CN110319933B - Light source spectrum optimization method based on CAM02-UCS color appearance model - Google Patents
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Abstract
The invention provides a light source spectrum optimization method based on a CAM02-UCS color appearance model, which comprises the following steps: firstly, establishing an imaging model, calculating an original pixel value of an image, and detecting pixel point coordinates of a target area by adopting a saliency detection method; then mapping the detected pixel point set of the target area into a CAM02-UCS color appearance model, establishing a target function of the maximized color difference value, and further optimizing the target function by utilizing a genetic algorithm to obtain a pixel value of the maximized color difference value; and finally, solving the optimal light source spectral power according to the pixel value. The invention has the beneficial effects that: the technical scheme provided by the invention combines an LED light source spectrum model, an optimization algorithm and a visual description method of a color gamut model, changes two important visual color factors of chroma and hue by optimizing the light source spectrum, and can visually see the optimization effect of the light source.
Description
Technical Field
The invention relates to the field of image optimization, in particular to a light source spectrum optimization method based on a CAM02-UCS color appearance model.
Background
In recent years, with the advent of low-cost, high-performance, and high-efficiency image processing systems, machine vision inspection has been widely used in product appearance, defect localization, barcode recognition, and the like. The sensitivity of a CCD camera, the speed of a CPU of a computer and the acquisition speed of an image acquisition card are improved. On the other hand, as the prices of software and hardware are reduced, more and more users are provided, the importance of the machine vision lighting system is widely known, and the investment in the aspect of lighting sources is increased, so that the research on the machine vision lighting system is deepened more and more. The machine vision system uses a video camera, a camera and the like to acquire image signals corresponding to a target, and the image signals are processed by the image processing system, so that the target is detected, tracked and identified by a computer, and finally, the automatic control of instruments and equipment, the detection of product defects, the quality improvement and the operation efficiency are realized.
The image quality and program algorithm in the machine vision system determine the processing speed and quality of the processing system, and the quality of the image is very critical to the whole vision system. The light source is an important factor affecting the image level of the machine vision system, since it directly affects the quality of the input data and the effect of at least 30% of the applications. The image quality is largely determined by the illumination environment around the target, the surface material of the target object, and the placement position of the object. The good illumination environment can effectively highlight the identification target of an object, and the target information and the background information in the image can be optimally separated through proper light source illumination design, so that a high-quality image which can be analyzed by a computer is obtained, the algorithm difficulty of image processing is greatly reduced, and the precision and the reliability of the system are improved. Therefore, the design quality of the illumination system is directly related to whether the acquisition equipment can obtain high-quality images, and a good light source is the guarantee that the machine vision system can operate efficiently. The main purpose of the design of the illumination system is to irradiate light rays on the surface of a detection target in an optimal mode and highlight characteristic information to be detected of the detected target, so that the illumination system is a key part of the whole machine vision system.
An excellent lighting system can separate the acquired image target information of the detected target from the background information, thereby simplifying subsequent analysis and being related to whether the whole system can normally operate. False illumination can cause a number of post-processing problems, for example, too high a light intensity can cause a number of important information to be lost, and shadows can cause errors in the detection of the contour dimensions. In industrial detection, due to the existence of various detection objects, in order to obtain stable and high-quality images, the most suitable lighting system needs to be selected for different targets, sometimes even different layouts of multiple light sources need to be combined, and the optimal light source combination and layout can be obtained through a large number of experimental tests. As a very practical technology, the method is widely applied to the fields of medical images, nondestructive detection, remote sensing measurement and the like, particularly the fields beyond visual limit, and can be used for analyzing abnormal phenomena of object colors caused by mixing of different substances, such as food pollution, skin diseases, resource detection, material evidence identification, printing anti-counterfeiting, crop plant diseases and insect pests and the like.
Most of the traditional machine vision processes the original image to be smooth, filtered, contrast enhanced and other preprocessing to obtain more definite field description information. Preprocessing does not add to the inherent information of the image data, only the high quality raw image contains more information available for further analysis. Raw image acquisition is strongly influenced by lighting conditions and camera parameters (intensity, color and relative position, etc.). The quality (information quantity) of an original image is improved by improving the lighting condition, and the method has very important significance for the post machine vision processing.
The object seen by human eyes reflects the light on the surface to human eyes, and the camera can capture the image of the point on the object, because the light on the point on the object is captured by the camera, so the image quality needs to be studied, and the imaging process and the change of the light brightness need to be studied. The purpose of finding the optimal illumination is to actively construct an optimal computer vision imaging environment, and the better the imaging environment is, the higher the quality of the obtained image is. Most of machine vision algorithm researches put the center of gravity on how to process low-quality visual images, however, the direct acquisition of high-quality images through optimized illumination is more direct and effective, and the cost is much lower than that of subsequent complicated image processing.
Because of the simultaneous requirement of high color rendering index at different color temperatures, the simulation is quite complicated and difficult, and therefore, the research reports are few. Although the existing lighting technology is researched a lot, no satisfactory research result exists in the process of comprehensively researching the spectral characteristics of the light source and the quality judgment technology of the collected image, and finally establishing and optimizing the light source spectrum visual detection lighting model.
Disclosure of Invention
In order to solve the problems, the invention provides a light source spectrum optimization method based on a CAM02-UCS color appearance model, which is applied to a light source spectrum optimization system based on a CAM02-UCS color appearance model;
the light source spectrum optimization system based on the CAM02-UCS color appearance model comprises: a light source box for generating a light source; the hyperspectral camera is used for acquiring the surface spectral reflectivity of the object; the CCD camera is used for acquiring an object image;
the light source spectrum optimization method based on the CAM02-UCS color appearance model specifically comprises the following steps:
s101: the light source box emits any visible light source to irradiate a target object in the light source box; detecting the spectral reflectivity of the surface of the target object at the moment by using a high spectral camera, and acquiring an object image by using a CCD (charge coupled device) camera;
s102: calculating to obtain a pixel point set G of the object image through an imaging modelC(x, y); the calculation formula of the imaging model is shown as formula (1):
Gc(x,y)＝∫Rc(λ)S(x,y,λ)C(λ)dλ(C＝{R,G,B},(x,y)∈RIO) (1)
in the above formula, R (λ) represents the spectral reflectance of the surface of the target object; the range of lambda is 4000nm,700nm](ii) a S (x, y, λ) represents the spectral power distribution of the visible light source emitted by the light source box; c (lambda) represents a spectrum sensitive function of the CCD camera, is a known fixed parameter of the CCD camera, and (x, y) is a coordinate of a certain pixel point on the object image; wherein, the pixel point set GC(x, y) including pixel points of a region where a target object is located and pixel points of a region where a non-target object is located on the object image;
s103: carrying out significance detection on the object image by adopting a significance detection method based on contrast to obtain the pixel point set GC(x, y) a target pixel point set consisting of all pixel points of the region where the target object is located;
s104: mapping all pixel points in the target pixel point set obtained by significance detection to a CAM02-UCS color appearance model to obtain a first three-dimensional coordinate point set corresponding to all pixel points in the target pixel point set in a uniform color space of the CAM02-UCS color appearance model;
for a first three-dimensional coordinate point (J) of the first set of three-dimensional coordinate points2,aM2,bM2) Defining a second three-dimensional coordinate point having a maximum color difference value with the first three-dimensional coordinate point as (J)1,aM1,bM1) Establishing a color difference value optimization objective function delta E shown in a formula (2);
optimizing the objective function delta E by adopting a genetic algorithm to obtain a coordinate value (J) of a second three-dimensional coordinate point with the maximum color difference value with the first three-dimensional coordinate point1,aM1,bM1) (ii) a Then, calculating to obtain second three-dimensional coordinate points corresponding to all the first three-dimensional coordinate points in the first three-dimensional coordinate point set by adopting the method, and obtaining a second three-dimensional coordinate point set consisting of all the second three-dimensional coordinate points;
s105: reducing each second three-dimensional coordinate point in the second three-dimensional coordinate point set to a corresponding pixel point (x1, y1) so as to form an optimized pixel point set GC(x1, y 1); g is to beC(x1, y1) is substituted into the formula (1), and the values of the spectral reflectance R (λ) and the spectral sensitivity function C (λ) in the step S101 are kept unchanged, so that the spectral power distribution corresponding to the pixel point set with the maximum primary pixel point color difference value of the object image, that is, the optimized spectral power distribution S (x1, y1, λ), is calculated;
s106: and the light source box emits a corresponding light source according to the optimized spectral power to irradiate on an object, so that the color of an object image acquired by the CCD camera is more vivid and the visual effect is better.
Further, the light source box comprises a plurality of light sources, and the light sources can be mixed to emit light sources with any spectral power distribution.
Further, the plurality of light sources are all LED light sources.
Further, in step S103, when the saliency of the object image is detected by using a saliency detection method (HC) based on contrast, a calculation formula of a saliency value of each pixel point is shown in formula (3):
in the above formula, any one pixel point IkSignificant value of S (I)k) The global contrast of the pixel point on the whole object image, namely the pixel point IkAnd all other pixel points IiThe sum of the distances in color; i isiHas a value range of [0,255 ]]；D(Ik,Ii) Is as I in CIELAB color spacekAnd IiThe distance of (d); converting equation (3) to a more intuitive color value representation equation, such as equation (4):
in the above formula, ci is the pixel IkIs a known quantity; n is the total number of different pixel color values in the object image, fjIs the frequency of occurrence of the pixel color value cj.
Further, in step S104, the brightness J' and chromaticity coordinate a in the uniform color space established by the CAM02-UCS color appearance modelM、bMThe calculation formula (2) is shown in formula (5):
in the above formula, the first and second carbon atoms are,
J. m and h are lightness, apparent chroma and hue angle of the color appearance model respectively.
The technical scheme provided by the invention has the beneficial effects that: the technical scheme provided by the invention combines an LED light source spectrum model, an optimization algorithm and a visual description method of a color gamut model, changes two important visual color factors of chroma and hue by optimizing the light source spectrum, and can visually see the optimization effect of the light source.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flowchart of a light source spectrum optimization method based on a CAM02-UCS color appearance model according to an embodiment of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
The implementation of the invention provides a light source spectrum optimization method based on a CAM02-UCS color appearance model, which is applied to a light source spectrum optimization system based on a CAM02-UCS color appearance model; the light source spectrum optimization system based on the CAM02-UCS color appearance model comprises: a light source box for generating a light source; the hyperspectral camera is used for acquiring the surface spectral reflectivity of the object; the CCD camera is used for acquiring an object image;
referring to fig. 1, fig. 1 is a flowchart of a light source spectrum optimization method based on a CAM02-UCS color appearance model in an embodiment of the present invention, which specifically includes the following steps:
s101: the light source box emits any visible light source to irradiate a target object in the light source box; detecting the spectral reflectivity of the surface of the target object at the moment by using a high spectral camera, and acquiring an object image by using a CCD (charge coupled device) camera;
s102: calculating to obtain a pixel point set G of the object image through an imaging modelC(x, y); the calculation formula of the imaging model is shown as formula (1):
Gc(x,y)＝∫Rc(λ)S(x,y,λ)C(λ)dλ(C＝{R,G,B},(x,y)∈RIO) (1)
in the above formula, R (λ) represents the spectral reflectance of the surface of the target object; the range of lambda is 4000nm,700nm](ii) a S (x, y, λ) represents the spectral power distribution of the visible light source emitted by the light source box; c (lambda) represents a spectrum sensitive function of the CCD camera, is a known fixed parameter of the CCD camera, and (x, y) is a coordinate of a certain pixel point on the object image; wherein, the pixel point set GC(x, y) including pixel points of a region where a target object is located and pixel points of a region where a non-target object is located on the object image;
s103: carrying out significance detection on the object image by adopting a significance detection method (HC) based on contrast to obtain the pixel point set GCAll of the areas in (x, y) where the target object is locatedA target pixel point set consisting of pixel points;
s104: mapping all pixel points in the target pixel point set obtained by significance detection to a CAM02-UCS color appearance model to obtain a first three-dimensional coordinate point set corresponding to all pixel points in the target pixel point set in a uniform color space of the CAM02-UCS color appearance model;
for a first three-dimensional coordinate point (J) of the first set of three-dimensional coordinate points2,aM2,bM2) Defining a second three-dimensional coordinate point having a maximum color difference value with the first three-dimensional coordinate point as (J)1,aM1,bM1) Establishing a color difference value optimization objective function delta E shown in a formula (2);
optimizing the objective function delta E by adopting a genetic algorithm to obtain a coordinate value (J) of a second three-dimensional coordinate point with the maximum color difference value with the first three-dimensional coordinate point1,aM1,bM1) (ii) a Then, calculating to obtain second three-dimensional coordinate points corresponding to all the first three-dimensional coordinate points in the first three-dimensional coordinate point set by adopting the method, and obtaining a second three-dimensional coordinate point set consisting of all the second three-dimensional coordinate points;
s105: reducing each second three-dimensional coordinate point in the second three-dimensional coordinate point set to a corresponding pixel point (x1, y1) so as to form an optimized pixel point set GC(x1, y 1); g is to beC(x1, y1) is substituted into the formula (1), and the values of the spectral reflectance R (λ) and the spectral sensitivity function C (λ) in the step S101 are kept unchanged, so that the spectral power distribution corresponding to the pixel point set with the maximum primary pixel point color difference value of the object image, that is, the optimized spectral power distribution S (x1, y1, λ), is calculated;
s106: and the light source box emits a corresponding light source according to the optimized spectral power to irradiate on an object, so that the color of an object image acquired by the CCD camera is more vivid and the visual effect is better.
In the embodiment of the present invention, the visible light source emitted from the light source box in step S101 is a D65 sun light source.
The light source box comprises a plurality of light sources, and can mix the light sources to emit light sources with any spectral power distribution.
The plurality of light sources are all LED light sources.
In step S103, when the saliency of the object image is detected by using the saliency detection method (HC) based on contrast, a calculation formula of a saliency value of each pixel point is shown in formula (3):
in the above formula, any one pixel point IkSignificant value of S (I)k) The global contrast of the pixel point on the whole object image, namely the pixel point IkAnd all other pixel points IiThe sum of the distances in color; i isiHas a value range of [0,255 ]]；D(Ik,Ii) Is as I in CIELAB color spacekAnd IiThe distance of (d); converting equation (3) to a more intuitive color value representation equation, such as equation (4):
in the above formula, ci is the pixel IkIs a known quantity; n is the total number of different pixel color values in the object image, fjIs the frequency of occurrence of the pixel color value cj.
In step S104, the brightness J' and chromaticity coordinate a in the uniform color space established by the CAM02-UCS color appearance modelM、bMThe calculation formula (2) is shown in formula (5):
in the above formula, the first and second carbon atoms are,
J. m and h are lightness, apparent chroma and hue angle of the color appearance model respectively.
The invention has the beneficial effects that: the technical scheme provided by the invention combines an LED light source spectrum model, an optimization algorithm and a visual description method of a color gamut model, changes two important visual color factors of chroma and hue by optimizing the light source spectrum, and can visually see the optimization effect of the light source.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A light source spectrum optimization method based on a CAM02-UCS color appearance model is applied to a light source spectrum optimization system based on a CAM02-UCS color appearance model; the method is characterized in that: the light source spectrum optimization system based on the CAM02-UCS color appearance model comprises: a light source box for generating a light source; the hyperspectral camera is used for acquiring the surface spectral reflectivity of the object; the CCD camera is used for acquiring an object image;
the light source spectrum optimization method based on the CAM02-UCS color appearance model specifically comprises the following steps:
s101: the light source box emits any visible light source to irradiate a target object in the light source box; detecting the spectral reflectivity of the surface of the target object at the moment by using a high spectral camera, and acquiring an object image by using a CCD (charge coupled device) camera;
s102: calculating to obtain a pixel point set G of the object image through an imaging modelC(x, y); the calculation formula of the imaging model is shown as formula (1):
Gc(x,y)＝∫Rc(λ)S(x,y,λ)C(λ)dλ(C＝{R,G,B},(x,y)∈RIO) (1)
in the above formula, R (λ) represents the spectral reflectance of the surface of the target object; the range of lambda is 4000nm,700nm](ii) a S (x, y, λ) represents the spectral power distribution of the visible light source emitted by the light source box; c (lambda) represents the spectral sensitivity function of a CCD camera and is knownThe (x, y) is the coordinate of a certain pixel point on the object image; wherein, the pixel point set GC(x, y) including pixel points of a region where a target object is located and pixel points of a region where a non-target object is located on the object image;
s103: carrying out significance detection on the object image by adopting a significance detection method based on contrast to obtain the pixel point set GC(x, y) a target pixel point set consisting of all pixel points of the region where the target object is located;
s104: mapping all pixel points in the target pixel point set obtained by significance detection to a CAM02-UCS color appearance model to obtain a first three-dimensional coordinate point set corresponding to all pixel points in the target pixel point set in a uniform color space of the CAM02-UCS color appearance model;
for a first three-dimensional coordinate point (J) of the first set of three-dimensional coordinate points2,aM2,bM2) Defining a second three-dimensional coordinate point having a maximum color difference value with the first three-dimensional coordinate point as (J)1,aM1,bM1) Establishing a color difference value optimization objective function delta E shown in a formula (2);
optimizing the objective function delta E by adopting a genetic algorithm to obtain a coordinate value (J) of a second three-dimensional coordinate point with the maximum color difference value with the first three-dimensional coordinate point1,aM1,bM1) (ii) a Then, calculating to obtain second three-dimensional coordinate points corresponding to all the first three-dimensional coordinate points in the first three-dimensional coordinate point set by adopting the method, and obtaining a second three-dimensional coordinate point set consisting of all the second three-dimensional coordinate points;
s105: reducing each second three-dimensional coordinate point in the second three-dimensional coordinate point set to a corresponding pixel point (x1, y1) so as to form an optimized pixel point set GC(x1, y 1); g is to beC(x1, y1) is substituted into the formula (1) and the values of the spectral reflectance R (λ) and the spectral sensitivity function C (λ) in step S101 are kept unchanged, thereby calculating the ANDingThe spectral power distribution corresponding to the pixel point set with the maximum original pixel point color difference value of the object image is the optimized spectral power distribution S (x1, y1, lambda);
s106: and the light source box emits a corresponding light source according to the optimized spectral power to irradiate on an object, so that the color of an object image acquired by the CCD camera is more vivid and the visual effect is better.
2. The light source spectrum optimization method based on the CAM02-UCS color appearance model as claimed in claim 1, wherein: the light source box comprises a plurality of light sources, and can mix the light sources to emit light sources with any spectral power distribution.
3. The light source spectrum optimization method based on the CAM02-UCS color appearance model as claimed in claim 2, wherein: the plurality of light sources are all LED light sources.
4. The light source spectrum optimization method based on the CAM02-UCS color appearance model as claimed in claim 1, wherein: in step S103, when the saliency of the object image is detected by using the saliency detection method (HC) based on contrast, a calculation formula of a saliency value of each pixel point is shown in formula (3):
in the above formula, any one pixel point IkSignificant value of S (I)k) The global contrast of the pixel point on the whole object image, namely the pixel point IkAnd all other pixel points IiThe sum of the distances in color; i isiHas a value range of [0,255 ]]；D(Ik,Ii) Is as I in CIELAB color spacekAnd IiThe distance of (d); converting equation (3) to a more intuitive color value representation equation, such as equation (4):
in the above formula, ci is the pixel IkIs a known quantity; n is the total number of different pixel color values in the object image, fjIs the frequency of occurrence of the pixel color value cj.
5. The light source spectrum optimization method based on the CAM02-UCS color appearance model as claimed in claim 1, wherein: in step S104, the brightness J' and chromaticity coordinate a in the uniform color space established by the CAM02-UCS color appearance modelM、bMThe calculation formula (2) is shown in formula (5):
in the above formula, the first and second carbon atoms are,
J. m and h are lightness, apparent chroma and hue angle of the color appearance model respectively.
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