CN103065334A - Color cast detection and correction method and device based on HSV (Hue, Saturation, Value) color space - Google Patents
Color cast detection and correction method and device based on HSV (Hue, Saturation, Value) color space Download PDFInfo
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Abstract
The invention provides a color cast detection and correction method and device, relating to the technical field of digital image processing. The method comprises the following steps of: inputting an image to be detected of an RGB (Red, Green, Blue) color space; calculating an S channel in the HSV (Hue, Saturation, Value) color space; counting the histogram vector h of the S channel; quantizing and filtering the histogram vector h; judging whether color cast exists in the image according to a histogram vector h' obtained after the quantizing and the filtering; if the image is judged to has color cast, respectively counting the contribution rates of an RGB channel to the maximum matrix and the minimum matrix, and judging a color cast channel according to the contribution rates; adjusting the value of the color cast channel in the RGB color space, recalculating the value of the S channel and judging whether color cast exists in the S channel, and repeating to execute until no color cast exists; and if the image is judged to have no color cast, ending the program. By using the color cast detection and correction method and device, various types of color cast images can be effectively detected and corrected, and the defects in the traditional method are overcome; and the color cast detection and correction method and device are favorable in adaptability to different scenes and high in algorithm efficiency.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to application of algorithms such as color space conversion, histogram statistics and median filtering in color cast detection and correction.
Background
Color is one of the important features of an image and is also an important basis for image processing and analysis. In a physical sense, the color of an object as viewed by the human eye is not only determined by its reflective properties, but also by the color of the light directed at the object. The human visual system has color constancy, can eliminate the influence of factors such as illumination conditions on the color to a certain extent, and accurately sense the color of an object. However, the imaging device does not have such a "adjustment" function, and the acquired image often has a certain degree of error, i.e. color cast, with the true color of the surface of the object. The color cast phenomenon is determined by different color temperatures of the external light source, and the degree of color cast is directly related to the change of the color temperature of the external light source.
The automatic white balance algorithm is an algorithm for detecting and correcting color cast of an image, and aims to reduce color difference caused by an external light source as much as possible, compensate color cast phenomenon caused by non-ideal characteristics of an external device, restore the original color of a photographed target under different color temperature conditions, keep color constancy of different equipment and different observation environments and enable a processed image to conform to the habit of human vision as much as possible.
With the increasing popularization of technologies such as internet, multimedia and the like, the application range of color images is continuously expanded, the detection and correction of color cast are more and more important, and accurate and effective color cast detection and correction can provide effective references for subsequent digital image processing, mode recognition, image retrieval and the like.
In the current color cast detection and correction technology, the classical algorithm mainly comprises: gray World algorithm (Gray World), Perfect reflection algorithm (Perfect Reflector), Fuzzy Neural Network algorithm (Fuzzy Neural Network), white balance algorithm based on edge detection, and improved algorithm based on the above algorithms. The grey world algorithm is based on the grey world assumption of Von-Kries coefficient theory, which assumes: for an image with sufficient color variation, the statistical average of the R, G, B three color components should tend to be the same gray value. The perfect reflection method has the advantages of simple calculation and good restoration effect when high-brightness areas exist in the image. However, the detection of white spots often lacks versatility, and does not work well when no mirror surface is present in the image or the image brightness is low. However, the current classical algorithms all have certain disadvantages, such as: for images with monotonous colors or images containing large-area color blocks, the gray world algorithm often has the problem of correction failure and sometimes generates over correction; when no mirror surface exists in the image or the image brightness is low, the perfect reflection algorithm is not good; edge-based white balance algorithms often fail when large-scale colored textures appear in the image; for the fuzzy neural network algorithm, a large amount of prior knowledge needs to be learned, and the problems of high logic unit consumption, high power consumption and the like exist. The algorithm provided by the invention can effectively detect different types of color cast images, has low algorithm complexity, can effectively overcome the problem of correction failure of the classical algorithm, and is suitable for various different scenes.
Disclosure of Invention
The embodiment of the invention provides a color cast detection and correction method based on an HSV color space, and the algorithm can effectively detect and correct different types of color cast images, is low in complexity and is suitable for various different scenes.
The invention adopts the following technical scheme:
1. inputting an image to be detected (generally, an RGB color space);
2. calculating an S channel in the HSV color space;
3. counting a histogram vector h of the S channel;
4. quantizing and filtering the histogram vector h;
5. judging whether the image is color cast according to the quantized and filtered histogram vector h';
6. if the image color cast is judged, respectively counting the contribution rates of the RGB channels to the maximum matrix and the minimum matrix, and judging the color cast channel according to the contribution rates;
7. in the RGB color space, adjusting the value of the color cast channel, and returning to the step 2;
8. if the image is determined not to be color-cast, the process ends.
An image color cast detection and correction apparatus comprising:
the calculation unit is used for calculating the value of an S channel in the HSV color space and counting a histogram vector h of the S channel;
a preprocessing unit for quantizing and filtering the histogram vectors;
the judging unit judges whether the image has color cast or not, and the program is ended;
the determining unit is used for judging a color cast channel;
and the correcting unit is used for correcting the color cast image and returning to the statistical unit.
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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 described below are only part of the drawings of 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 flowchart of a color cast detection and correction method provided in embodiment 1 of the present invention.
FIG. 2 is a graph comparing the effects of the present invention and the conventional method; FIG. 2(a) a patch test pattern lacking highlight regions; FIG. 2(b) is a graph of the algorithm results of the present invention; FIG. 2(c) a graph of the gray world algorithm results; FIG. 2(d) is a graph of the results of the perfect reflection algorithm.
[ Special description ] of: since fig. 2, which was submitted as a black and white picture, does not show the better contrast effect of the present invention, a color picture 2 was submitted in other documentations.
Fig. 3 is a structural diagram of a color cast detection and correction device provided in embodiment 2 of the present invention.
Fig. 4 is a structural diagram of the calculation unit 11 provided in embodiment 2 of the present invention.
Fig. 5 is a structural diagram of the preprocessing unit 12 provided in embodiment 2 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
an embodiment 1 of the present invention provides a color cast detection and correction method based on an HSV color space, as shown in fig. 1, the method includes:
101. inputting to-be-detected image of RGB color space
Color Space (Color Space), also known as a Color coordinate system, is a method of abstracting and describing colors. The RGB color space is one of the most common color models, and images output by digital imaging devices are often in RGB format.
102. Computing S-channels in HSV color space
The existing color cast detection algorithm usually converts the RGB color space into the HSV color space, and then extracts the S channel for color cast detection, while the H channel and the V channel do not participate in the subsequent processing process. The invention adopts a method of directly calculating the S channel, thereby greatly reducing the calculation amount and reducing the time and space overhead of the algorithm. The formula for calculating the S channel in HSV color space from RGB color space is as follows:
1) statistical maximum and minimum value matrix
2) calculating the value of S channel
103. Histogram vector h of statistical S channel
Obtaining a histogram of an image is well known to those skilled in the art and will not be described in detail herein.
104. Quantizing and filtering the histogram vector h
The quantization and filtering of the histogram vectors can be seen as a pre-processing step of the data.
1) And (3) quantification: quantizing the histogram vector, and reserving 4 bits after the decimal point, wherein the 5 th bit is cut into 5 according to 4;
2) improved one-dimensional median filtering is adopted:
Wherein,
the median filter function is represented.
105. Judging whether the image has color cast according to the histogram vector h' after quantization and filtering
If the first N elements in the histogram vector satisfy one of the following conditions, the image is determined to be color-biased, otherwise, the image is determined not to be color-biased (according to statistics of various types of images, N is preferably 15-25):
1) the values of the first N elements are all zero or very close to zero;
setting a threshold TVTo quantify the degree of approach to zero. If the average of the first N elements is less than the threshold, the values of the first N elements are considered to be very close to zero.
2) The values of the first N elements show a phenomenon of oscillation, namely: multiple occurrences of a local maximum and minimum;
setting a threshold TN(3-5) for quantifying the number of occurrences of the local maximum and minimum. If the calculated number of times is greater than the threshold, the first N elements are considered to exhibit an oscillation phenomenon.
Is provided with
To representThe first N elements in the histogram vector, let:
equation 4
Using function statisticsIs obtained by sign change inThe number of times a local maximum minimum occurs in the sequence.
106. If the image color cast is judged, the contribution rates of the RGB channels to the maximum matrix and the minimum matrix are respectively counted, and the color cast channel is judged according to the contribution rates
Here, the contribution rate of the RGB channel to the maximum and minimum value matrices refers to a ratio of the number of pixels of a channel providing the maximum value or the minimum value divided by the number of all pixels of the channel when counting the maximum and minimum value matrices. Taking the R channel as an example, the contribution ratio to the maximum and minimum value matrices is calculated by the following formula:
Wherein,
the counting function is used for counting the number of pixel points meeting the conditions in the matrix.
Maximum contribution rate of RGB channel
Respectively subtracting the corresponding minimum contribution rates
And obtaining three difference values, wherein the channel corresponding to the minimum value is the color cast channel.
107. And in the RGB color space, adjusting the value of the color cast channel and returning to the step 2.
And (3) multiplying the numerical value of the color cast channel by a correction coefficient k (the value of k is 1.02-1.05) to obtain a new image, and repeating the step 2.
108. If the image is determined not to be color-cast, the process ends.
Fig. 2 is a graph comparing the effect of the present invention with the conventional method (gray world algorithm and perfect reflection algorithm). FIG. 2(a) is a color block test image without highlight, which shows a yellowish color; FIG. 2(b) is the correction result by the algorithm, and it can be seen from the figure that the adjusted color block is pure and accords with the visual effect of human eyes; fig. 2(c) is a correction result using the gray world algorithm, the image is somewhat bluish, showing that the white balance correction of the algorithm is insufficient due to: the test image contains large-area color blocks, and the average value of the RGB three channels does not accord with the precondition assumption of a gray world algorithm; fig. 2(d) is a correction result using a perfect reflection algorithm, which substantially matches the visual effect of human eyes, but also has a yellowish phenomenon (overcorrection) due to the following reasons: there are no distinct highlighted areas in the image, so that the standard white assignment is biased. From experimental results of a plurality of actual color cast images, the algorithm improves automatic white balance adjustment, has a good correction effect on different types of color cast images, and shows wide applicability of the algorithm.
Example 2:
an embodiment 2 of the present invention provides a color cast detection and correction device based on an HSV color space, and as shown in fig. 3, the method includes: a calculation unit 11, a preprocessing unit 12, a judgment unit 13, a determination unit 14 and a correction unit 15.
Wherein,
the calculation unit 11 is configured to calculate a value of an S channel in the HSV color space, and count a histogram vector h of the S channel;
a preprocessing unit 12 for quantizing and filtering the histogram vectors;
a judging unit 13 for judging whether the image is color-cast;
a determination unit 14 for determining a color cast channel;
and the correcting unit 15 is used for correcting the color cast channel of the image and returning to the statistical unit.
Further, as shown in fig. 4, the calculation unit 11 includes: a calculation module 111, a statistics module 112; as shown in fig. 4, the preprocessing unit 12 includes: a quantization module 121 and a filtering module 122.
When the calculating unit 11 shown in fig. 4 is used to calculate S channel and count histogram thereof, the specific process can be seen in the methods 102 and 103 in embodiment 1.
When the preprocessing unit 12 shown in fig. 5 implements quantization and filtering, the specific process can be referred to the method shown as 104 in embodiment 1.
The embodiment of the invention is mainly applied to the processing of the color cast detection of the image, the technical scheme provided by the embodiment of the invention is not limited by scenes or priori knowledge, has universal adaptability, and simultaneously improves the accuracy and the reliability of the color cast detection.
Claims (13)
1. A color cast detection and correction method based on HSV color space comprises the following steps:
(1) inputting an image to be detected;
(2) calculating the value of an S channel in the HSV color space;
(3) counting a histogram vector h of the S channel;
(4) quantizing and filtering the histogram vector h;
(5) judging whether the image is color cast according to the quantized and filtered histogram vector h';
(6) if the image color cast is judged, respectively counting the contribution rates of the RGB channels to the maximum matrix and the minimum matrix, and judging the color cast channel according to the contribution rates;
(7) in the RGB color space, adjusting the value of the color cast channel, and returning to the step 2;
(8) if the image is determined not to be color-cast, the process ends.
2. The method of calculating the value of the S channel of claim 1 by: 1) and counting the maximum value matrix and the minimum value matrix of the S channel, and respectively recording as:(ii) a 2) Calculating the value of S channel by
And (5) obtaining the value of the S channel by a formula.
3. The HSV color space-based color cast detection and correction method of claim 1, wherein: and (4) quantizing the histogram vector h by reserving 4 bits after decimal point of a quantized value, and rounding 5 in the 5 th bit according to 4.
4. The HSV color space-based color cast detection and correction method of claim 1, wherein: and (4) filtering the histogram vector h by using an improved one-dimensional median filtering formula:
wherein,is the median filter function.
5. The HSV color space-based color cast detection and correction method of claim 1, wherein: and (5) judging whether the histogram vector h 'is color cast or not by taking the first N elements in the histogram vector h' for judgment, wherein the value range of N is a positive integer between 15 and 25.
6. The HSV color space-based color cast detection and correction method of claim 1, wherein: the method for judging whether the histogram vector h 'is color cast in the step (5) is to take the average value of the first N elements of the histogram vector h' and the threshold value TVComparing if the average value is less than the threshold value TVThen a color cast is considered, threshold TVThe value of (a) is in the range of 0.0002 to 0.0005.
7. The HSV color space-based color cast detection and correction method of claim 1, wherein: the method for judging whether the histogram vector h 'is color cast in the step (5) is to take the values of the first N elements of the histogram vector h', if the number of the calculated local maximum and minimum values is greater than the threshold value TNIf the color is color cast, the threshold value TNThe value of (a) is a positive integer between 3 and 5.
8. The statistical method of the local range occurrence of the maximum and minimum values according to claim 7
And
and (5) formula statistics.
9. The HSV color space-based bias of claim 1The color detection and correction method is characterized in that: step (6) determining the color cast channel method is to determine the maximum value contribution rate of each RGB channel
Respectively subtracting the corresponding minimum contribution ratesAnd obtaining three difference values, wherein the channel corresponding to the minimum value is the color cast channel, and the contribution rate of the maximum and minimum value matrixes is the ratio of the number of pixels of the maximum or minimum value provided by a certain channel to the number of all pixels of the channel when counting the maximum and minimum value matrixes.
10. The HSV color space-based color cast detection and correction method of claim 1, wherein: and (5) the method for adjusting the color cast channel value in the step (7) is to multiply the value of the color cast channel by a correction coefficient k, wherein the value range of k is 1.02-1.05.
11. An apparatus for detecting and correcting color cast of an image, comprising:
the calculation unit is used for calculating the value of an S channel in the HSV color space and counting a histogram vector h of the S channel;
a preprocessing unit for quantizing and filtering the histogram vectors;
a judging unit for judging whether the image has color cast or not, if the average value of the first N elements in the histogram vector is less than the preset threshold value TVOr the number of times of local maximum minimum value appearing in the first N elements in the histogram vector is more than a threshold value TNIf the input image is determined to be color-cast, otherwise, the input image is determined not to be color-cast, the program is ended, the value range of N is a positive integer between 15 and 25, and the threshold value T isVThe value range of (1) is 0.0002-0.0005, and the threshold value TNThe value range of (1) is a positive integer between 3 and 5;
a determining unit for determining color cast channel and determining maximum contribution rate of each RGB channel
Respectively subtracting the corresponding minimum contribution rates
Obtaining three difference values, wherein the channel corresponding to the minimum value is a color cast channel;
and the correction unit is used for correcting the color cast image, multiplying the numerical value of the color cast channel by a correction coefficient k (k takes the value of 1.02-1.05), and returning to the statistical unit.
12. The computing unit of claim 11, comprising:
a calculation module for calculating S channel value in HSV color space by using formula
The maximum and minimum matrices of the S channel are respectively noted as: MX and MN;
and the statistical module is used for counting the square vector h of the S channel.
13. The pre-processing unit of claim 11, comprising:
the quantization module is used for quantizing the histogram vector by a method of reserving 4 bits after decimal point of a quantized value, and the 5 th bit is divided into 5 bits according to 4 bits;
the filtering module is used for filtering the histogram vector and adopting an improved one-dimensional median filtering formula to carry out filtering, wherein the formula is as follows:
wherein,
is the median filter function.
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