CN111986113B - Optical image shadow elimination method and system - Google Patents
Optical image shadow elimination method and system Download PDFInfo
- Publication number
- CN111986113B CN111986113B CN202010840972.7A CN202010840972A CN111986113B CN 111986113 B CN111986113 B CN 111986113B CN 202010840972 A CN202010840972 A CN 202010840972A CN 111986113 B CN111986113 B CN 111986113B
- Authority
- CN
- China
- Prior art keywords
- image
- gaussian
- dimensional
- gray level
- original image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000008030 elimination Effects 0.000 title claims abstract description 24
- 238000003379 elimination reaction Methods 0.000 title claims abstract description 24
- 230000003287 optical effect Effects 0.000 title claims abstract description 16
- 238000004364 calculation method Methods 0.000 claims abstract description 37
- 238000010586 diagram Methods 0.000 claims abstract description 20
- 230000011218 segmentation Effects 0.000 claims description 4
- 230000000694 effects Effects 0.000 description 6
- 238000012360 testing method Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000010191 image analysis Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
- G06T5/94—Dynamic range modification of images or parts thereof based on local image properties, e.g. for local contrast enhancement
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20021—Dividing image into blocks, subimages or windows
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
- Facsimile Image Signal Circuits (AREA)
Abstract
The invention discloses a method and a system for eliminating optical image shadows. The method comprises the following steps: s1, inputting an original image I with width W and height H, and dividing the original image I into n image small blocks in equal size; s2, calculating the gray level total value of each image small block, and taking the center point coordinate (i, j) of the image small block with the maximum gray level total value as the strongest point of the shadow; s3, taking all point coordinate sets of columns where x=i are located as l on the original image I W Taking the coordinate set of all points of the row where y=j is located as l H The method comprises the steps of carrying out a first treatment on the surface of the S4, according to the coordinate set l W 、l H Respectively performing gray value calculation to obtain l W 、l H A respective one-dimensional gray level histogram; s5, according to l W 、l H Respectively carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l W 、l H Respective Gaussian equation y 1 、y 2 The method comprises the steps of carrying out a first treatment on the surface of the S6, according to y 1 、y 2 Fitting to obtain a two-dimensional Gaussian distribution diagram y; s7, performing degaussing according to the original image I and the two-dimensional Gaussian distribution diagram y, and obtaining an image Img after light shadow elimination. The invention fits the light and shadow of the optical image in a two-dimensional Gaussian fitting mode, so that the brightness in the image is more uniform.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to an optical image shadow elimination method and system.
Background
In image analysis, the quality of the image directly affects the accuracy of the design and effect of the recognition algorithm, so that preprocessing is required before image analysis (feature extraction, segmentation, matching, recognition, etc.). The main purpose of image preprocessing is to eliminate irrelevant information in the image, recover useful real information, enhance the detectability of relevant information, simplify data to the maximum extent, and thereby improve the reliability of feature extraction, image segmentation, matching and recognition.
For some images with uneven brightness distribution caused by light and shadow influence, the effective pretreatment is more beneficial to the subsequent treatment, so that the image with higher quality can be obtained by an image pretreatment method, thereby improving the effect of the subsequent experiment.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art and provides an optical image shadow eliminating method and system.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an optical image shadow elimination method, comprising the steps of:
s1, inputting an original image I with width W and height H, and dividing the original image I into n image small blocks in equal size;
s2, calculating the gray level total value of each image small block, and taking the center point coordinate (i, j) of the image small block with the maximum gray level total value as the strongest point of the shadow;
s3, taking all point coordinate sets of columns where x=i are located as l on the original image I W Taking the coordinate set of all points of the row where y=j is located as l H ；
S4, according to the coordinate set l W Gray value calculation is performed to obtain l W According to the coordinate set l H Gray value calculation is performed to obtain l H Is a one-dimensional gray level histogram of (a);
s5, according to l W Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l W Is of the Gaussian equation y 1 According to l H Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l H Is of the Gaussian equation y 2 ；
S6, according to y 1 And y 2 Fitting to obtain a two-dimensional Gaussian distribution diagram y;
s7, performing degaussing according to the original image I and the two-dimensional Gaussian distribution diagram y, and obtaining an image Img after light shadow elimination.
Preferably, in step S4, the method is performed according to the coordinate set l W Calculated to obtain (x) W ,y W ) Wherein x is W ∈(1，2，3......H)，y W E (f (1), f (2), f (3.. Fw.. F (H)), f function is a calculated gray value function, y W For being based on the coordinate set l W Calculating the corresponding gray value to obtain l W Is a one-dimensional gray scale histogram of (a).
Preferably, in step S4, the method is performed according to the coordinate set l H Calculated to obtain (x) H ,y H ) Wherein x is H ∈(1，2，3......W)，y H ∈(f(1),f(2),f(3)......f(W))，y H For being based on the coordinate set l H Calculating the corresponding gray value to obtain l H Is a one-dimensional gray scale histogram of (a).
Preferably, in step S5, for l W Data (x) in one-dimensional gray level histogram of (1) W ,y W ) Performing Gaussian fitting calculation to obtain l W Is of the Gaussian equation y 1 。
Preferably, in step S5, the method is carried out by comparing l H Data (x) in one-dimensional gray level histogram of (1) H ,y H ) Performing Gaussian fitting calculation to obtain l H Is of the Gaussian equation y 2 。
Preferably, in step S6, equation y is performed 1 Taking x 1 ＝[1,...H]Obtaining y' 1 Column vector, equation y 2 Taking x 2 ＝[1,...W]Obtaining y' 2 Line vector according to y=y' 1 ×y' 2 ObtainingA two-dimensional gaussian profile y.
In a preferred embodiment, in step S7, the original image I is divided by the corresponding pixels on the two-dimensional gaussian distribution diagram y to perform degussion, thereby obtaining an image Img after the light shadow is removed.
Preferably, in step S1, all of the n image tiles are rectangular.
Preferably, w=1600 px and h=1200 px of the original image I.
The invention also provides an optical image shadow elimination system, which comprises:
the input module is used for inputting an original image I with width W and height H;
the segmentation module is used for segmenting the original image I into n image small blocks with the same size;
the calculation module is used for calculating the gray level total value of each image small block;
the coordinate set acquisition module is used for acquiring the center point coordinates (I, j) of the image small block with the highest gray level total value, and taking all point coordinate sets of the column where x=i is located as l on the original image I W Taking the coordinate set of all points of the row where y=j is located as l H ；
A one-dimensional gray histogram acquisition module for acquiring a gray histogram according to the coordinate set l W Gray value calculation is performed to obtain l W According to the coordinate set l H Gray value calculation is performed to obtain l H Is a one-dimensional gray level histogram of (a);
a Gaussian equation acquisition module for acquiring the Gaussian equation according to the L W Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l W Is of the Gaussian equation y 1 According to l H Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l H Is of the Gaussian equation y 2 ；
Fitting module for according to y 1 And y 2 Fitting to obtain a two-dimensional Gaussian distribution diagram y;
and the Gaussian elimination module is used for carrying out Gaussian elimination according to the original image I and the two-dimensional Gaussian distribution diagram y so as to obtain an image Img after the light shadow elimination.
The beneficial effects of the invention are as follows: for some images with uneven brightness distribution caused by the light and shadow effect, a two-dimensional Gaussian fitting mode is adopted to fit the light and shadow of the optical image, so that an effective pretreatment effect is obtained, the brightness in the image is more uniform, the subsequent experimental treatment of the image is facilitated, and the experimental effect is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of optical image shadow elimination;
FIG. 2 is an unprocessed raw image I;
fig. 3 is an image Img obtained by performing deghosting on the original image I;
fig. 4 is a schematic diagram of an optical image shadow eliminating system.
Detailed Description
The following specific examples are presented to illustrate the present invention, and those skilled in the art will readily appreciate the additional advantages and capabilities of the present invention as disclosed herein. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
Embodiment one:
referring to fig. 1, the present embodiment provides an optical image light and shadow eliminating method, which includes the steps of:
s1, inputting an original image I with width W and height H, and dividing the original image I into n image small blocks in equal size;
s2, calculating the gray level total value of each image small block, and taking the center point coordinate (i, j) of the image small block with the maximum gray level total value as the strongest point of the shadow;
s3, taking all point coordinate sets of columns where x=i are located as l on the original image I W Taking the coordinate set of all points of the row where y=j is located as l H ；
S4, according to the coordinate set l W Gray value calculation is performed to obtain l W According to the coordinate set l H Gray value calculation is performed to obtain l H Is a one-dimensional gray level histogram of (a);
s5, according to l W Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l W Is of the Gaussian equation y 1 According to l H Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l H Is of the Gaussian equation y 2 ；
S6, according to y 1 And y 2 Fitting to obtain a two-dimensional Gaussian distribution diagram y;
s7, performing degaussing according to the original image I and the two-dimensional Gaussian distribution diagram y, and obtaining an image Img after light shadow elimination.
Specific:
referring to fig. 2, the present embodiment is described taking w=1600 px, h=1200 px of the input original image I, and dividing the original image I into 4 segments on average in both width and length, i.e., dividing the original image I into 16 rectangles of equal size as an example.
After the gray level total value is calculated in step S2, the coordinates of the center point of the image patch with the maximum gray level total value is (915,463), i.e. the center point is the strongest point of the shadow, and the coordinate set of all points in the column where x=915 is located is taken as l on the original image I W Taking all point coordinate sets of the row where y=463 is located as l H 。
In step S4, according to the coordinate set l W Calculated to obtain (x) W ,y W ) Wherein x is W ∈(1,...1200)，y W E (f (1),...f (1200)), the f function is a calculated gray value function, y W For being based on the coordinate set l W Calculating the corresponding gray value to obtain l W Is a one-dimensional gray level histogram of (a); according to the coordinatesCollection l H Calculated to obtain (x) H ,y H ) Wherein x is H ∈(1,...1600)，y H ∈(f(1),...f(1600))，y H For being based on the coordinate set l H Calculating the corresponding gray value to obtain l H Is a one-dimensional gray scale histogram of (a).
In step S5, for l W Data (x) in one-dimensional gray level histogram of (1) W ,y W ) Performing Gaussian fitting calculation to obtain l W Is of the Gaussian equation y 1 By the method of l H Data (x) in one-dimensional gray level histogram of (1) H ,y H ) Performing Gaussian fitting calculation to obtain l H Is of the Gaussian equation y 2 。
The principle of Gaussian fitting is as follows:
is provided with a set of test data (x i ,y i ) (i=1, 2,3,) can be described by a gaussian function:
in the parameters to be estimated y max 、x max And S is peak value, peak position, and half width information of the gaussian curve, respectively. Taking natural logarithms from two sides of the formula (1), and obtaining the natural logarithms as follows:
and (3) making:
and considering all test data, the formula (3) is expressed in a matrix form as:
the method is characterized by comprising the following steps:
Z＝XB (5)
according to the least square principle, the generalized least square solution of the matrix B is:
B＝(X T X) -1 X T Z (6)
then according to (3), the parameter y to be estimated is obtained max 、x max S, to-be-estimated parameter y max 、x max And S is substituted into the Gaussian function of (1) to obtain the test data (x) i ,y i ) (i=1, 2,3,) gaussian equation.
From the above Gaussian fitting principle, we can see that when we need to do so W Is of the Gaussian equation y 1 In this case, only the test data (x i ,y i ) (i=1, 2,3,) is replaced with (x W ,y W ) The subsequent calculation is performed when we need to ask for l H Is of the Gaussian equation y 2 In this case, only the test data (x i ,y i ) (i=1, 2,3,) is replaced with (x H ,y H ) And performing subsequent calculation.
After obtaining l W Is of the Gaussian equation y 1 L H Is of the Gaussian equation y 2 Then, go to equation y 1 Taking x 1 ＝[1,...H]Obtaining y' 1 Column vector, equation y 2 Taking x 2 ＝[1,...W]Obtaining y' 2 Line vector according to y=y' 1 ×y' 2 A two-dimensional gaussian distribution diagram y is obtained.
Finally, the original image I and the corresponding point pixels on the two-dimensional Gaussian distribution diagram y are divided to remove Gaussian, so that an image Img after light shadow elimination is obtained, and the image Img after light shadow elimination is obtained by referring to FIG. 3, and the brightness of the image is more uniform than that of the original image I, so that the subsequent experimental treatment of the image is facilitated, and the experimental effect is improved.
Embodiment two:
referring to fig. 4, the present invention also provides an optical image light shadow eliminating system, comprising:
the input module is used for inputting an original image I with width W and height H;
the segmentation module is used for segmenting the original image I into n image small blocks with the same size;
the calculation module is used for calculating the gray level total value of each image small block;
the coordinate set acquisition module is used for acquiring the center point coordinates (I, j) of the image small block with the highest gray level total value, and taking all point coordinate sets of the column where x=i is located as l on the original image I W Taking the coordinate set of all points of the row where y=j is located as l H ；
A one-dimensional gray histogram acquisition module for acquiring a gray histogram according to the coordinate set l W Gray value calculation is performed to obtain l W According to the coordinate set l H Gray value calculation is performed to obtain l H Is a one-dimensional gray level histogram of (a);
a Gaussian equation acquisition module for acquiring the Gaussian equation according to the L W Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l W Is of the Gaussian equation y 1 According to l H Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l H Is of the Gaussian equation y 2 ；
Fitting module for according to y 1 And y 2 Fitting to obtain a two-dimensional Gaussian distribution diagram y;
and the Gaussian elimination module is used for carrying out Gaussian elimination according to the original image I and the two-dimensional Gaussian distribution diagram y so as to obtain an image Img after the light shadow elimination.
The above examples are merely illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the protection scope of the present invention without departing from the design spirit of the present invention.
Claims (10)
1. An optical image shadow elimination method, comprising the steps of:
s1, inputting an original image I with width W and height H, and dividing the original image I into n image small blocks in equal size;
s2, calculating the gray level total value of each image small block, and taking the center point coordinate (i, j) of the image small block with the maximum gray level total value as the strongest point of the shadow;
s3, taking all point coordinate sets of columns where x=i are located as l on the original image I W Taking the coordinate set of all points of the row where y=j is located as l H ；
S4, according to the coordinate set l W Gray value calculation is performed to obtain l W According to the coordinate set l H Gray value calculation is performed to obtain l H Is a one-dimensional gray level histogram of (a);
s5, according to l W Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l W Is of the Gaussian equation y 1 According to l H Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l H Is of the Gaussian equation y 2 ；
S6, according to y 1 And y 2 Fitting to obtain a two-dimensional Gaussian distribution diagram y;
s7, performing degaussing according to the original image I and the two-dimensional Gaussian distribution diagram y, and obtaining an image Img after light shadow elimination.
2. The method of claim 1, wherein: in step S4, according to the coordinate set l W Calculated to obtain (x) W ,y W ) Wherein x is W ∈(1，2，3......H)，y W E (f (1), f (2), f (3.. Fw.. F (H)), f function is a calculated gray value function, y W For being based on the coordinate set l W Calculating the corresponding gray value to obtain l W Is a one-dimensional gray scale histogram of (a).
3. The method of claim 2, wherein: in step S4, according to the coordinate set l H Calculated to obtain (x) H ,y H ) Wherein x is H ∈(1，2，3......W)，y H ∈(f(1),f(2),f(3)......f(W))，y H For being based on the coordinate set l H Calculating the corresponding gray value to obtain l H Is a one-dimensional gray scale histogram of (a).
4. A method of optical image light shadow elimination according to claim 3, wherein: in step S5, for l W Data (x) in one-dimensional gray level histogram of (1) W ,y W ) Performing Gaussian fitting calculation to obtain l W Is of the Gaussian equation y 1 。
5. The method of claim 4, wherein: in step S5, by the method of the pair l H Data (x) in one-dimensional gray level histogram of (1) H ,y H ) Performing Gaussian fitting calculation to obtain l H Is of the Gaussian equation y 2 。
6. The method of claim 5, wherein in step S6, the algorithm y is 1 Taking x 1 ＝[1,...H]Obtaining y' 1 Column vector, equation y 2 Taking x 2 ＝[1,...W]Obtaining y' 2 Line vector according to y=y' 1 ×y′ 2 A two-dimensional gaussian distribution diagram y is obtained.
7. The method according to claim 1, wherein in step S7, the original image I is divided by the pixels of the corresponding points on the two-dimensional gaussian distribution diagram y to perform degaussing, so as to obtain the image Img after the light shadow is removed.
8. The method according to claim 1, wherein in step S1, the n image patches are rectangular.
9. The method according to claim 1, wherein the original image I has w=1600 px and h=1200 px.
10. An optical image shadow elimination system, comprising:
the input module is used for inputting an original image I with width W and height H;
the segmentation module is used for segmenting the original image I into n image small blocks with the same size;
the calculation module is used for calculating the gray level total value of each image small block;
the coordinate set acquisition module is used for acquiring the center point coordinates (I, j) of the image small block with the highest gray level total value, and taking all point coordinate sets of the column where x=i is located as l on the original image I W Taking the coordinate set of all points of the row where y=j is located as l H ；
A one-dimensional gray histogram acquisition module for acquiring a gray histogram according to the coordinate set l W Gray value calculation is performed to obtain l W According to the coordinate set l H Gray value calculation is performed to obtain l H Is a one-dimensional gray level histogram of (a);
a Gaussian equation acquisition module for acquiring the Gaussian equation according to the L W Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l W Is of the Gaussian equation y 1 According to l H Carrying out Gaussian fitting calculation on the one-dimensional gray level histogram of (1) to obtain l H Is of the Gaussian equation y 2 ；
Fitting module for according to y 1 And y 2 Fitting to obtain a two-dimensional Gaussian distribution diagram y;
and the Gaussian elimination module is used for carrying out Gaussian elimination according to the original image I and the two-dimensional Gaussian distribution diagram y so as to obtain an image Img after the light shadow elimination.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010840972.7A CN111986113B (en) | 2020-08-20 | 2020-08-20 | Optical image shadow elimination method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010840972.7A CN111986113B (en) | 2020-08-20 | 2020-08-20 | Optical image shadow elimination method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111986113A CN111986113A (en) | 2020-11-24 |
| CN111986113B true CN111986113B (en) | 2024-03-22 |
Family
ID=73442327
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010840972.7A Active CN111986113B (en) | 2020-08-20 | 2020-08-20 | Optical image shadow elimination method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111986113B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101739678A (en) * | 2008-11-10 | 2010-06-16 | 财团法人工业技术研究院 | Method for detecting shadow of object |
| CN102231206A (en) * | 2011-07-14 | 2011-11-02 | 浙江理工大学 | Colorized night vision image brightness enhancement method applicable to automotive assisted driving system |
| KR20130073170A (en) * | 2011-12-23 | 2013-07-03 | 현대모비스 주식회사 | Device for processing image and method thereof |
| WO2014067296A1 (en) * | 2012-11-05 | 2014-05-08 | 深圳市恩普电子技术有限公司 | Method for identifying, tracing and measuring internal and external membranes of vessel |
| JP2017174296A (en) * | 2016-03-25 | 2017-09-28 | 隆夫 西谷 | Image processing device and image processing method |
| CN110060293A (en) * | 2019-04-24 | 2019-07-26 | 中国兵器科学研究院宁波分院 | A kind of defect detection performance boundary appraisal procedure of CT detection system |
| CN110136146A (en) * | 2019-05-17 | 2019-08-16 | 浙江理工大学 | SAR image Watershed segmentation method based on sinusoidal SPF distribution and Level Set Models |
| CN110443807A (en) * | 2019-06-27 | 2019-11-12 | 中国地质大学（武汉） | A kind of even carrying out image threshold segmentation method of uneven illumination based on luminance proportion |
| CN110487737A (en) * | 2019-09-26 | 2019-11-22 | 清华苏州环境创新研究院 | Image information for smart phone spectral detection is extracted and calculation method and system |
| CN110689601A (en) * | 2019-10-07 | 2020-01-14 | 绍兴秀我家科技有限公司 | Scatter elimination algorithm suitable for AR virtual soft package synthesis |
-
2020
- 2020-08-20 CN CN202010840972.7A patent/CN111986113B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101739678A (en) * | 2008-11-10 | 2010-06-16 | 财团法人工业技术研究院 | Method for detecting shadow of object |
| CN102231206A (en) * | 2011-07-14 | 2011-11-02 | 浙江理工大学 | Colorized night vision image brightness enhancement method applicable to automotive assisted driving system |
| KR20130073170A (en) * | 2011-12-23 | 2013-07-03 | 현대모비스 주식회사 | Device for processing image and method thereof |
| WO2014067296A1 (en) * | 2012-11-05 | 2014-05-08 | 深圳市恩普电子技术有限公司 | Method for identifying, tracing and measuring internal and external membranes of vessel |
| JP2017174296A (en) * | 2016-03-25 | 2017-09-28 | 隆夫 西谷 | Image processing device and image processing method |
| CN110060293A (en) * | 2019-04-24 | 2019-07-26 | 中国兵器科学研究院宁波分院 | A kind of defect detection performance boundary appraisal procedure of CT detection system |
| CN110136146A (en) * | 2019-05-17 | 2019-08-16 | 浙江理工大学 | SAR image Watershed segmentation method based on sinusoidal SPF distribution and Level Set Models |
| CN110443807A (en) * | 2019-06-27 | 2019-11-12 | 中国地质大学（武汉） | A kind of even carrying out image threshold segmentation method of uneven illumination based on luminance proportion |
| CN110487737A (en) * | 2019-09-26 | 2019-11-22 | 清华苏州环境创新研究院 | Image information for smart phone spectral detection is extracted and calculation method and system |
| CN110689601A (en) * | 2019-10-07 | 2020-01-14 | 绍兴秀我家科技有限公司 | Scatter elimination algorithm suitable for AR virtual soft package synthesis |
Non-Patent Citations (5)
| Title |
|---|
| 二维高斯分布光斑中心快速提取算法研究;冯新星;张丽艳;叶南;杨博文;光学学报;20121231;第32卷(第5期);全文 * |
| 基于互相关和改进高斯拟合的光斑中心提取方法;吴頔;吕且妮;卢琳;陈曦;葛宝臻;光电子·激光;20171231;第28卷(第2期);全文 * |
| 基于多点标记的罚球出手帧获取算法研究;孙锐兴;包晓敏;吕文涛;唐晓彤;汪亚明;;工业控制计算机;20170425(第04期);全文 * |
| 基于改进图像自适应算法的车道线识别研究;包晓敏;陈家凡;吴龙;吕文涛;杨晓城;;无线电通信技术;20170918(第05期);全文 * |
| 移动高光对三维场景重建的影响与消除;全思博;李伟光;;光学精密工程;20170715(07);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111986113A (en) | 2020-11-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yeh et al. | Multi-scale deep residual learning-based single image haze removal via image decomposition | |
| CN104463804B (en) | Image enhancement method based on intuitional fuzzy set | |
| CN108460757A (en) | A kind of mobile phone TFT-LCD screens Mura defects online automatic detection method | |
| CN103295010B (en) | A kind of unitary of illumination method processing facial image | |
| CN108182671B (en) | Single image defogging method based on sky area identification | |
| CN110175506B (en) | Pedestrian re-identification method and device based on parallel dimensionality reduction convolutional neural network | |
| CN112287765B (en) | Face living body detection method, device, equipment and readable storage medium | |
| CN112330613A (en) | Method and system for evaluating quality of cytopathology digital image | |
| CN112750089B (en) | Optical remote sensing image defogging method based on local block maximum and minimum pixel prior | |
| CN112784894B (en) | Automatic labeling method for rock slice microscopic image | |
| CN106952236B (en) | Fisheye lens shot image distortion correction method based on BP neural network | |
| CN110705568B (en) | Optimization method for image feature point extraction | |
| CN111986113B (en) | Optical image shadow elimination method and system | |
| CN117132503A (en) | Method, system, equipment and storage medium for repairing local highlight region of image | |
| CN109145875B (en) | Method and device for removing black frame glasses in face image | |
| CN104408430B (en) | License plate positioning method and device | |
| CN114998186B (en) | Method and system for detecting surface scab defect of copper starting sheet based on image processing | |
| CN113643290B (en) | Straw counting method and device based on image processing and storage medium | |
| CN111932469B (en) | Method, device, equipment and medium for fusing saliency weight fast exposure images | |
| CN108171750A (en) | The chest handling positioning identification system of view-based access control model | |
| Kajihara et al. | Feature-based non-rigid registration of serial section images by blending rigid transformations | |
| CN113781333A (en) | Method for processing underwater image by GAN network based on guided filtering | |
| CN112085683A (en) | Depth map reliability detection method in significance detection | |
| CN110647843A (en) | Face image processing method | |
| Han et al. | Single image dehazing method via sky-regions segmentation and dark channel prior |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |