CN111986113A - Optical image shadow eliminating method and system - Google Patents
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Abstract
The invention discloses a method and a system for eliminating light and shadow of an optical image. The method comprises the following steps: s1, inputting an original image I with the width W and the height H, and dividing the original image I into n image small blocks in the same size; s2, calculating the total gray value of each image small block, and taking the central point coordinate (i, j) of the image small block with the maximum total gray value as the strongest light and shadow point; s3, taking the coordinate set of all points in the column where x ═ I is located as l on the original image IWTaking the coordinate set of all points in the row where y is equal to j as lH(ii) a S4, according to the coordinate set lW、lHRespectively performing gray value calculation to obtain lW、lHRespective one-dimensional gray level histograms; s5, according to lW、lHRespectively carrying out Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lW、lHRespective gaussian equation y1、y2(ii) a S6, according to y1、y2Fitting to obtain a two-dimensional Gaussian distribution diagram y; s7, according to the original image IAnd removing gausses from the two-dimensional Gaussian distribution map y to obtain an image Img with the shadow eliminated. The invention adopts a two-dimensional Gaussian fitting mode to fit the light and shadow of the optical image, 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 eliminating method and system.
Background
In image analysis, the quality of image quality directly affects the precision of the design and effect of recognition algorithm, so that preprocessing is required before image analysis (feature extraction, segmentation, matching, recognition, etc.). The main purposes of image preprocessing are to eliminate irrelevant information in images, recover useful real information, enhance the detectability of relevant information, and simplify data to the maximum extent, thereby improving the reliability of feature extraction, image segmentation, matching and recognition.
For some images with uneven brightness distribution caused by the influence of light and shadow, effective preprocessing is more beneficial to subsequent processing, so that images with higher quality can be obtained by an image preprocessing method, and the effect of subsequent experiments is improved.
Disclosure of Invention
The invention aims to provide a method and a system for eliminating optical image shadows, which aim to overcome the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
an optical image shadow elimination method, comprising the steps of:
s1, inputting an original image I with the width W and the height H, and dividing the original image I into n image small blocks in the same size;
s2, calculating the total gray value of each image small block, and taking the central point coordinate (i, j) of the image small block with the maximum total gray value as the strongest light and shadow point;
s3, taking the coordinate set of all points in the column where x ═ I is located as l on the original image IWTaking the coordinate set of all points in the row where y is equal to j as lH；
S4, according to the coordinate set lWPerforming gray value calculation to obtain lWAccording to the coordinate set lHPerforming gray value calculation to obtain lHOne dimension of (A)A gray level histogram;
s5, according to lWPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lWGaussian equation y of1According to lHPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lHGaussian equation y of2；
S6, according to y1And y2Fitting to obtain a two-dimensional Gaussian distribution diagram y;
and S7, removing gausses according to the original image I and the two-dimensional Gaussian distribution map y to obtain an image Img with the shadow eliminated.
Preferably, in step S4, the method further includes the step of calculating a coordinate set lWIs calculated to obtain (x)W,yW) Wherein x isW∈(1，2，3......H)，yWE (f (1), f (2), f (3).. f (H)), wherein the f function is a function for calculating gray values, and y is a function for calculating gray valuesWAccording to a coordinate set lWCalculating the corresponding gray value to obtain lWA one-dimensional gray-scale histogram of (a).
Preferably, in step S4, the method further includes the step of calculating a coordinate set lHIs calculated to obtain (x)H,yH) Wherein x isH∈(1，2，3......W)，yH∈(f(1),f(2),f(3)......f(W))，yHAccording to a coordinate set lHCalculating the corresponding gray value to obtain lHA one-dimensional gray-scale histogram of (a).
Preferably, in step S5, l isWData (x) in one-dimensional gray histogram of (a)W,yW) Performing a Gaussian fitting calculation to obtain lWGaussian equation y of1。
Preferably, in step S5, l is selectedHData (x) in one-dimensional gray histogram of (a)H,yH) Performing a Gaussian fitting calculation to obtain lHGaussian equation y of2。
Preferably, in step S6, the process y is executed1Get x1＝[1,...H]To give y'1Column vector, equation y2Get x2＝[1,...W]To give y'2Line vector according to y ═ y'1×y'2And obtaining a two-dimensional Gaussian distribution diagram y.
Preferably, in step S7, the original image I and the corresponding point pixel on the two-dimensional gaussian distribution map y are divided by degussing to obtain an image Img with the shadow removed.
Preferably, in step S1, the n image tiles are all rectangular.
Preferably, W of the original image I is 1600px and H is 1200 px.
The present invention also provides an optical image shadow eliminating system, comprising:
the input module is used for inputting an original image I with the width W and the height H;
the segmentation module is used for segmenting the original image I into n image small blocks with equal size;
the calculation module is used for calculating the total gray value of each image small block;
a coordinate set obtaining module, configured to obtain a coordinate (I, j) of a center point of the image patch with the highest total gray value, and take a coordinate set of all points in a column where x is I as l on the original image IWTaking the coordinate set of all points in the row where y is equal to j as lH；
A one-dimensional gray histogram acquisition module for acquiring the gray histogram according to the coordinate set lWPerforming gray value calculation to obtain lWAccording to the coordinate set lHPerforming gray value calculation to obtain lHA one-dimensional gray level histogram of (a);
a Gaussian equation obtaining module for obtaining the equation according to lWPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lWGaussian equation y of1According to lHPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lHGaussian equation y of2；
Fitting module for fitting according to y1And y2Fitting to obtain a two-dimensional Gaussian distribution diagram y;
and the Gaussian removing module is used for removing Gaussian according to the original image I and the two-dimensional Gaussian distribution diagram y so as to obtain the image Img with the shadow removed.
The invention has the beneficial effects that: for some images with uneven brightness distribution caused by the shadow effect, a two-dimensional Gaussian fitting mode is adopted to fit the shadow of the optical image, so that an effective preprocessing effect is obtained, the brightness in the image is more uniform, the follow-up experimental treatment on the image is facilitated, and the experimental effect is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an optical image light and shadow removal method;
FIG. 2 is an unprocessed original image I;
FIG. 3 is an image Img after the original image I has been subjected to a matte finish;
fig. 4 is a schematic structural diagram of an optical image light and shadow elimination system.
Detailed Description
The following description of the embodiments of the present invention is provided by way of specific examples, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure herein. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
The first embodiment is as follows:
referring to fig. 1, the present embodiment provides an optical image shadow elimination method, including the steps of:
s1, inputting an original image I with the width W and the height H, and dividing the original image I into n image small blocks in the same size;
s2, calculating the total gray value of each image small block, and taking the central point coordinate (i, j) of the image small block with the maximum total gray value as the strongest light and shadow point;
s3, taking the coordinate set of all points in the column where x ═ I is located as l on the original image IWTaking the coordinate set of all points in the row where y is equal to j as lH；
S4, according to the coordinate set lWPerforming gray value calculation to obtain lWAccording to the coordinate set lHPerforming gray value calculation to obtain lHA one-dimensional gray level histogram of (a);
s5, according to lWPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lWGaussian equation y of1According to lHPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lHGaussian equation y of2；
S6, according to y1And y2Fitting to obtain a two-dimensional Gaussian distribution diagram y;
and S7, removing gausses according to the original image I and the two-dimensional Gaussian distribution map y to obtain an image Img with the shadow eliminated.
Specifically, the method comprises the following steps:
referring to fig. 2, the present embodiment will be described by taking as an example that W of the input original image I is 1600px and H is 1200px, and the original image I is divided into 4 pieces in average in width and length, that is, into 16 rectangles of equal size.
After the total gray scale value is calculated in step S2, the coordinate of the center point of the image patch with the maximum total gray scale value is (915,463), that is, the center point is the strongest point of light and shadow, and the coordinate set of all points in the original image I where x equals 915 is taken as lWTaking the coordinate set of all points in the row where y is 463 as lH。
In step S4, according to the coordinate set lWIs calculated to obtain (x)W,yW) Wherein x isW∈(1,...1200)，yWE (f (1) · f (1200)), the f function being the function of calculating the grey values, yWAccording to a coordinate set lWCalculating the corresponding gray value to obtain lWA one-dimensional gray level histogram of (a); according to a set of coordinates lHIs calculated to obtain (x)H,yH) Wherein x isH∈(1,...1600)，yH∈(f(1),...f(1600))，yHAccording to a coordinate set lHCalculating the corresponding gray value to obtain lHA one-dimensional gray-scale histogram of (a).
In step S5, l is pairedWData (x) in one-dimensional gray histogram of (a)W,yW) Performing a Gaussian fitting calculation to obtain lWGaussian equation y of1By making a pair lHData (x) in one-dimensional gray histogram of (a)H,yH) Performing a Gaussian fitting calculation to obtain lHGaussian equation y of2。
The principle of gaussian fitting is as follows:
is provided with a set of test data (x)i,yi) (i ═ 1,2, 3.) can be described by a gaussian function:
where the parameter y to be estimatedmax、xmaxAnd S are the peak, peak position and half-width information of the gaussian curve, respectively. Taking natural logarithm on two sides of the formula (1) and converting into:
order:
and considering all experimental data, equation (3) is expressed in matrix form as:
for brevity, this is:
Z＝XB (5)
according to the least squares principle, the generalized least squares solution of the constructed matrix B is:
B＝(XTX)-1XTZ (6)
then, the parameter y to be estimated is solved according to the formula (3)max、xmaxAnd S, the parameter y to be estimatedmax、xmaxSubstituting S into the Gaussian function of formula (1) to obtain the desired test data (x)i,yi) (i ═ 1,2, 3. -) gaussian.
From the above Gaussian fitting principle, we can know that when we need to require lWGaussian equation y of1Only the test data (x) are requiredi,yi) (i ═ 1,2, 3..) is replaced by (x)W,yW) Performing subsequent calculation, when we need to require lHGaussian equation y of2Only the test data (x) are requiredi,yi) (i ═ 1,2, 3..) is replaced by (x)H,yH) And carrying out subsequent calculation.
In obtaining lWGaussian equation y of1And lHGaussian equation y of2Then, the equation y1Get x1＝[1,...H]To give y'1Column vector, equation y2Get x2＝[1,...W]To give y'2Line vector according to y ═ y'1×y'2And obtaining a two-dimensional Gaussian distribution diagram y.
And finally, removing gausses by dividing the original image I and the corresponding point pixels on the two-dimensional Gaussian distribution map y to obtain an image Img with the eliminated light and shadow, and referring to fig. 3, namely the image Img with the eliminated light and shadow, wherein 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.
Example two:
referring to fig. 4, the present invention also provides an optical image light and shadow eliminating system, including:
the input module is used for inputting an original image I with the width W and the height H;
the segmentation module is used for segmenting the original image I into n image small blocks with equal size;
the calculation module is used for calculating the total gray value of each image small block;
a coordinate set obtaining module, configured to obtain a coordinate (I, j) of a center point of the image patch with the highest total gray value, and take a coordinate set of all points in a column where x is I as l on the original image IWTaking the coordinate set of all points in the row where y is equal to j as lH；
A one-dimensional gray histogram acquisition module for acquiring the gray histogram according to the coordinate set lWPerforming gray value calculation to obtain lWAccording to the coordinate set lHPerforming gray value calculation to obtain lHA one-dimensional gray level histogram of (a);
a Gaussian equation obtaining module for obtaining the equation according to lWPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lWGaussian equation y of1According to lHPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lHGaussian equation y of2；
Fitting module for fitting according to y1And y2Fitting to obtain a two-dimensional Gaussian distribution diagram y;
and the Gaussian removing module is used for removing Gaussian according to the original image I and the two-dimensional Gaussian distribution diagram y so as to obtain the image Img with the shadow removed.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention by those skilled in the art 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 is characterized by comprising the following steps:
s1, inputting an original image I with the width W and the height H, and dividing the original image I into n image small blocks in the same size;
s2, calculating the total gray value of each image small block, and taking the central point coordinate (i, j) of the image small block with the maximum total gray value as the strongest light and shadow point;
s3, taking the coordinate set of all points in the column where x ═ I is located as l on the original image IWTaking the coordinate set of all points in the row where y is equal to j as lH；
S4, according to the coordinate set lWPerforming gray value calculation to obtain lWAccording to the coordinate set lHPerforming gray value calculation to obtain lHA one-dimensional gray level histogram of (a);
s5, according to lWPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lWGaussian equation y of1According to lHPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lHGaussian equation y of2；
S6, according to y1And y2Fitting to obtain a two-dimensional Gaussian distribution diagram y;
and S7, removing gausses according to the original image I and the two-dimensional Gaussian distribution map y to obtain an image Img with the shadow eliminated.
2. The optical image shadow removal method according to claim 1, wherein: in step S4, according to the coordinate set lWIs calculated to obtain (x)W,yW) Wherein x isW∈(1，2，3......H)，yWE (f (1), f (2), f (3).. f (H)), wherein the f function is a function for calculating gray values, and y is a function for calculating gray valuesWAccording to a coordinate set lWCalculating the corresponding gray value to obtain lWA one-dimensional gray-scale histogram of (a).
3. The optical image shadow removal method according to claim 2, wherein: in step S4, according to the coordinate set lHIs calculated to obtain (x)H,yH) Wherein x isH∈(1，2，3......W)，yH∈(f(1),f(2),f(3)......f(W))，yHAccording to a coordinate set lHCalculating the corresponding gray value to obtain lHA one-dimensional gray-scale histogram of (a).
4. The optical image shadow elimination method according to claim 3, characterized in that: in step S5, l is pairedWData (x) in one-dimensional gray histogram of (a)W,yW) Performing a Gaussian fitting calculation to obtain lWGaussian equation y of1。
5. The optical image shadow elimination method according to claim 4, characterized in that: in step S5, by pair lHData (x) in one-dimensional gray histogram of (a)H,yH) Performing a Gaussian fitting calculation to obtain lHGaussian equation y of2。
6. The method for removing shadow in optical image as claimed in claim 5, wherein in step S6, the equation y is set1Get x1＝[1,...H]To give y'1Column vector, equation y2Get x2＝[1,...W]To give y'2Line vector according to y ═ y'1×y′2And obtaining a two-dimensional Gaussian distribution diagram y.
7. The method of claim 1, wherein in step S7, the original image I is divided by the corresponding point pixel on the two-dimensional gaussian distribution map y to remove gaussian, thereby obtaining the image Img after shadow removal.
8. The method according to claim 1, wherein in step S1, the n image patches are all rectangular.
9. The method as claimed in claim 1, wherein W1600 px and H1200 px of the original image I.
10. An optical image light and shadow elimination system, comprising:
the input module is used for inputting an original image I with the width W and the height H;
the segmentation module is used for segmenting the original image I into n image small blocks with equal size;
the calculation module is used for calculating the total gray value of each image small block;
a coordinate set obtaining module, configured to obtain a coordinate (I, j) of a center point of the image patch with the highest total gray value, and take a coordinate set of all points in a column where x is I as l on the original image IWTaking the coordinate set of all points in the row where y is equal to j as lH；
A one-dimensional gray histogram acquisition module for acquiring the gray histogram according to the coordinate set lWPerforming gray value calculation to obtain lWAccording to the coordinate set lHPerforming gray value calculation to obtain lHA one-dimensional gray level histogram of (a);
a Gaussian equation obtaining module for obtaining the equation according to lWPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lWGaussian equation y of1According to lHPerforming Gaussian fitting calculation on the one-dimensional gray level histogram to obtain lHGaussian equation y of2；
Fitting module for fitting according to y1And y2Fitting to obtain a two-dimensional Gaussian distribution diagram y;
and the Gaussian removing module is used for removing Gaussian according to the original image I and the two-dimensional Gaussian distribution diagram y so as to obtain the image Img with the shadow removed.
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