are precisely distinguished, no effective information is lost from the binary images, image binarization quality is improved, and the following image processing is simplified.
Description
Technical field
The invention belongs to technical field of image processing, be specifically related to a kind of image processing method based on homomorphic filtering and system.
Background technology
In digital image processing field, image binaryzation occupies very important status, particularly in the image procossing of practicality, there is the system formed with binary Images Processing realization of One's name is legion, such as, electronic eyes scanning car plate, mobile phone camera shooting one-dimension code or Quick Response Code etc.
The principle of image binaryzation is: first process the image into gray-scale map, then a suitable threshold value is got, the pixel that all gray scales are more than or equal to threshold value is judged as and belongs to certain objects, its gray-scale value is 255 expressions, otherwise, gray scale is excluded beyond object area lower than the pixel of threshold value, and gray-scale value is 0, represents the object area of background or exception.The setting of threshold value is very crucial, if arrange too high, some details of certain objects may be filtered out; If arrange too low, in background, some interfering objects cannot filter out.Therefore, around the setting of threshold value, derived many image binaryzation Processing Algorithm, wherein, OTSU algorithm is Application comparison a kind of algorithm widely.
OTSU algorithm also claims difference method between maximum kind, sometimes be also referred to as Otsu algorithm, be considered to the optimal algorithm that threshold value in Iamge Segmentation is chosen, have calculating simply, not by the advantage that brightness of image and contrast affect, therefore, Digital Image Processing is widely used.Its principle is: by the gamma characteristic of image, image is divided into background and prospect two parts.Inter-class variance between background and prospect is larger, illustrates that the two-part difference of composing images is larger, when part prospect mistake is divided into background, or when part background mistake is divided into prospect, two parts difference all can be caused to diminish.Therefore, the segmentation making inter-class variance maximum means that misclassification probability is minimum.In the use of reality, OTSU often can not be used to carry out calculating to whole image and to obtain a threshold value, but image is divided into multiple sizeable fritter, then threshold value be got to each fritter OTSU, and then regional area is not disturbed by other regions.
Although OTSU algorithm is the optimal algorithm that in Iamge Segmentation, threshold value is chosen, but, in the process identifying Quick Response Code code figure, because the code-point on code figure is very little, and, code figure background differs less with code-point color, or because illumination deficiency causes image quality decrease, therefore, when direct use OTSU Binarization methods carries out binaryzation to Quick Response Code figure, can part code-point be thought by mistake background and filter, thus make the code figure after binaryzation lost part effective information, and then impact is brought on follow-up code figure identification.
Summary of the invention
For the defect that prior art exists, the invention provides a kind of image processing method based on homomorphic filtering and system, can effectively solve the problem.
The technical solution used in the present invention is as follows:
The invention provides a kind of image processing method based on homomorphic filtering, comprise the following steps:
S1, gathers source code figure;
S2, carries out homomorphic filtering process to collected described source code figure, obtains the code figure after homomorphic filtering process;
Code diagram root after described homomorphic filtering process, based on the partitioning algorithm preset, is several yard of figure subelement by S3;
S4, adopts OTSU algorithm to carry out binary conversion treatment to code figure subelement described in each respectively, obtains binaryzation code figure subelement;
S5, the binaryzation code figure that described in each, binaryzation code figure subelement combination producing is final.
Preferably, in S3, described partitioning algorithm is: the code figure number sub-cells positive correlation of code figure accuracy of identification and division; That is: if the code figure accuracy of identification of setting is higher, then the area of code figure subelement is less, divides the code figure number sub-cells obtained more.
Preferably, in S3, divide obtain each described in the shape of code figure subelement identical or not identical; And/or
Divide obtain each described in the area of code figure subelement identical or not identical.
Preferably, in S4, adopt OTSU algorithm to carry out binary conversion treatment to code figure subelement described in each respectively, obtain binaryzation code figure subelement, specifically comprise the following steps:
S4.1, reads the pixel distribution of pending described code figure subelement, if described code figure subelement comprises N × M pixel;
S4.2, adding up gray scale in described code figure subelement is the number of pixels n (i) that i is corresponding, then the average gray value of this yard of figure subelement is:
u＝∑i*n(i)/(M*N)；
S4.3, arranges initial parameter: note t is the segmentation threshold of object and background, and it is w1 that the object pixel that note gray scale is greater than t accounts for a yard ratio for figure subelement image, and the average gray of note object pixel is u1:
W1=W1/ (M*N), wherein, W1 is the statistical number that gray-scale value is greater than t
u1＝∑i*n(i)/W1,i>t
In like manner, the background pixel that note gray scale is less than t accounts for the ratio w2 of image, the average gray u2 of background pixel;
S4.4, the t in traversal S4.3, make G=w1* (u1-u) * (u1-u)+w2* (u2-u) * (u2-u) maximum, t is now optimal threshold;
S4.5, after obtaining described optimal threshold t, using described optimal threshold t as binaryzation boundary line, carries out binary conversion treatment to described code figure subelement.
Preferably, described code figure is Quick Response Code code figure or one-dimension code code figure.
The present invention also provides a kind of image processing system based on homomorphic filtering, comprising:
Acquisition module, for gathering source code figure;
Homomorphic filtering processing module, carries out homomorphic filtering process for the described source code figure collected described acquisition module, obtains the code figure after homomorphic filtering process;
Dividing module, for based on the partitioning algorithm preset, is several yard of figure subelement by the code diagram root after the process of described homomorphic filtering processing module;
Binarization block, for adopting OTSU algorithm to carry out binary conversion treatment to dividing the code figure subelement that Module Division obtains described in each respectively, obtains binaryzation code figure subelement;
Binaryzation code figure generation unit, for described binarization block is obtained each described in the final binaryzation code figure of binaryzation code figure subelement combination producing.
Preferably, the partitioning algorithm that described division module uses is: the code figure number sub-cells positive correlation of code figure accuracy of identification and division; That is: if the code figure accuracy of identification of setting is higher, then the area of code figure subelement is less, divides the code figure number sub-cells obtained more.
Preferably, described division Module Division obtain each described in the shape of code figure subelement identical or not identical; And/or
Divide obtain each described in the area of code figure subelement identical or not identical.
Preferably, described binarization block specifically for:
S4.1, reads the pixel distribution of pending described code figure subelement, if described code figure subelement comprises N × M pixel;
S4.2, adding up gray scale in described code figure subelement is the number of pixels n (i) that i is corresponding, then the average gray value of this yard of figure subelement is:
u＝∑i*n(i)/(M*N)；
S4.3, arranges initial parameter: note t is the segmentation threshold of object and background, and it is w1 that the object pixel that note gray scale is greater than t accounts for a yard ratio for figure subelement image, and the average gray of note object pixel is u1:
W1=W1/ (M*N), wherein, W1 is the statistical number that gray-scale value is greater than t
u1＝∑i*n(i)/W1,i>t
In like manner, the background pixel that note gray scale is less than t accounts for the ratio w2 of image, the average gray u2 of background pixel;
S4.4, the t in traversal S4.3, make G=w1* (u1-u) * (u1-u)+w2* (u2-u) * (u2-u) maximum, t is now optimal threshold;
S4.5, after obtaining described optimal threshold t, using described optimal threshold t as binaryzation boundary line, carries out binary conversion treatment to described code figure subelement.
Preferably, the described source code figure that described acquisition module collects is Quick Response Code code figure or one-dimension code code figure.
Beneficial effect of the present invention is as follows:
Image processing method based on homomorphic filtering provided by the invention and system, before binaryzation is carried out to code figure, first homomorphic filtering process is carried out, by homomorphic filtering, image is effectively strengthened, thus effectively avoid the image quality decrease because illumination deficiency causes, and interested image detail is effectively strengthened, therefore, follow-up when carrying out binary conversion treatment, at utmost can play the advantage of OTSU algorithm, accurate differentiation prospect and background, the code figure after binaryzation is made to lose effective information, improve the quality of image binaryzation, simplify successive image processing procedure.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the image processing method based on homomorphic filtering provided by the invention;
Fig. 2 is the structural representation of the image processing system based on homomorphic filtering provided by the invention.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in detail:
As shown in Figure 1, the invention provides a kind of image processing method based on homomorphic filtering, comprise the following steps:
S1, gathers source code figure;
Herein, code figure both can be Quick Response Code code figure, and also can be one-dimension code code figure, the present invention limit gathered code figure particular type.
S2, carries out homomorphic filtering process to collected described source code figure, obtains the code figure after homomorphic filtering process;
In this step, homomorphic filtering refers to a kind of image processing method frequency filter and greyscale transformation combined, and it relies on the basis of illumination/Reflectivity Model as frequency domain process of image, utilizes compression brightness range and strengthen contrast to improve picture quality.
For particularly suitable code figure identification, the invention provides a kind of novel homomorphic filtering image processing method, comprise the following steps:
Described source code figure is taken the logarithm, makes the multiplying in iconic model be converted into simple additive operation, obtain the image function of addition representation;
Again Fourier transform is carried out to image function, image function is transformed into frequency domain, be expressed as the function of luminance component and contrast component;
Then, the variation range of compression luminance component, strengthens the contrast of contrast component, strengthens details, obtains the image function after changing;
Again filtering process is carried out to the image function after change, inverse fourier transform and exponent arithmetic is carried out to filter result, obtains the Output rusults after homomorphic filtering.
Certainly, in actual applications, also can use other Homomorphic Filtering Algorithm, carry out homomorphic filtering process to source code figure, the present invention does not limit this.
Code diagram root after described homomorphic filtering process, based on the partitioning algorithm preset, is several yard of figure subelement by S3;
In this step, partitioning algorithm is: the code figure number sub-cells positive correlation of code figure accuracy of identification and division; That is: if the code figure accuracy of identification of setting is higher, then the area of code figure subelement is less, divides the code figure number sub-cells obtained more.
That is, in the present invention, the quantity of the code figure subelement obtained after code diagram root is not limited, sets flexibly according to actual code figure accuracy of identification demand.
In addition, in the present invention, divide obtain each described in the shape of code figure subelement identical or not identical.Divide obtain each described in the area of code figure subelement identical or not identical.
S4, adopts OTSU algorithm to carry out binary conversion treatment to code figure subelement described in each respectively, obtains binaryzation code figure subelement;
OTSU algorithm is a kind of Dynamic Binarization method of globalize, is Da-Jin algorithm again, is a kind of algorithms most in use of Binary Sketch of Grey Scale Image.The basic thought of this algorithm is: establish and use some threshold values by gray level image according to gray scale size, be divided into target part and background parts two class, when the variance within clusters of this two class is minimum and inter-class variance is maximum, the threshold value obtained is optimum binary-state threshold.
The invention provides the OTSU algorithm of following a kind of improvement, principle is:
Such as, to the image of width N × M pixel, following binarization method can be adopted:
1, the average gray u of first computed image, is calculated as follows:
For the image of M × N number of pixel, it is the number of pixels n (i) that i is corresponding that statistics obtains gray scale in all images, then the average gray value of this image is:
u＝∑i*n(i)/(M*N)；
2, the correlated variables solving best threshold values t is listed
Note t is the segmentation threshold of object and background, note object pixel (it is w1 that gray scale is greater than ratio t) accounting for image, and the average gray of note object pixel is u1:
W1=W1/ (M*N), W1 are wherein the statistical number that gray-scale value is greater than t
u1＝∑i*n(i)/W1,i>t.
In like manner, obtain the ratio w2 that background pixel accounts for image, the average gray u2 of background pixel.
3, solving best threshold values t is that class difference is maximum
T in traversal step 2, when making G=w1* (u1-u) * (u1-u)+w2* (u2-u) * (u2-u) maximum .G maximum, namely obtains optimal threshold
4, according to the threshold value that step 3 is determined, image binaryzation process is carried out.
S5, the binaryzation code figure that described in each, binaryzation code figure subelement combination producing is final.
As shown in Figure 2, the present invention also provides a kind of image processing system based on homomorphic filtering, comprising:
Acquisition module, for gathering source code figure;
Wherein, the described source code figure that acquisition module collects is Quick Response Code code figure or one-dimension code code figure.
Homomorphic filtering processing module, carries out homomorphic filtering process for the described source code figure collected described acquisition module, obtains the code figure after homomorphic filtering process;
Dividing module, for based on the partitioning algorithm preset, is several yard of figure subelement by the code diagram root after the process of described homomorphic filtering processing module;
Wherein, the partitioning algorithm that division module uses is: the code figure number sub-cells positive correlation of code figure accuracy of identification and division; That is: if the code figure accuracy of identification of setting is higher, then the area of code figure subelement is less, divides the code figure number sub-cells obtained more.
Divide that Module Division obtains each described in the shape of code figure subelement identical or not identical.Divide that Module Division obtains each described in the area of code figure subelement identical or not identical.
Binarization block, for adopting OTSU algorithm to carry out binary conversion treatment to dividing the code figure subelement that Module Division obtains described in each respectively, obtains binaryzation code figure subelement;
Binarization block specifically for:
S4.1, reads the pixel distribution of pending described code figure subelement, if described code figure subelement comprises N × M pixel;
S4.2, adding up gray scale in described code figure subelement is the number of pixels n (i) that i is corresponding, then the average gray value of this yard of figure subelement is:
u＝∑i*n(i)/(M*N)；
S4.3, arranges initial parameter: note t is the segmentation threshold of object and background, and it is w1 that the object pixel that note gray scale is greater than t accounts for a yard ratio for figure subelement image, and the average gray of note object pixel is u1:
W1=W1/ (M*N), wherein, W1 is the statistical number that gray-scale value is greater than t
u1＝∑i*n(i)/W1,i>t
In like manner, the background pixel that note gray scale is less than t accounts for the ratio w2 of image, the average gray u2 of background pixel;
S4.4, the t in traversal S4.3, make G=w1* (u1-u) * (u1-u)+w2* (u2-u) * (u2-u) maximum, t is now optimal threshold;
S4.5, after obtaining described optimal threshold t, using described optimal threshold t as binaryzation boundary line, carries out binary conversion treatment to described code figure subelement.
Binaryzation code figure generation unit, for described binarization block is obtained each described in the final binaryzation code figure of binaryzation code figure subelement combination producing.
Image processing method based on homomorphic filtering provided by the invention and system, before binaryzation is carried out to code figure, first homomorphic filtering process is carried out, by homomorphic filtering, image is effectively strengthened, thus effectively avoid the image quality decrease because illumination deficiency causes, and interested image detail is effectively strengthened, therefore, follow-up when carrying out binary conversion treatment, at utmost can play the advantage of OTSU algorithm, accurate differentiation prospect and background, the code figure after binaryzation is made to lose effective information, improve the quality of image binaryzation, simplify successive image processing procedure.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should look protection scope of the present invention.
Claims (10)
1. based on an image processing method for homomorphic filtering, it is characterized in that, comprise the following steps:
S1, gathers source code figure;
S2, carries out homomorphic filtering process to collected described source code figure, obtains the code figure after homomorphic filtering process;
Code diagram root after described homomorphic filtering process, based on the partitioning algorithm preset, is several yard of figure subelement by S3;
S4, adopts OTSU algorithm to carry out binary conversion treatment to code figure subelement described in each respectively, obtains binaryzation code figure subelement;
S5, the binaryzation code figure that described in each, binaryzation code figure subelement combination producing is final.
2. the image processing method based on homomorphic filtering according to claim 1, is characterized in that, in S3, described partitioning algorithm is: the code figure number sub-cells positive correlation of code figure accuracy of identification and division; That is: if the code figure accuracy of identification of setting is higher, then the area of code figure subelement is less, divides the code figure number sub-cells obtained more.
3. the image processing method based on homomorphic filtering according to claim 1, is characterized in that, in S3, divide obtain each described in the shape of code figure subelement identical or not identical; And/or
Divide obtain each described in the area of code figure subelement identical or not identical.
4. the image processing method based on homomorphic filtering according to claim 1, is characterized in that, in S4, adopts OTSU algorithm to carry out binary conversion treatment respectively, obtain binaryzation code figure subelement, specifically comprise the following steps code figure subelement described in each:
S4.1, reads the pixel distribution of pending described code figure subelement, if described code figure subelement comprises N × M pixel;
S4.2, adding up gray scale in described code figure subelement is the number of pixels n (i) that i is corresponding, then the average gray value of this yard of figure subelement is:
u＝∑i*n(i)/(M*N)；
S4.3, arranges initial parameter: note t is the segmentation threshold of object and background, and it is w1 that the object pixel that note gray scale is greater than t accounts for a yard ratio for figure subelement image, and the average gray of note object pixel is u1:
W1=W1/ (M*N), wherein, W1 is the statistical number that gray-scale value is greater than t
u1＝∑i*n(i)/W1,i>t
In like manner, the background pixel that note gray scale is less than t accounts for the ratio w2 of image, the average gray u2 of background pixel;
S4.4, the t in traversal S4.3, make G=w1* (u1-u) * (u1-u)+w2* (u2-u) * (u2-u) maximum, t is now optimal threshold;
S4.5, after obtaining described optimal threshold t, using described optimal threshold t as binaryzation boundary line, carries out binary conversion treatment to described code figure subelement.
5. the image processing method based on homomorphic filtering according to any one of claim 1-4, is characterized in that, described code figure is Quick Response Code code figure or one-dimension code code figure.
6. based on an image processing system for homomorphic filtering, it is characterized in that, comprising:
Acquisition module, for gathering source code figure;
Homomorphic filtering processing module, carries out homomorphic filtering process for the described source code figure collected described acquisition module, obtains the code figure after homomorphic filtering process;
Dividing module, for based on the partitioning algorithm preset, is several yard of figure subelement by the code diagram root after the process of described homomorphic filtering processing module;
Binarization block, for adopting OTSU algorithm to carry out binary conversion treatment to dividing the code figure subelement that Module Division obtains described in each respectively, obtains binaryzation code figure subelement;
Binaryzation code figure generation unit, for described binarization block is obtained each described in the final binaryzation code figure of binaryzation code figure subelement combination producing.
7. the image processing system based on homomorphic filtering according to claim 6, is characterized in that, the partitioning algorithm that described division module uses is: the code figure number sub-cells positive correlation of code figure accuracy of identification and division; That is: if the code figure accuracy of identification of setting is higher, then the area of code figure subelement is less, divides the code figure number sub-cells obtained more.
8. the image processing system based on homomorphic filtering according to claim 6, is characterized in that, described division Module Division obtain each described in the shape of code figure subelement identical or not identical; And/or
Divide obtain each described in the area of code figure subelement identical or not identical.
9. the image processing system based on homomorphic filtering according to claim 6, is characterized in that, described binarization block specifically for:
S4.1, reads the pixel distribution of pending described code figure subelement, if described code figure subelement comprises N × M pixel;
S4.2, adding up gray scale in described code figure subelement is the number of pixels n (i) that i is corresponding, then the average gray value of this yard of figure subelement is:
u＝∑i*n(i)/(M*N)；
S4.3, arranges initial parameter: note t is the segmentation threshold of object and background, and it is w1 that the object pixel that note gray scale is greater than t accounts for a yard ratio for figure subelement image, and the average gray of note object pixel is u1:
W1=W1/ (M*N), wherein, W1 is the statistical number that gray-scale value is greater than t
u1＝∑i*n(i)/W1,i>t
In like manner, the background pixel that note gray scale is less than t accounts for the ratio w2 of image, the average gray u2 of background pixel;
S4.4, the t in traversal S4.3, make G=w1* (u1-u) * (u1-u)+w2* (u2-u) * (u2-u) maximum, t is now optimal threshold;
S4.5, after obtaining described optimal threshold t, using described optimal threshold t as binaryzation boundary line, carries out binary conversion treatment to described code figure subelement.
10. the image processing system based on homomorphic filtering according to any one of claim 6-9, is characterized in that, the described source code figure that described acquisition module collects is Quick Response Code code figure or one-dimension code code figure.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105488448A (en) * | 2015-12-11 | 2016-04-13 | 杭州晟元数据安全技术股份有限公司 | Barcode and two-dimensional code distinguishing method |
| CN105869175A (en) * | 2016-04-21 | 2016-08-17 | 北京邮电大学 | Image segmentation method and system |
| CN106373104A (en) * | 2016-09-07 | 2017-02-01 | 河海大学常州校区 | Adaptive enhancement method of rock boring image |
| CN106815587A (en) * | 2015-11-30 | 2017-06-09 | 浙江宇视科技有限公司 | Image processing method and device |
| CN106846276A (en) * | 2017-02-06 | 2017-06-13 | 上海兴芯微电子科技有限公司 | A kind of image enchancing method and device |
| CN107730508A (en) * | 2017-09-01 | 2018-02-23 | 上海微元计算机系统集成有限公司 | Color documents images multichannel binary processing method |
| CN110263595A (en) * | 2019-06-25 | 2019-09-20 | 北京慧眼智行科技有限公司 | A kind of two dimensional code detection method and device |
| CN110289909A (en) * | 2019-06-28 | 2019-09-27 | 华南理工大学 | Outdoor visible light communication target signal source based on optical flow method tracks extracting method |
| CN110633592A (en) * | 2018-06-25 | 2019-12-31 | 视联动力信息技术股份有限公司 | Image processing method and device |
| CN110874732A (en) * | 2018-08-31 | 2020-03-10 | 北京意锐新创科技有限公司 | Scanned payment method and device based on mobile payment equipment |
| CN110874733A (en) * | 2018-08-31 | 2020-03-10 | 北京意锐新创科技有限公司 | Passive code scanning payment method and device supporting external equipment |
| CN110910591A (en) * | 2018-09-18 | 2020-03-24 | 北京意锐新创科技有限公司 | Cash registering method and device based on mobile payment device and applied to scenic spot |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110085732A1 (en) * | 2009-10-09 | 2011-04-14 | Ting-Yuan Cheng | Qr code processing method and apparatus thereof |
| CN103235948A (en) * | 2013-04-22 | 2013-08-07 | 中山大学 | Adaptive threshold binarization method for two-dimensional barcode |
-
2014
- 2014-12-26 CN CN201410834140.9A patent/CN104504662A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110085732A1 (en) * | 2009-10-09 | 2011-04-14 | Ting-Yuan Cheng | Qr code processing method and apparatus thereof |
| CN103235948A (en) * | 2013-04-22 | 2013-08-07 | 中山大学 | Adaptive threshold binarization method for two-dimensional barcode |
Non-Patent Citations (1)
| Title |
|---|
| 张宁: "基于摄像方式的二维条码识别算法的研究", 《中国优秀硕士学位论文全文数据库》 * |
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| CN106815587B (en) * | 2015-11-30 | 2019-10-18 | 浙江宇视科技有限公司 | Image processing method and device |
| CN105488448B (en) * | 2015-12-11 | 2018-10-09 | 杭州晟元数据安全技术股份有限公司 | A kind of differentiating method of bar code and Quick Response Code |
| CN105488448A (en) * | 2015-12-11 | 2016-04-13 | 杭州晟元数据安全技术股份有限公司 | Barcode and two-dimensional code distinguishing method |
| CN105869175A (en) * | 2016-04-21 | 2016-08-17 | 北京邮电大学 | Image segmentation method and system |
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| CN110633592A (en) * | 2018-06-25 | 2019-12-31 | 视联动力信息技术股份有限公司 | Image processing method and device |
| CN110633592B (en) * | 2018-06-25 | 2024-03-15 | 视联动力信息技术股份有限公司 | Image processing method and device |
| CN110874733A (en) * | 2018-08-31 | 2020-03-10 | 北京意锐新创科技有限公司 | Passive code scanning payment method and device supporting external equipment |
| CN110874732A (en) * | 2018-08-31 | 2020-03-10 | 北京意锐新创科技有限公司 | Scanned payment method and device based on mobile payment equipment |
| CN110910591A (en) * | 2018-09-18 | 2020-03-24 | 北京意锐新创科技有限公司 | Cash registering method and device based on mobile payment device and applied to scenic spot |
| CN110263595B (en) * | 2019-06-25 | 2023-02-17 | 北京慧眼智行科技有限公司 | Two-dimensional code detection method and device |
| CN110263595A (en) * | 2019-06-25 | 2019-09-20 | 北京慧眼智行科技有限公司 | A kind of two dimensional code detection method and device |
| CN110289909A (en) * | 2019-06-28 | 2019-09-27 | 华南理工大学 | Outdoor visible light communication target signal source based on optical flow method tracks extracting method |
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