CN112161708A - Color measurement platform for calculating color tristimulus values and spectrum preprocessing method - Google Patents
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Abstract
The invention provides a color measurement platform for calculating a color tristimulus value and a spectrum preprocessing method, wherein the color measurement platform comprises: the method comprises the steps of placing a reference standard on a reflection measurement support, collecting reference standard spectral response data by using an optical fiber spectrometer, replacing the reference standard with a sample to be measured, collecting spectral response data of the sample to be measured by using the optical fiber spectrometer, placing a visible light cut-off baffle plate between the reflection measurement support and the optical fiber spectrometer, collecting dark noise spectral response data by using the optical fiber spectrometer, removing spectral abnormal values and fluctuation values from the reference standard, the sample to be measured and the spectral response data of dark noise, performing preprocessing such as spectral smoothing and spectral interpolation, and obtaining spectral data which can be used for calculating spectral reflectivity and color tristimulus values. The invention can eliminate spectrum abnormal peak value and high-frequency random noise, is suitable for preprocessing spectrum data without obvious characteristic peak value and with more gentle curve change trend, and can improve the accuracy and repeatability of the optical fiber spectrometer for measuring color.
Description
Technical Field
The invention relates to the technical field of spectrum preprocessing, in particular to a color measurement platform for calculating a color tristimulus value and a spectrum preprocessing method.
Background
The color is an important index for evaluating the product quality in the industries of printing, textile, food, automobile manufacturing and the like. According to the national standard GB/T3977-2008 color representation method, object colors are represented by tristimulus values CIE XYZ. According to the national standard GB/T7921-2008 'uniform color space and color difference formula', chromaticity parameters of uniform color space CIELab values, color difference values and the like which accord with the visual characteristics of human eyes for evaluating the color quality of products are calculated based on tristimulus values CIEXYZ. The tristimulus values are therefore the basis for the calculation of colorimetric parameters and the quantitative evaluation of color. Compared with the method of using a three-primary-color filter imitating a CIE color matching function and a visible light detector for color measurement, the method has the advantages that more original spectral information and color information can be obtained by using the fiber spectrometer for color measurement, and the requirements of color measurement and color quality detection in different occasions, such as online color detection and control in a factory, can be met.
According to the national standard GB/T3979-2008 < measuring method of object color >, the method for measuring the tristimulus value of color by the fiber spectrometer is as follows: for the reflective samples, first, under the reflection measurement geometry specified by CIE, the spectral response data V of the samples in the visible light range is collected by using a fiber optic spectrometers(λ), spectral response data V of reference standardr(λ) and spectral response data V of dark noised(λ); calculating the spectral reflectivity rho (lambda) of the object by adopting the formula (9); finally, the spectral reflectance ρ (λ), the spectral power distribution S (λ) relative to the CIE-specified standard illuminant, and standard chromaticity observer data
Multiplying and summing to obtain the color tristimulus value, which is shown in a formula (10). The measurement and calculation methods for transmission samples are similar to those for reflection samples, exceptThe spectral transmittance τ (λ) is calculated according to the transmission spectral response data of the sample, with reference to the transmission spectral response data of the standard and the transmission spectral response data of the dark noise, and then the tristimulus value is calculated by using equation (10).
According to the method for calculating the spectral reflectivity and the color tristimulus values, the spectral response data collected by the fiber spectrometer is used for calculating the reflection or transmission spectral characteristics, the tristimulus values CIEXYZ and CIEL of the object*a*b*And basic data of chromaticity parameters such as chromatic aberration. However, the spectral response data collected by the fiber optic spectrometer contains various noise data in addition to color information, due to factors such as detectors, electronics, dark current, and external environmental interference. Therefore, in order to ensure the accuracy and repeatability of the color measurement data, spectrum preprocessing such as removing abnormal values, denoising and smoothing is required to be performed on the original spectrum response data measured by the fiber spectrometer, and noise data in the spectrum response data is suppressed or eliminated.
The existing spectrum pretreatment methods are mainly divided into the following categories: (1) the method for eliminating the spectrum abnormal value comprises the following steps: during the measurement process, the spectral response data may be mixed with abnormal values under the influence of changes of factors such as the performance of the measuring instrument, the measuring method or the measuring environment, namely, part of the data is obviously inconsistent with other data. Abnormal spectral response data directly affects spectral calculations and analysis results and can even lead to erroneous conclusions. Therefore, abnormal data needs to be eliminated through a mathematical statistic method. The existing commonly used method for eliminating the abnormal value of the spectral response data is to utilize indexes such as mahalanobis distance, Cook distance, the abnormal value of spectral feature, spectral residual ratio, chemical value absolute error and the like, and then combine a mathematical statistic test method, such as a triple standard deviation method, a ratio method or a truncation point method, to judge the abnormal value of the spectral response data. (2) The spectrum denoising method comprises the following steps: the purpose of spectrum denoising is to eliminate system errors, especially measurement data changes caused by sample uneven distribution, surface scattering changes, optical path changes and the like. The commonly used correction methods at present include standard normal variable correction method, multivariate scattering correction method, orthogonal signal correction and the like. (3) The spectrum smoothing method comprises the following steps: the purpose of the spectral response data smoothing is to eliminate random noise, especially high frequency noise, in the spectral response data. Common spectral smoothing methods are moving average filters, polynomial smoothing filters, median filters, and the like.
Disclosure of Invention
The invention provides a color measurement platform for calculating a color tristimulus value and a spectrum preprocessing method, and aims to solve the problems that the traditional spectrum preprocessing method is not suitable for processing spectrum response data which has no obvious characteristic peak value, has a relatively gentle curve change trend and is used for calculating the color tristimulus value.
In order to achieve the above object, an embodiment of the present invention provides a color measurement platform for color tristimulus value calculation, including:
a reflectance measurement mount;
a sample to be measured, wherein the sample to be measured is placed at the top of the reflection measurement bracket;
the first collimating mirror is arranged right above the sample to be detected;
the second collimating lens is obliquely arranged above the side of the sample to be detected, and the included angle between the second collimating lens and the first collimating lens is 45 degrees;
the optical fiber light source is connected with the second collimating mirror through an incident optical fiber;
the first end of the fiber spectrometer is connected with the first collimating mirror through an emergent optical fiber;
a computer electrically connected to the second end of the fiber optic spectrometer.
Embodiments of the present invention also provide a spectrum preprocessing method for color tristimulus value calculation, including:
step 1, placing a reference standard on a reflection measurement support, adopting a fiber optic spectrometer to collect spectral response data of the reference standard, replacing the reference standard with a sample to be measured, adopting the fiber optic spectrometer to collect spectral response data of the sample to be measured, placing a visible light cut-off baffle plate between the reflection measurement support and the fiber optic spectrometer, and adopting the fiber optic spectrometer to collect spectral response data of dark noise to obtain reference standard spectral response data, spectral response data of the sample to be measured and spectral response data of the dark noise;
and step 10, calculating the color tristimulus value of the sample to be detected according to the calculated spectral reflectivity of the sample to be detected.
Wherein, the step 1 specifically comprises:
respectively aligning reference standard, sample to be measured and dark noise by using fiber optic spectrometerCollecting spectral response data for not less than 6 times to obtain reference standard spectral response data Vr(lambda) spectral response data V of the sample to be measureds(λ) and spectral response data V of dark noised(lambda), the integration time of the fiber spectrometer is the integration time when the reference standard spectral response data reaches the maximum, and the spectral sampling wavelength interval is less than one tenth of the wavelength interval of the color tristimulus value calculation.
Wherein, the step 2 specifically comprises:
calculating the standard deviation s of the reference standard spectral response data at each wavelength sampling pointr(λ), as follows:
wherein s isr(λ) represents the standard deviation of the reference standard spectral response data, n represents the number of measurements, i represents the ith measurement, λ represents the wavelength, Vr-i(λ) represents the reference standard spectral response data for the ith measurement,
means representing reference standard spectral response data;
searching for standard deviation s of reference standard spectral response data within the color tristimulus value calculation wavelength intervalr(lambda) maximum wavelength point lambdamax_rAnd eliminating abnormal wavelength point lambdamax_rReference standard spectral response data of (a);
calculating the standard deviation s of the spectral response data of the sample to be measured at each wavelength sampling points(λ), as follows:
wherein s iss(lambda) represents the standard deviation of the spectral response data of the sample to be measured, n represents the number of measurements, i represents the ith measurement, lambda represents the wavelength, Vs-i(λ) represents the i-th measurement to be takenMeasuring the spectral response data of the sample,
the average value of the spectral response data of the sample to be measured is represented;
searching the standard deviation s of the spectral response data of the sample to be detected in the wavelength interval of the color tristimulus valuess(lambda) maximum wavelength point lambdamax_sAnd eliminating abnormal wavelength point lambdamax_sSpectral response data of the sample to be detected;
calculating the standard deviation s of the spectral response data of the dark noise at each wavelength sampling pointd(λ), as follows:
wherein s isd(λ) represents the standard deviation of the spectral response data of dark noise, n represents the number of measurements, i represents the ith measurement, λ represents the wavelength, Vd-i(lambda) represents the spectral response data of the dark noise measured at the ith time,
an average of spectral response data representing dark noise;
finding the standard deviation s of the spectral response data of dark noise within the color tristimulus value calculation wavelength intervald(lambda) maximum wavelength point lambdamax_dAnd eliminating abnormal wavelength point lambdamax_dThe spectral response data of the dark noise.
Wherein, the step 3 specifically comprises:
respectively calculating the average value of the reference standard spectral response data after eliminating the abnormal values
Average value of spectral response data of sample to be detected after eliminating abnormal value
And light of dark noise after removing abnormal valueMean of spectral response data
Wherein the step 4, the step 5 and the step 6 specifically include:
calculating the average value of the reference standard spectral response data after eliminating the abnormal value
First derivative d ofr(λ), as follows:
wherein d isr(λj) A first derivative representing the average of the reference standard spectral response data after outliers are removed,
denotes λjThe wavelength points are referenced to the average of the standard spectral response data,
denotes λj-1The wavelength points are referenced to the mean of the standard spectral response data, λ denotes wavelength, λjDenotes the jth wavelength point, λj-1Represents the j-1 wavelength point;
within the wavelength interval of color tristimulus values, the average value of the reference standard spectral response data after the abnormal values are removed
First derivative d ofrTwo wavelength points λ corresponding to the maximum value in (λ)kAnd λk-1The spectral response data are removed to obtain the average value of the reference standard spectral response data after removal
Calculating the average value of the spectral response data of the sample to be detected after eliminating the abnormal value
First derivative d ofs(λ), as follows:
wherein d iss(λj) The first derivative of the average value of the spectral response data of the sample to be detected after the abnormal value is eliminated is shown,
denotes λjThe average value of the spectral response data of the sample to be detected after the abnormal value is removed from the wavelength points,
denotes λj-1The average value of the spectral response data of the sample to be measured after the abnormal value of the wavelength point is removed, wherein lambda represents the wavelength, and lambda represents the wavelengthjDenotes the jth wavelength point, λj-1Represents the j-1 wavelength point;
within the wavelength interval of the color tristimulus values, the average value of the spectral response data of the sample to be measured after the abnormal values are removed
First derivative d ofsTwo wavelength points λ corresponding to the maximum value in (λ)kAnd λk-1The spectral response data are removed to obtain the average value of the spectral response data of the samples to be detected after removal
Calculating the average value of the spectral response data of the dark noise after eliminating the abnormal value
First derivative d ofd(λ), as follows:
wherein d isd(λj) The first derivative of the average of the spectral response data representing dark noise after outliers are removed,
denotes λjThe average value of the spectral response data of the dark noise after the abnormal value is removed from the wavelength points,
denotes λj-1Average value of spectral response data of dark noise with abnormal values removed from wavelength points, wherein lambda represents wavelength and lambda represents noisejDenotes the jth wavelength point, λj-1Represents the j-1 wavelength point;
within the wavelength interval of color tristimulus value calculation, the average value of the spectral response data of the dark noise after the abnormal value is removed
First derivative d ofdTwo wavelength points λ corresponding to the maximum value in (λ)kAnd λk-1The spectral response data is removed to obtain the average value of the spectral response data of the removed dark noise
Wherein, the step 7 specifically comprises:
averaging the rejected reference standard spectral response data by adopting a spectral smoothing method
Average value of spectral response data of rejected samples to be detected
And the spectral response number of the rejected dark noiseAccording to the average value
De-noising to obtain the average value of the de-noised reference standard spectral response data
Average value of denoised spectral response data of sample to be detected
And average of spectral response data of denoised dark noise
Wherein, the step 8 specifically comprises:
in the visible light range, the wavelength interval is calculated by using the color tristimulus values as an interpolation interval, and the average value of the denoised reference standard spectral response data is calculated by adopting an interpolation algorithm
Average value of denoised spectral response data of sample to be detected
And average of spectral response data of denoised dark noise
Converting to obtain the average value of the converted reference standard spectral response data in the same wavelength range and the same wavelength interval
Average value of converted spectral response data of sample to be detected
And average of the converted spectral response data of dark noise
Wherein, the step 9 specifically comprises:
based on the obtained average value of the converted reference standard spectral response data
Average value of converted spectral response data of sample to be detected
And average of the converted spectral response data of dark noise
Calculating the spectral reflectivity rho of the sample to be measureds(λ), as follows:
where ρ iss(lambda) represents the spectral reflectance, rho, of the sample to be measuredr(λ) denotes the spectral reflectance of the reference standard, λ denotes the wavelength,
represents the average of the converted reference standard spectral response data,
represents the average value of the converted spectral response data of the sample to be measured,
an average of the spectral response data representing the converted dark noise.
Wherein, the step 10 specifically includes:
according to the obtained spectral reflectivity rho of the sample to be measureds(lambda), calculating the color tristimulus value of the sample to be measured as follows:
where ρ iss(lambda) represents the spectral reflectance of the sample to be measured, S (lambda) represents the CIE specified relative spectral power distribution of the standard illuminant,
for the standard chromaticity observer data specified by CIE, Δ λ represents the wavelength interval, k represents the normalization coefficient, and λ represents the wavelength.
The scheme of the invention has the following beneficial effects:
the color measurement platform for calculating the color tristimulus values and the spectrum preprocessing method can eliminate the spectrum abnormal peak value and the high-frequency random noise, can eliminate the spectrum abnormal peak value, are suitable for preprocessing the spectrum data without obvious characteristic peak values and with relatively gentle curve change trends, and can improve the accuracy and the repeatability of the optical fiber spectrometer for measuring the color.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of a color measuring platform according to the present invention;
FIG. 3 is a graph of raw spectral response data for a reference standard, a sample to be tested, and dark noise in accordance with the present invention;
FIG. 4 is a standard deviation plot of reference standard spectral response data according to the present invention;
FIG. 5 is a schematic of the first derivative of the reference standard spectral response data of the present invention;
FIG. 6 is a diagram showing the spectral response data of the reference standard, the sample to be tested and the dark noise after the spectral preprocessing according to the present invention.
[ description of reference ]
1-a reflection measurement mount; 2-a sample to be tested; 3-a first collimating mirror; 4-a second collimating mirror; 5-a fiber optic light source; 6-fiber optic spectrometer; 7-a computer; 8-an incident optical fiber; 9-exit fiber.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a color measurement platform for color tristimulus value calculation and a spectrum preprocessing method, aiming at the problems that the existing spectrum preprocessing method is not suitable for processing spectrum response data which has no obvious characteristic peak value, has relatively gentle curve variation trend and is used for color tristimulus value calculation.
As shown in fig. 1 to 6, an embodiment of the present invention provides a color measurement platform for color tristimulus value calculation, including: a reflection measurement mount 1; a sample 2 to be measured, wherein the sample 2 to be measured is placed at the top of the reflection measurement bracket 1; the first collimating mirror 3 is arranged right above the sample 2 to be detected; the second collimating lens 4 is obliquely arranged above the side of the sample 2 to be detected, and an included angle between the second collimating lens 4 and the first collimating lens 3 is 45 degrees; the optical fiber light source is connected with the second collimating mirror 4 through an incident optical fiber 8; the first end of the fiber spectrometer 6 is connected with the first collimating mirror 3 through an emergent optical fiber 9; a computer 7, wherein the computer 7 is electrically connected with the second end of the fiber spectrometer 6.
According to the color measurement platform and the spectrum preprocessing method for calculating the color tristimulus value, the optical fiber light source 5 adopts an optical fiber halogen lamp, and the color measurement platform for calculating the color tristimulus value is built by adopting a 45-degree/0-degree reflection measurement support 1, a visible light station optical fiber spectrometer 6 and the computer 7 with spectrum acquisition and analysis software.
Embodiments of the present invention also provide a spectrum preprocessing method for color tristimulus value calculation, including: step 1, placing a reference standard on a reflection measurement bracket 1, adopting a fiber spectrometer 6 to collect spectral response data of the reference standard, replacing the reference standard with a sample 2 to be measured, and adopting the fiber spectrometer 6 to perform spectrum on the sample 2 to be measuredResponse data acquisition, namely placing a visible light cut-off baffle between the reflection measurement bracket 1 and the optical fiber spectrometer 6, and performing spectral response data acquisition on dark noise by using the optical fiber spectrometer 6 to obtain reference standard spectral response data, spectral response data of a sample 2 to be measured and spectral response data of the dark noise; step 2, respectively calculating the standard deviation of reference standard spectral response data, the standard deviation of the spectral response data of the sample 2 to be detected and the standard deviation of the spectral response data of dark noise at each wavelength sampling point, respectively setting an abnormal value rejection criterion and a method according to the standard deviation of the reference standard spectral response data, the standard deviation of the spectral response data of the sample 2 to be detected and the standard deviation of the spectral response data of the dark noise in the color tristimulus value calculation wavelength interval, and rejecting the standard deviation of the reference standard spectral response data, the standard deviation of the spectral response data of the sample 2 to be detected and the abnormal value of the standard deviation of the spectral response data of the dark noise corresponding to the reference standard spectral response data, the spectral response data of the sample 2 to be detected and the spectral response data of the dark noise; step 3, respectively calculating the average value of the reference standard spectral response data after the abnormal values are removed, the average value of the spectral response data of the sample 2 to be detected after the abnormal values are removed and the average value of the spectral response data of the dark noise after the abnormal values are removed; step 4, calculating a first derivative of the average value of the reference standard spectral response data after the abnormal values are removed, and removing two wavelength points lambda corresponding to the maximum value of the first derivative in the wavelength interval of the color tristimulus valueskAnd λk-1Obtaining the average value of the rejected reference standard spectral response data; step 5, calculating a first derivative of the average value of the spectral response data of the sample 2 to be detected after the abnormal value is removed, and removing two wavelength points lambda corresponding to the maximum value of the first derivative in the wavelength interval of the color tristimulus value calculationkAnd λk-1Obtaining the average value of the spectral response data of the rejected sample 2 to be detected; step 6, calculating a first derivative of the average value of the spectral response data of the dark noise after the abnormal value is removed, and removing two wavelength points lambda corresponding to the maximum value of the first derivative in the wavelength interval of the color tristimulus value calculationkAnd λk-1The spectral response data of (a) a,obtaining the average value of the spectral response data of the rejected dark noise; step 7, denoising the average value of the rejected reference standard spectral response data, the average value of the rejected sample 2 to be detected spectral response data and the average value of the rejected dark noise spectral response data by adopting a spectral smoothing method to obtain the average value of the denoised reference standard spectral response data, the average value of the denoised sample 2 to be detected spectral response data and the average value of the denoised dark noise spectral response data; step 8, in the visible light range, calculating a wavelength interval by using a color tristimulus value as an interpolation interval, and converting the average value of the denoised reference standard spectral response data, the average value of the denoised sample 2 to be detected spectral response data and the average value of the denoised dark noise spectral response data by adopting an interpolation algorithm to obtain the average value of the converted reference standard spectral response data, the average value of the converted sample 2 to be detected spectral response data and the average value of the converted dark noise spectral response data; step 9, calculating the spectral reflectivity of the sample 2 to be detected according to the average value of the converted reference standard spectral response data, the average value of the converted spectral response data of the sample 2 to be detected and the average value of the converted spectral response data of the dark noise; and step 10, calculating the color tristimulus value of the sample 2 to be detected according to the calculated spectral reflectivity of the sample 2 to be detected.
Wherein, the step 1 specifically comprises: respectively carrying out spectral response data acquisition on the reference standard, the sample 2 to be detected and the dark noise by adopting the optical fiber spectrometer 6, wherein the acquisition times are not less than 6 times to obtain reference standard spectral response data Vr(lambda) spectral response data V of sample 2 to be measureds(λ) and spectral response data V of dark noised(λ), the integration time of the fiber spectrometer 6 is the integration time for which the reference standard spectral response data reaches the maximum, and the spectral sampling wavelength interval is less than one tenth of the color tristimulus value calculation wavelength interval.
The color measurement platform and the spectrum preprocessing method for calculating the color tristimulus value according to the above embodiments of the present invention determine the measurement parameters: the sampling wavelength range of the optical fiber spectrometer 6 is 380nm-780nm, and the sampling is performedThe wavelength interval is 0.5nm, and the sampling frequency is 6 times; the reference standard adopts a BCRA standard white ceramic plate, the sample 2 to be measured adopts a BCRA standard red ceramic plate, the reference standard is placed on the reflection measurement support 1, the fiber spectrometer 6 is adopted to measure spectral response data of the reference standard, the integration time of the reference standard spectral response value reaching the maximum is determined to be 155 milliseconds, and 155 milliseconds are set as the integration time of the fiber spectrometer 6. Placing a reference standard on the reflection measurement bracket 1, measuring the reference standard for 6 times by taking 155 milliseconds as the integration time of the fiber spectrometer 6, and obtaining reference standard spectral response data Vr(λ) as shown in FIG. 3. Placing the sample 2 to be measured on the reflection measurement bracket 1, measuring for 6 times by taking 155 milliseconds as 6 integration time of the fiber spectrometer, and obtaining spectral response data V of the sample 2 to be measureds(λ) as shown in FIG. 3. A visible light cut-off baffle plate is arranged between the reflection measurement bracket 1 and the optical fiber spectrometer 6, the integration time of the optical fiber spectrometer 6 is 155 milliseconds, the measurement is carried out for 6 times, and dark noise spectral response data V are obtainedd(λ) as shown in FIG. 3.
Wherein, the step 2 specifically comprises: calculating the standard deviation s of the reference standard spectral response data at each wavelength sampling pointr(λ), as follows:
wherein s isr(λ) represents the standard deviation of the reference standard spectral response data, n represents the number of measurements, i represents the ith measurement, λ represents the wavelength, Vr-i(λ) represents the reference standard spectral response data for the ith measurement,
means representing reference standard spectral response data;
searching for standard deviation s of reference standard spectral response data within the color tristimulus value calculation wavelength intervalr(lambda) maximum wavelength point lambdamax_rAnd eliminating abnormal wavelength point lambdamax_rReference standard spectrum ofResponse data;
calculating the standard deviation s of the spectral response data of the sample 2 to be measured at each wavelength sampling points(λ), as follows:
wherein s iss(lambda) represents the standard deviation of the spectral response data of the sample 2 to be measured, n represents the number of measurements, i represents the ith measurement, lambda represents the wavelength, Vs-i(lambda) represents the spectral response data of the sample 2 to be measured of the ith measurement,
the average value of the spectral response data of the sample 2 to be measured;
searching the standard deviation s of the spectral response data of the sample 2 to be detected in the wavelength interval of the color tristimulus value calculations(lambda) maximum wavelength point lambdamax_sAnd eliminating abnormal wavelength point lambdamax_sSpectral response data of the sample 2 to be detected;
calculating the standard deviation s of the spectral response data of the dark noise at each wavelength sampling pointd(λ), as follows:
wherein s isd(λ) represents the standard deviation of the spectral response data of dark noise, n represents the number of measurements, i represents the ith measurement, λ represents the wavelength, Vd-i(lambda) represents the spectral response data of the dark noise measured at the ith time,
an average of spectral response data representing dark noise;
finding the standard deviation s of the spectral response data of dark noise within the color tristimulus value calculation wavelength intervald(lambda) maximum wavelength point lambdamax_dAnd eliminating abnormal wavelength point lambdamax_dIs generated by the dark noiseSpectral response data of sound.
The color measurement platform and the spectrum preprocessing method for calculating the color tristimulus value in the embodiment of the invention calculate the standard deviation s of the reference standard spectrum response datar(λ), as shown in FIG. 4; and sorting the standard deviations of all wavelength points from large to small in the wavelength interval of the color tristimulus values, and removing the sample spectral response data of the first five wavelength points.
TABLE Standard deviation of reference Standard spectral response data between 1600 nm and 610nm
For example, as shown in table 1, standard deviations sorted from large to small between wavelength intervals of 600nm to 610nm are calculated for color tristimulus values, according to the above abnormal value rejection criteria and methods, it is determined that the spectral response values of wavelength points 607nm, 607.5nm, 609nm, 609.5nm and 605nm are abnormal, and the spectral response values of the five wavelength points are rejected, and the data of other wavelength points are rejected by the same method; calculating the standard deviation s of the spectral response data of the sample 2 to be measureds(lambda), sorting the standard deviation of each wavelength point from large to small in the wavelength interval of the color tristimulus values, and eliminating abnormal values in the spectral response data of the sample 2 to be detected of the first five wavelength points; calculating the standard deviation s of the spectral response data of dark noised(lambda), sorting the standard deviation of each wavelength point from large to small in the wavelength interval of the color tristimulus values, and eliminating abnormal values in the spectral response data of the dark noise of the first five wavelength points.
Wherein, the step 3 specifically comprises: respectively calculating the average value of the reference standard spectral response data after eliminating the abnormal values
Average value of spectral response data of sample 2 to be detected after eliminating abnormal value
And eliminating anomaliesAverage of spectral response data of post-valued dark noise
Wherein the step 4, the step 5 and the step 6 specifically include: calculating the average value of the reference standard spectral response data after eliminating the abnormal value
First derivative d ofr(λ), as follows:
wherein d isr(λj) A first derivative representing the average of the reference standard spectral response data after outliers are removed,
denotes λjThe wavelength points are referenced to the average of the standard spectral response data,
denotes λj-1The wavelength points are referenced to the mean of the standard spectral response data, λ denotes wavelength, λjDenotes the jth wavelength point, λj-1Represents the j-1 wavelength point;
within the wavelength interval of color tristimulus values, the average value of the reference standard spectral response data after the abnormal values are removed
First derivative d ofrTwo wavelength points λ corresponding to the maximum value in (λ)kAnd λk-1The spectral response data are removed to obtain the average value of the reference standard spectral response data after removal
Difference is eliminated by calculationAverage value of spectral response data of sample 2 to be measured after constant value
First derivative d ofs(λ), as follows:
wherein d iss(λj) The first derivative of the average value of the spectral response data of the sample to be detected after the abnormal value is eliminated is shown,
denotes λjThe average value of the spectral response data of the sample to be detected after the abnormal value is removed from the wavelength points,
denotes λj-1The average value of the spectral response data of the sample to be measured after the abnormal value of the wavelength point is removed, wherein lambda represents the wavelength, and lambda represents the wavelengthjDenotes the jth wavelength point, λj-1Represents the j-1 wavelength point;
within the wavelength interval of the color tristimulus values, the average value of the spectral response data of the sample 2 to be measured after the abnormal values are removed
First derivative d ofsTwo wavelength points λ corresponding to the maximum value in (λ)kAnd λk-1The spectral response data are removed to obtain the average value of the spectral response data of the sample 2 to be detected after removal
Calculating the average value of the spectral response data of the dark noise after eliminating the abnormal value
First derivative d ofd(λ), as follows:
wherein d isd(λj) The first derivative of the average of the spectral response data representing dark noise after outliers are removed,
denotes λjThe average value of the spectral response data of the dark noise after the abnormal value is removed from the wavelength points,
denotes λj-1Average value of spectral response data of dark noise with abnormal values removed from wavelength points, wherein lambda represents wavelength and lambda represents noisejDenotes the jth wavelength point, λj-1Represents the j-1 wavelength point;
within the wavelength interval of color tristimulus value calculation, the average value of the spectral response data of the dark noise after the abnormal value is removed
First derivative d ofdTwo wavelength points λ corresponding to the maximum value in (λ)kAnd λk-1The spectral response data is removed to obtain the average value of the spectral response data of the removed dark noise
The color measurement platform and the spectrum preprocessing method for calculating the tristimulus color values in the embodiments of the invention calculate the average value of the reference standard spectrum response data after removing the abnormal values
And calculating the average value of the reference standard spectral response data after removing the abnormal value according to the formula (6)
First derivative d ofr(λ), as shown in FIG. 5; within the wavelength interval of color tristimulus values, the average value of the reference standard spectral response data after the abnormal values are removed
Two wavelength points lambda corresponding to the maximum value of the first derivative value ofkAnd λk-1The spectral response data are removed to obtain the average value of the reference standard spectral response data after removal
TABLE 2600 nm-610nm first derivative of reference Standard spectral response data
As shown in Table 2, the average value of the reference standard spectral response data after eliminating the abnormal values and sorted from large to small with the wavelength of 600nm to 610nm
The first derivative of the wavelength point 602.5nm is the maximum, so that the spectral response data of 602.5nm and 602nm corresponding to the maximum value of the first derivative are removed, and the data of other wavelength points are removed by the same method to remove the abnormality; calculating the average value of the spectral response data of the sample 2 to be detected after eliminating the abnormal value
And calculating the average value of the spectral response data of the sample 2 to be detected after eliminating the abnormal value according to the formula (4)
First derivative d ofs(λ), as shown in FIG. 5;within the wavelength interval of the color tristimulus values, the average value of the spectral response data of the sample 2 to be measured after the abnormal values are removed
Two wavelength points lambda corresponding to the maximum value of the first derivative value ofkAnd λk-1The spectral response data are removed to obtain the average value of the spectral response data of the sample 2 to be detected after removal
Calculating the average value of the spectral response data of the dark noise after eliminating the abnormal value
And calculating the average value of the spectral response data of the dark noise after removing the abnormal value according to the formula (4)
First derivative d ofd(λ), as shown in FIG. 5; within the wavelength interval of color tristimulus value calculation, the average value of the spectral response data of the dark noise after the abnormal value is removed
Two wavelength points lambda corresponding to the maximum value of the first derivative value ofkAnd λk-1The spectral response data are removed to obtain the average value of the spectral response data of the sample 2 to be detected after removal
Wherein, the step 7 specifically comprises: averaging the rejected reference standard spectral response data by adopting a spectral smoothing method
Average value of spectral response data of rejected sample 2 to be detected
Picking deviceAverage of spectral response data of the divided dark noise
De-noising to obtain the average value of the de-noised reference standard spectral response data
Average value of denoised spectral response data of sample 2 to be detected
And average of spectral response data of denoised dark noise
Wherein, the step 8 specifically comprises: in the visible light range, the wavelength interval is calculated by using the color tristimulus values as an interpolation interval, and the average value of the denoised reference standard spectral response data is calculated by adopting an interpolation algorithm
Average value of denoised spectral response data of sample 2 to be detected
And average of spectral response data of denoised dark noise
Converting to obtain the average value of the converted reference standard spectral response data in the same wavelength range and the same wavelength interval
Average value of converted spectral response data of sample 2 to be measured
And average of the converted spectral response data of dark noise
In the color measurement platform and the spectrum preprocessing method for calculating the tristimulus color values according to the embodiments of the present invention, the average value of the denoised reference standard spectrum response data is calculated by using an interpolation algorithm
Average value of denoised spectral response data of sample 2 to be detected
And average of spectral response data of denoised dark noise
Average value of converted reference standard spectral response data converted to a wavelength range of 380nm to 780nm at a wavelength interval of 5nm
Average value of converted spectral response data of sample 2 to be measured
And average of the converted spectral response data of dark noise
As shown in fig. 6.
Wherein, the step 9 specifically comprises: based on the obtained average value of the converted reference standard spectral response data
Average value of converted spectral response data of sample 2 to be measured
And average of the converted spectral response data of dark noise
Calculating the spectral reflectivity rho of the sample to be measureds(λ), as follows:
where ρ iss(lambda) represents the spectral reflectance, rho, of the sample to be measuredr(λ) denotes the spectral reflectance of the reference standard, λ denotes the wavelength,
represents the average of the converted reference standard spectral response data,
represents the average value of the converted spectral response data of the sample to be measured,
an average of the spectral response data representing the converted dark noise.
Wherein, the step 10 specifically includes: according to the obtained spectral reflectivity rho of the sample to be measureds(lambda), calculating the color tristimulus value of the sample to be measured as follows:
where ρ iss(lambda) represents the spectral reflectance of the sample to be measured, S (lambda) represents the CIE specified relative spectral power distribution of the standard illuminant,
for the standard chromaticity observer data specified by CIE, Δ λ represents the wavelength interval, k represents the normalization coefficient, and λ represents the wavelength.
The color measurement platform and the spectrum preprocessing method for calculating the color tristimulus value, which are provided by the embodiment of the invention, repeat the steps 2 to 10, repeat the measurement on the reference standard, the sample 2 to be measured and the dark noise for four times, and calculate the X value, the Y value and the Z value of the color tristimulus value;
TABLE 3 calculation of X, Y and Z value comparisons of spectral response data before and after pretreatment
As can be seen from table 3, the standard deviation of the X value, the Y value, and the Z value calculated by the preprocessed spectral response data is significantly lower than the standard deviation of the X value, the Y value, and the Z value calculated by the original spectral response data before preprocessing, which indicates that the color measurement platform and the spectral preprocessing method for calculating the color tristimulus value can effectively eliminate the noise influence in the measurement process, and improve the measurement accuracy and repeatability.
According to the color measurement platform and the spectrum preprocessing method for calculating the color tristimulus value, the color measurement platform for calculating the color tristimulus value is set up by adopting the optical fiber light source 5, the reflection measurement bracket 1, the optical fiber spectrometer 6 and the computer 7 with spectrum acquisition and analysis software according to the color measurement geometrical conditions specified by CIE; determining measurement parameters: the frequency of acquiring the spectral response data by the optical fiber spectrometer 6 is not less than 6, the optical fiber spectrometer 6 is adopted to measure reference standard spectral response data, the integration time of the reference standard spectral response data reaching the maximum is determined to be 155 milliseconds, 155 milliseconds are set as the integration time of the optical fiber spectrometer 6, the integration time of the optical fiber spectrometer 6 is the integration time of the reference standard spectral response data reaching the maximum, the spectral sampling wavelength range is 380nm-780nm, the spectral sampling wavelength interval is 0.5nm, and the spectral sampling wavelength interval is less than one tenth of the color tristimulus value calculation wavelength interval. And respectively measuring the reference standard, the sample 2 to be measured and the spectral response data of dark noise by adopting the color measuring platform for calculating the color tristimulus values. The standard deviation of spectral response data based on a single wavelength point is determined, the standard deviation of the spectral response data measured for multiple times at each wavelength point is calculated, and the wavelength point with the abnormal spectral response data is searched and removed according to the abnormal value criterion and method. And calculating the average value of the reference standard after the abnormal value is eliminated, the sample 2 to be detected and the spectral response data of the dark noise. Calculating a reference standard, a first derivative of the average value of the spectral response data of the sample 2 to be measured and the dark noise, eliminating the reference standard, the average value of the spectral response data corresponding to the maximum value in the first derivative of the average value of the spectral response data of the sample 2 to be measured and the dark noise, denoising the eliminated reference standard and the average value of the spectral response data of the sample 2 to be measured and the dark noise by adopting a spectral smoothing method, converting the denoised reference standard and the average value of the spectral response data of the sample 2 to be measured and the dark noise into the converted reference standard with the same wavelength range and the same wavelength interval and the average value of the spectral response data of the sample 2 to be measured and the dark noise by adopting an interpolation algorithm, and calculating the spectral reflectivity of the sample 2 to be measured according to the converted reference standard and the average value of the spectral response data of the sample 2 to be measured and the dark noise, and calculating the color tristimulus value of the sample 2 to be detected according to the spectral reflectivity of the sample 2 to be detected.
The color measurement platform and the spectrum preprocessing method for calculating the color tristimulus values in the embodiment of the invention improve the accuracy and the repeatability of the color measurement of the fiber spectrometer 6, can eliminate the spectrum abnormal peak value and the high-frequency random noise, and are suitable for preprocessing the spectrum data which has no obvious characteristic peak value and has a relatively gentle curve change trend.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A color measurement platform for color tristimulus value calculation, comprising:
a reflectance measurement mount;
a sample to be measured, wherein the sample to be measured is placed at the top of the reflection measurement bracket;
the first collimating mirror is arranged right above the sample to be detected;
the second collimating lens is obliquely arranged above the side of the sample to be detected, and the included angle between the second collimating lens and the first collimating lens is 45 degrees;
the optical fiber light source is connected with the second collimating mirror through an incident optical fiber;
the first end of the fiber spectrometer is connected with the first collimating mirror through an emergent optical fiber;
a computer electrically connected to the second end of the fiber optic spectrometer.
2. A spectral preprocessing method for color tristimulus value calculation applied to the color measurement platform for color tristimulus value calculation according to claim 1, comprising:
step 1, placing a reference standard on a reflection measurement support, adopting a fiber optic spectrometer to collect spectral response data of the reference standard, replacing the reference standard with a sample to be measured, adopting the fiber optic spectrometer to collect spectral response data of the sample to be measured, placing a visible light cut-off baffle plate between the reflection measurement support and the fiber optic spectrometer, and adopting the fiber optic spectrometer to collect spectral response data of dark noise to obtain reference standard spectral response data, spectral response data of the sample to be measured and spectral response data of the dark noise;
step 2, respectively calculating the standard deviation of reference standard spectral response data, the standard deviation of the spectral response data of the sample to be detected and the standard deviation of the spectral response data of dark noise at each wavelength sampling point, respectively setting an abnormal value rejection criterion and a method according to the standard deviation of the reference standard spectral response data, the standard deviation of the spectral response data of the sample to be detected and the standard deviation of the spectral response data of the dark noise in the color tristimulus value calculation wavelength interval, and rejecting the abnormal value of the standard deviation of the reference standard spectral response data, the standard deviation of the spectral response data of the sample to be detected and the abnormal value of the standard deviation of the spectral response data of the dark noise corresponding to the reference standard spectral response data, the spectral response data of the sample to be detected and the spectral response data of the dark noise;
step 3, respectively calculating the average value of the reference standard spectral response data after the abnormal values are removed, the average value of the spectral response data of the sample to be detected after the abnormal values are removed and the average value of the spectral response data of the dark noise after the abnormal values are removed;
step 4, calculating a first derivative of the average value of the reference standard spectral response data after the abnormal values are removed, and removing two wavelength points lambda corresponding to the maximum value of the first derivative in the wavelength interval of the color tristimulus valueskAnd λk-1Obtaining the average value of the rejected reference standard spectral response data;
step 5, calculating a first derivative of the average value of the spectral response data of the sample to be detected after the abnormal value is removed, and removing two wavelength points lambda corresponding to the maximum value of the first derivative in the wavelength interval of the color tristimulus value calculationkAnd λk-1Obtaining the average value of the spectral response data of the rejected samples to be detected;
step 6, calculating a first derivative of the average value of the spectral response data of the dark noise after the abnormal value is removed, and removing two wavelength points lambda corresponding to the maximum value of the first derivative in the wavelength interval of the color tristimulus value calculationkAnd λk-1Obtaining the average value of the spectral response data of the rejected dark noise;
step 7, denoising the average value of the rejected reference standard spectral response data, the average value of the rejected sample to be detected spectral response data and the average value of the rejected dark noise spectral response data by adopting a spectral smoothing method to obtain the average value of the denoised reference standard spectral response data, the average value of the denoised sample to be detected spectral response data and the average value of the denoised dark noise spectral response data;
step 8, in a visible light range, calculating a wavelength interval by using a color tristimulus value as an interpolation interval, and converting the average value of the denoised reference standard spectral response data, the average value of the denoised sample spectral response data and the average value of the denoised dark noise spectral response data by adopting an interpolation algorithm to obtain the average value of the converted reference standard spectral response data, the average value of the converted sample spectral response data and the average value of the converted dark noise spectral response data;
step 9, calculating the spectral reflectivity of the sample to be measured according to the average value of the converted reference standard spectral response data, the average value of the converted sample to be measured spectral response data and the average value of the converted dark noise spectral response data;
and step 10, calculating the color tristimulus value of the sample to be detected according to the calculated spectral reflectivity of the sample to be detected.
3. The spectral preprocessing method for color tristimulus value calculation according to claim 2, wherein the step 1 specifically comprises:
respectively collecting spectral response data of the reference standard, the sample to be detected and dark noise by using an optical fiber spectrometer, wherein the collection times are not less than 6 times to obtain reference standard spectral response data Vr(lambda) spectral response data V of the sample to be measureds(λ) and spectral response data V of dark noised(lambda), the integration time of the fiber spectrometer is the integration time when the reference standard spectral response data reaches the maximum, and the spectral sampling wavelength interval is less than one tenth of the wavelength interval of the color tristimulus value calculation.
4. The spectral preprocessing method for color tristimulus value calculation according to claim 2, wherein the step 2 specifically comprises:
calculating the standard deviation s of the reference standard spectral response data at each wavelength sampling pointr(λ), as follows:
wherein s isr(λ) represents the standard deviation of the reference standard spectral response data, n represents the number of measurements, i represents the ith measurement, λ represents the wavelength, Vr-i(λ) represents the reference standard spectral response data for the ith measurement,
means representing reference standard spectral response data;
searching for standard deviation s of reference standard spectral response data within the color tristimulus value calculation wavelength intervalr(lambda) maximum wavelength point lambdamax_rAnd eliminating abnormal wavelength point lambdamax_rReference standard spectral response data of (a);
calculating the standard deviation s of the spectral response data of the sample to be measured at each wavelength sampling points(λ), as follows:
wherein s iss(lambda) represents the standard deviation of the spectral response data of the sample to be measured, n represents the number of measurements, i represents the ith measurement, lambda represents the wavelength, Vs-i(lambda) represents the spectral response data of the sample to be measured of the ith measurement,
the average value of the spectral response data of the sample to be measured is represented;
searching the standard deviation s of the spectral response data of the sample to be detected in the wavelength interval of the color tristimulus valuess(lambda) maximum wavelength point lambdamax_sAnd eliminating abnormal wavelength point lambdamax_sSpectral response data of the sample to be detected;
calculating the standard deviation s of the spectral response data of the dark noise at each wavelength sampling pointd(λ), as follows:
wherein s isd(λ) represents the standard deviation of the spectral response data of dark noise, n represents the number of measurements, i represents the ith measurement, λ represents the wavelength, Vd-i(lambda) represents the spectral response data of the dark noise measured at the ith time,
an average of spectral response data representing dark noise;
finding the standard deviation s of the spectral response data of dark noise within the color tristimulus value calculation wavelength intervald(lambda) maximum wavelength point lambdamax_dAnd eliminating abnormal wavelength point lambdamax_dThe spectral response data of the dark noise.
5. The spectral preprocessing method for color tristimulus value calculation according to claim 2, wherein said step 3 specifically comprises:
respectively calculating the average value of the reference standard spectral response data after eliminating the abnormal values
Average value of spectral response data of sample to be detected after eliminating abnormal value
And average value of spectral response data of dark noise after removing abnormal value
6. The spectral preprocessing method for color tristimulus value calculation according to claim 2, wherein the steps 4, 5 and 6 specifically include:
calculating the average value of the reference standard spectral response data after eliminating the abnormal value
First derivative d ofr(λ), as follows:
wherein d isr(λj) A first derivative representing the average of the reference standard spectral response data after outliers are removed,
denotes λjThe wavelength points are referenced to the average of the standard spectral response data,
denotes λj-1The wavelength points are referenced to the mean of the standard spectral response data, λ denotes wavelength, λjDenotes the jth wavelength point, λj-1Represents the j-1 wavelength point;
within the wavelength interval of color tristimulus values, the average value of the reference standard spectral response data after the abnormal values are removed
First derivative d ofrTwo wavelength points λ corresponding to the maximum value in (λ)kAnd λk-1The spectral response data are removed to obtain the average value of the reference standard spectral response data after removal
Calculating the average value of the spectral response data of the sample to be detected after eliminating the abnormal value
First derivative d ofs(λ), as follows:
wherein d iss(λj) The first derivative of the average value of the spectral response data of the sample to be detected after the abnormal value is eliminated is shown,
denotes λjThe average value of the spectral response data of the sample to be detected after the abnormal value is removed from the wavelength points,
denotes λj-1The average value of the spectral response data of the sample to be measured after the abnormal value of the wavelength point is removed, wherein lambda represents the wavelength, and lambda represents the wavelengthjDenotes the jth wavelength point, λj-1Represents the j-1 wavelength point;
within the wavelength interval of the color tristimulus values, the average value of the spectral response data of the sample to be measured after the abnormal values are removed
First derivative d ofsTwo wavelength points λ corresponding to the maximum value in (λ)kAnd λk-1The spectral response data are removed to obtain the average value of the spectral response data of the samples to be detected after removal
Calculating the average value of the spectral response data of the dark noise after eliminating the abnormal value
First derivative d ofd(λ), as follows:
wherein d isd(λj) The first derivative of the average of the spectral response data representing dark noise after outliers are removed,
denotes λjThe average value of the spectral response data of the dark noise after the abnormal value is removed from the wavelength points,
denotes λj-1Average value of spectral response data of dark noise with abnormal values removed from wavelength points, wherein lambda represents wavelength and lambda represents noisejDenotes the jth wavelength point, λj-1Represents the j-1 wavelength point;
within the wavelength interval of color tristimulus value calculation, the average value of the spectral response data of the dark noise after the abnormal value is removed
First derivative d ofdTwo wavelength points λ corresponding to the maximum value in (λ)kAnd λk-1The spectral response data is removed to obtain the average value of the spectral response data of the removed dark noise
7. The spectral preprocessing method for color tristimulus value calculation according to claim 2, characterized in that said step 7 specifically comprises:
averaging the rejected reference standard spectral response data by adopting a spectral smoothing method
Average value of spectral response data of rejected samples to be detected
And after being removedAverage of the spectral response data of the dark noise
De-noising to obtain the average value of the de-noised reference standard spectral response data
Average value of denoised spectral response data of sample to be detected
And average of spectral response data of denoised dark noise
8. The spectral preprocessing method for color tristimulus value calculation according to claim 2, wherein said step 8 specifically comprises:
in the visible light range, the wavelength interval is calculated by using the color tristimulus values as an interpolation interval, and the average value of the denoised reference standard spectral response data is calculated by adopting an interpolation algorithm
Average value of denoised spectral response data of sample to be detected
And average of spectral response data of denoised dark noise
Converting to obtain the average value of the converted reference standard spectral response data in the same wavelength range and the same wavelength interval
After conversion to be readyAverage value of spectral response data of measured sample
And average of the converted spectral response data of dark noise
9. The spectral preprocessing method for color tristimulus value calculation according to claim 2, wherein said step 9 specifically comprises:
based on the obtained average value of the converted reference standard spectral response data
Average value of converted spectral response data of sample to be detected
And average of the converted spectral response data of dark noise
Calculating the spectral reflectivity rho of the sample to be measureds(λ), as follows:
where ρ iss(lambda) represents the spectral reflectance, rho, of the sample to be measuredr(λ) denotes the spectral reflectance of the reference standard, λ denotes the wavelength,
represents the average of the converted reference standard spectral response data,
represents the average value of the converted spectral response data of the sample to be measured,
an average of the spectral response data representing the converted dark noise.
10. The spectral preprocessing method for color tristimulus value calculation according to claim 2, characterized in that said step 10 specifically comprises:
according to the obtained spectral reflectivity rho of the sample to be measureds(lambda), calculating the color tristimulus value of the sample to be measured as follows:
where ρ iss(lambda) represents the spectral reflectance of the sample to be measured, S (lambda) represents the CIE specified relative spectral power distribution of the standard illuminant,
for the standard chromaticity observer data specified by CIE, Δ λ represents the wavelength interval, k represents the normalization coefficient, and λ represents the wavelength.
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