CN109239918A

CN109239918A - A kind of Optical devices changing human color vision's perception

Info

Publication number: CN109239918A
Application number: CN201811359234.XA
Authority: CN
Inventors: 柯南瓦伦丁
Original assignee: Individual
Current assignee: Individual
Priority date: 2016-08-30
Filing date: 2016-08-30
Publication date: 2019-01-18
Also published as: CN106199954B; CN106199954A

Abstract

The invention discloses a kind of design methods of Optical devices, it passes through selection colorant and its concentration and the transmitted spectrum design for correct the weak colour blindness of blue yellow, the colour vision perception that the weak achromate of blue yellow can be corrected, reservation or the faint position for changing user's colour vision white point while improving its blue yellow coloured silk resolution.

Description

Optical device for changing human color vision perception

Technical Field

The invention belongs to the fields of optics, colorimetry and medicine, and particularly relates to an intelligent colorant matching and optical device design method for an optical transmission device based on a colorant and used for realizing target absorption or transmission spectrum. And to an optical device design method for thin film based optical devices to achieve a target transmission spectrum.

Background

With aging, the visual function of the human lens and pupil gradually decreases so that the color vision white point shifts to yellow and finally blue-yellow weak achromatopsia is caused. For example, in the middle-aged and elderly people, blue-yellow color confusion, i.e., blue-yellow amblyopia, which is caused by the continuous change of color vision exists in a large amount. The later age-related blue-yellow blindness weakness behaves like the hereditary red-green blindness weakness. Other diseases and perennial habits can also lead to the formation of blue-yellow amblyopia, which includes both cone damage and a decrease in the visual function of the lens and pupil, and thus such patients are not limited to the elderly. Such diseases include, but are not limited to, diabetes, glaucoma, macular degeneration, Alzheimer's disease, Parkinson's disease, multiple sclerosis, chronic alcoholism, leukemia and sickle cell anemia. The existing reports indicate that the blue-yellow amblyopia caused by aging and various etiologies accounts for 45% to 65% of the mouth of the middle-aged and elderly people. Recent demographic data indicate that in some developed and developing countries, such as the united states and china, the number of elderly people is increasing and even some countries are entering into population aging. Therefore, an optical device capable of correcting the color perception of the person with the color blindness of blue and yellow is more important. At present, there is no optical device, such as glasses, which can correct the color vision perception of the above-mentioned blue-yellow amblyopia, i.e. the position of the user's color vision white point is retained or weakly changed while the blue-yellow color resolution is improved.

Disclosure of Invention

In order to solve the above problems, the invention discloses a design method of an optical device for correcting blue-yellow amblyopia, which comprises the following steps:

1) carrying out color blindness and color weakness test on a user;

2) testing data of a color matching function of a user;

3) optimizing mapping from standard color matching functions to user's color matching functionsH(λ)OrWhereinH(λ)Is optimized by using three color matching functions of the color matching function and the standard of the userThe optimal yellowing function of the pre-retinal medium,is the set of the optimal color sense transformation function and the retina medium abnormal function;

4) according toH(λ)OrOptimizing each main design index of color perception;

5) bonding ofH(λ)OrAnd each of the main design criteria, designing the optical device.

The test in the step 1) comprises classification judgment and direct measurement, and the classification and rough degree of the blue-yellow blind color blindness and color blindness are tested through the classification judgment; the direct determination is to accurately determine the category and degree of achromatopsia and color weakness by measuring the sensitivity of a cone of view to different light waves, and the determined blue-yellow blindness category comprises two categories: the first type is the retinal medium abnormality but the S-M-L trichromatism cone is normal, the second type is the retinal medium abnormality and the S-M-L trichromatism cone abnormality, and the determined degree of the blue-yellow blindness is divided into mild degree, moderate degree and severe degree according to the degree.

Wherein, in the step 2), for the first kind of blue-yellow blind color weaknesses, the color matching function is the CIE standard color matching functionAnd its yellowing functionAt different transmission wavelengths (λ), i.e.

,

Wherein,is a color matching function for a patient with weak blue and yellow;

for the second kind of blue-yellow weak patients, the color matching function is that the standard color matching function passes through the yellowing function according to the abnormal sensitivities of the patients to different light wavesH(λ)The transition that is carried out, namely:

,

，

wherein the color sense transition function,,Which are used to describe the sensitivity of the L, M, S cones to anomalies in the respective wavelengths of transmitted light, including anomalies in the detection of the cones themselves and/or anomalies in the perception of color behind the cones, respectively.

In the step 3), an artificial intelligent optimizer is used for automatically verifying the linear, nonlinear, convex or non-convex type of the optimal mapping, and a proper optimizer is selected to actually optimize the target.

The artificial intelligence optimization method comprises a linear optimization simplicity method, a convex optimization interior point method, a secondary gradient method, non-convex optimization simulated annealing, a genetic algorithm, dynamic dimension search and macrodimension annealing.

Wherein the setting is based on an optimization methodH(λ)Having a single value at each wavelength, using a blue-yellow weak patient extension matching function and a standard color matching function through a weight function { w } pairH(λ)Expressed as:

wherein the weight function w isA weighted average of the ratio of the color matching function to the standard color matching function for a patient with weak blue and yellow at a particular wavelength,

and

；

alternatively, the weighting functions w are equal,

；

alternatively, the weighting function w is based on the difference from the standard color matching function for the patient with weak blue-yellow color:

；

alternatively, the weight function w is based on the value of the color matching function itself for the blue-yellow weak patient multiplied by the gap between the color matching function for the blue-yellow weak patient and the standard color matching function:

。

wherein a color difference minimization is established by the following optimization formH(λ)Solution:

the limiting conditions are as follows:

where γ is the weight function, u and v are variables for a patient with blue-yellow achromate in CIELUV color space, I is the ith Munsell color, H is the variable derived from the yellowing function, N is the number of Munsell colors, { X, Y, Z } is the tristimulus value, I is the light source spectrum, C_iIs the spectrum of the ith munsell color,is a function of the standard color matching function,is a color matching function for patients with weak blue and yellow colors.

Wherein the same optimization form is used to establish the solution of H (lambda)The solution of (1).

The design indexes in the step 4) comprise blue-yellow difference distance, white point position, saturated colors, color gamut area of soft colors, red-green color difference and/or chromaticity.

Wherein, in the step 5), the design can be directly offsetH(λ)OrTransmission spectrum ofΨ(lambda) and (D) is such thatWherein,Ψ(λ)Less than or equal to 1, C is a constant,Ψis the transmission spectrum of the optical device that corrects or improves the color perception.

Wherein, in step 5), theApplied to the spectral power distribution M (λ) such that a new spectral power distribution is obtainedWhereinM _old (λ)The spectral power distribution before change is a function of the optical body spectrum and the transmission spectrum of the optical device.

The invention also discloses a design method of the optical device, which comprises the following steps:

(1) selecting a colorant and its concentration;

(2) and (4) carrying out transmission spectrum design for correcting the blue-yellow weak color blindness, wherein the design method is adopted in the step.

Further, the colorant and the concentration thereof are selected in the step (1) by the following selection method:

11) inputting an optimization target;

12) screening a colorant from an electronic database of colorants;

13) specifying a constraint index;

14) simulating the optical transmission of an optical device taking a colorant as an effective component and the color perception effect of a user by using an optical and coloristic simulation method;

15) optimizing various parameters of the desired optical device to be as close as possible to or achieve the desired goal within the required range of the constraint index;

16) drawing and displaying data of the design result;

17) storing the design result.

Wherein, in step 14), an optical simulation of the absorption of light by the colorant in the matrix is carried out according to the lambert-beer law by integrating the incident light of the matrix of the layer, the molar extinction and density of the colorant in the layer and the thickness of the matrix; optical simulations of the fluorescence generated by each colorant in the matrix were performed by the emission spectral characteristics of the colorant, the integrated incident light intensity and quantum yield of the matrix of the layer, and the diminishing effect produced by the physical and geometric characteristics of the optically transmissive device and the human eye.

Wherein, the optimization in the step 15) comprises the following steps:

151) determining the fluorescence shape coefficient of the colorant according to the relative position and distance between the geometry of the optical device and human eyes and the geometry of pupils of the human eyes;

152) inputting a singular spectral target;

153) defining an optimized cost function of a design spectrum;

154) optical device design goals of individual or composite nature are selected for multi-objective optimization.

Wherein, the simulation method adopted in step 4) gives the optical device multiple degrees of freedom, including but not limited to: the effective structure of the optical device is composed of a single base layer or a plurality of base layers, a single coloring agent or a plurality of coloring agents exist in each base layer at the same time as effective spectrum absorption components, the thickness of each base layer can be freely regulated, and the type and the concentration of the coloring agents in each base layer can also be independently regulated.

The final transmission spectrum of one layer in the optical device is composed of incident light and fluorescence which are not absorbed by the layer, the comprehensive spectrum of the multilayer matrix in the optical device is calculated in sequence according to the matrix layers passing through the incident path of the incident light, and the comprehensive transmission spectrum of the whole optical transmission device is the comprehensive transmission spectrum passing through the last layer.

Wherein the optimization objectives in step 11) include, but are not limited to, transmission spectrum objectives, and/or colorant amounts, and/or optical device substrate layer numbers, thicknesses, and/or various desired color perception metrics such as color saturation, color gamut, color difference, chromaticity, white point location, and/or optical device manufacturing cost.

The database of colorants includes, among other things, the available parameters of the colorants including, but not limited to, the type of colorant involved, the absorption spectrum characteristics, the molar extinction coefficient, the fluorescence spectrum characteristics, the quantum yield, the excitation, the optical stability, the chemical stability, the thermodynamic stability, the solubility and optical changes in different matrices, the chemical interactions with other colorants, the cost.

Wherein the constraint index in step 13) is any one or more optimization objectives in step 11).

And judging the optimal optimization mode of the optimization method adopted in the step 15), judging whether the properties of the optimization and constraint indexes are linear, convex or multi-objective, and selecting the optimal optimization method to optimize each parameter of the optical device based on the properties.

Wherein, the optimization category is judged by adopting an artificial intelligence method.

Among other things, optimization goals and constraints include the number, thickness, and refractive index of the base layers, the type, amount, concentration, and manufacturing cost of the colorants in each base layer, the thickness, refractive index, and overall number and manufacturing cost of the colorants throughout the optical device.

Wherein, the optimization method involved in step 15) includes but is not limited to: a linear optimization simplicity method, a convex optimization interior point method and a secondary gradient method, a non-convex optimization simulated annealing, a genetic algorithm and dynamic dimension search.

Wherein the colorant fluorescence shape factor is an absolute shape factor or a relative fluorescence shape factor based on a shape factor of light transmission of the optical device.

Wherein, the comprehensive incident light of the substrate is obtained by the vector linear superposition of the comprehensive transmitted light of the previous substrate and the fluorescence generated by all the colorants in the substrate, and the specific algorithm is as follows:

wherein,the composite incident light of the nth layer matrix;

is the integrated transmitted light of the n-1 layer matrix;

the substrate colorant of the n-th layer absorbs the fluorescence generated by the incident light of the n-th layer, and has a shape factor of；

Is fluorescence generated by the n-1 th layer of the substrate absorbing the fluorescence of the n-th layer of the substrate, and has a shape factor ofWhere i is the index of the colorant.Is the total number of colorants in the matrix of the nth layer.

Wherein the integrated transmitted light of each substrate layer is the change of the integrated incident light of the substrate layer according to a plurality of colorants in the substrate layer, the change is calculated by a logarithmic superposition method of the absorption of light by each colorant, and the logarithmic superposition formula is as follows:

wherein,is the overall transmitted light of the nth layer of matrix,

the transmission spectrum of all colorants of the n-th layer matrix.

Wherein the function of the optical simulation is:

the fluorescence at wavelength λ intrinsic to the n-base layer can be expressed as:

wherein,

Ψ_i,nthe integrated value of fluorescence generated for colorant i in the visible range (380 to 780 nm);

is the fluorescence spectrum of the colorant i after normalization at the wavelength lambda;

for the colorant i in the base layer n at the wavelength(ii) independent fluorescence of;

for all colorants radiating from the substrate n to the substrate n +1 at the wavelengthThe remaining fluorescence of the next step;

is a wavelengthThen, the ratio of the fluorescence generated by the colorant i to the residual fluorescence after the fluorescence is absorbed and consumed by other colorants in the matrix layer n is determined;

first moment of center (first moment arm) which is the ratio of the remaining fluorescence in the stromal layer n;

is the total number of colorants in the matrix of the nth layer;

the transmission spectrum of all colorants that are the matrix of the nth layer;

is the form factor of the n-th to n + 1-th substrates.

Wherein the optimization cost function is:

wherein

N is the number of layers of the substrate in the optical device;

TS is an abbreviation for transmission spectrum;

TS_Targetand TS_DesignTarget and designed transmission spectra, respectively;

u is the total number of unique colorants;

SR is the variation vector of two adjacent spectral regions;

j is the number of SR spectral regions;

j is the index of the SR region;

γ₁and gamma₂A cost parameter;

α and β are constants;

a is a colorant usage amount limit;

b is a constant;

η is the number of 1 nanometer unit light wave in SR spectrum region;

SP is the index of the target and design.

Wherein, when a large number of variables are to be optimized after the non-convex optimization is determined, a heuristic algorithm of macroannealing is automatically used.

Wherein, the heuristic algorithm of the macro-dimensional annealing comprises the following steps:

1) searching a probability function used by a neighborhood for each variable needing to be optimized so as to construct a new candidate solution;

2) checking whether the candidate solution satisfies the design constraint, and if not, reselecting the candidate solution value to satisfy the related design constraint;

3) the candidate solution comprises a variable which is not changed and a variable which is changed, and the candidate solution is evaluated to judge the change of the candidate solution to the optimization target and judge whether all the limiting conditions are met;

4) calculating the target value of the candidate solution plus any cost exceeding the limiting condition as a total cost value, and comparing the total cost value difference between the candidate solution and the current solution;

5) if the cost of the candidate solution is less than the current solution, the candidate solution is accepted as a new current solution and used for the next round of candidate solution and total cost calculation; if the cost of the candidate solution is greater than the current solution, then the candidate solution has a probability of being accepted as the provisional current solution and used for the next round of computation.

The number of cyclic solutions is a preset value, and the algorithm is terminated when the number of cyclic solutions is reached or the cost variation is smaller than a threshold value.

The determination of the artificial intelligence optimal optimization mode is to determine whether the optimization and constraint target properties are linear, convex or multi-objective, wherein the property determination comprises calculating and determining the hessian matrix and the associated feature scalar value, or a rapid gradient descent method or a gradient ascent method is used for identifying the existence of the local optimal solution.

The optimization method comprises two major categories of convex optimization and non-convex optimization, and the judgment of the optimization category by the artificial intelligence method comprises the steps of judging whether an optimized Hessian matrix is a semi-positive definite matrix or not, wherein the Hessian matrix is expressed as follows:

where f is an optimization objective or constraint function;

c is the colorant concentration;

is the concentration of the colorant and its matrix layer.

Wherein, the optical spectrum comprises a transmission spectrum with relatively high absorption in the optical wave range of 440-540nm, or a transmission spectrum with relatively high absorption in any wavelength band of 440-540nm, or a transmission spectrum with relatively high absorption in the optical wave range of 556-626nm, or a transmission spectrum with relatively high absorption in any wavelength band of 556-626nm,

wherein the designed transmission spectrum is realized by the selection of the colorant and the concentration and combination mode thereof, wherein the colorant is used for absorbing the spectrum in the region of 630-780nm to maintain the position index of the white point.

Wherein the colorant comprises cyanine dye, triarylmethane dye, coumarin, fluorone, xanthene, sulfonated colorant, oxazine, pyrene, and derivatives thereof.

Wherein the thickness of the dielectric layer of the optical device is 0.03 to 90 mm; the number of the dielectric layers is 1-300; the respective colorant concentration is from 0.02 to 5000 micromolar.

The invention aims to invent an optical device for correcting and improving the perception of blue-yellow blind color weakness. The method of the invention can also be used for designing optical devices for correcting other forms of color-blind color weaknesses. The invention includes the transmission spectrum of such optical devices, as well as colorants used to achieve such transmission spectrum, the physical dimensions of the devices (including layering), colorimetric parameters, optimization of the cost and properties of the colorants, and design methods and results. In contrast to the red-green blind Test, the classification of blue-yellow blind colour weakness requires special pseudometameric tests (pseudomorphic images) and special colour arrangement tests such as the Farnsworth-Munsell Hue Test (Farnsworth-Munsell Hue Test). When the direct test is carried out on the person with the blue-yellow blind, the type and the degree of the color blindness can be tested by using an achromatopscope (anomalscope).

Drawings

FIG. 1: a standard color matching function;

FIG. 2: color matching function for blue-yellow weak patients;

FIG. 3: first example optical device user (dashed line) and naked eye (solid line) gamut performance in CIELUV color space;

FIG. 4: second example color gamut performance in CIELUV color space by the user of the optical device (dashed line) and the naked eye (solid line);

FIG. 5: color perception of a user of the optical device is a number of examples.

FIG. 6: a spatial schematic of human color perception represented by CIELUV;

FIG. 7: a smoothed optical device optimal spectral example graph;

FIG. 8 is a set of graphs showing the results of smoothing of the resulting transmission spectra;

FIG. 9 is a flow chart of the operation of a specific design method for the transmission spectrum of an optical device.

FIG. 10: the light source and the substrate layer of the optical transmission device are schematically transmitted;

FIG. 11: schematic diagram of substrate layer, colorant and color perception design;

FIG. 12: the invention uses a shape coefficient model diagram describing the fluorescence generated by an optical transmission device received by human eyes;

FIG. 13: using the method of the present invention to achieve a first exemplary plot of a target spectrum for an optical lens;

FIG. 14: a second exemplary illustration of an optical lens achieving a target spectrum using the method of the present invention;

FIG. 15: a third exemplary illustration of an optical lens achieving a target spectrum is achieved using the method of the present invention.

Detailed Description

Design method for correcting transmission spectrum and spectral energy distribution of blue-yellow weak color blindness

Yellowing of the visual white point (color perception) caused by a decrease in the visual function of the lens, pupil and others can cause the patient to have poor color blindness of blue-yellow. The objective color perception white point yellowing has various causes. These reasons are: (1) yellowing of the lens caused by changes in lens material, (2) yellowing of the lens caused by excessive blue light absorption by the lens due to miosis, (3) abnormalities in the trichromat, and (4) abnormalities in color perception behind the trichromat, such as abnormal analysis of color perception by the brain. (1) And (2) are examples of reduced function of the preretinal medium. All three-color view frustum abnormalities of the present invention include abnormalities in the view frustum itself and/or abnormalities in the perception of color behind the view frustum. Please look at the color perception transition function later.

In general, the method of the invention aims at the following four conditions of blue-yellow blindness and weak color and provides a correction mode: (a) if the cone is normal, the formation of blue-yellow achromatism is yellowing of the lens caused by the reduction of the function of the retina foreignal medium, and the color vision perception can be corrected by counteracting the redundant yellow through an optical device so as to restore the normal color vision. (b) If the function of the cone is abnormally added with the function of the preretinal medium to be reduced, the blue-yellow blindness and the amblyopia can also correct the color vision perception by offsetting the redundant yellow through an optical device so as to restore the normal color vision. (c) The blue-yellow perception is improved, corrected and even enhanced by increasing the blue-yellow difference, including the fact that the corrected white point is objectively white. (d) The perception of blue and yellow is improved, corrected and even enhanced by increasing the saturation of a plurality (including all) colors, including the correction of the white point location as an objective white.

First, test

The invention will test the achromate and the achromate by two methods, namely classification judgment and direct measurement.

The classification determination is to determine the kind and rough degree of the color blindness and the degree of the color blindness using a blue-yellow color blindness map or pseudo-isochromatic image Test (pseudochromatism images) and a color arrangement Test such as a Farnsworth-Munsell Test (Farnsworth-Munsell Hue Test). The decision can test the category and rough degree to which the blue-yellow blind color-blind person belongs. The determined blue-yellow blind category includes two broad categories: the first is an abnormal preretinal medium (reduced function, e.g., yellowing of the lens) but normal S-M-L trichromatis. The second category is epiretinal media abnormalities and S-M-L trichromatism pyramidal abnormalities. The determined degree of blue-yellow blindness is roughly classified according to lightness, such as mild, moderate and severe.

Direct determination tests are used to determine the type and degree of achromatopsia and weakness by measuring the sensitivity of the cone of view to different light waves (colors). The current method of direct determination is based on the use of achromatopscope (anomaloscope). By using the device, the color blindness, the degree and the category of the color blindness and the color blindness of the blue-yellow color amblyopia can be accurately digitalized.

Obtaining data of color matching function

For normal human color perception, it is determined by the perception of color by the trichroic cones in the eye. The trichroic cones are the S cone that primarily perceives short wavelengths, the M cone that primarily perceives medium wavelengths, and the L cone that primarily perceives long wavelengths, respectively. The response of the ordinary human trichromat to light waves changes along with the change of the wavelength. The CIE uses the following standard Color Matching Function (CMF) to express the light wave sensitivity of the common human S-M-L cone to its induced color (i.e. the intensity perception of light waves at different wavelengths), respectively, see fig. 1:

-a standard color matching function for the L view frustum,

-a standard color matching function for the M view cones,

-standard color matching function of S view frustum

In any color (sensation) space of the present invention, such as the CIE color space, human perception of color of any color can be finally normalized to the sensation of a trichroic cone, which is a tristimulus value. In CIE color space, any set of light waves, expressed as their spectral energy distributionM(λ)The tristimulus values of (a) can be expressed as follows:

the standard trichromatic cone lightwave sensitivity, i.e. the standard color matching function, in fig. 1 is an average generalization of CIE to normal humans as a whole population.

FIG. 2 is a color matching function for a patient with weak bluish-yellow color. Including yellowing functionH(λ)Also, it isIs the transmission spectrum of an optical device, such as a yellow-tinted lens. Its use includes the use of optical devices containing the transmission spectrum to achieve a color perception of a blue-yellow weak patient, and also includes spectra designed to cancel the transmission spectrum.

The sensitivity of the light wave of the regular cone can be directly connected with the CIE standard color matching function，And (c) expressing.

For the first type of blue-yellow achromate, the sensitivity of the cone light wave measured therefrom can be regarded as the CIE standard color matching function due to the abnormality of the preretinal mediumWith its yellowing functionAt different transmission wavelengths (λ). The resulting color matching function for the first category of weak blue-yellow patients is thus:

,

is the yellowing function of the preretinal medium.And also the transmission spectrum after yellowing of the lens and other media in front of the retina.

Is a color matching function for patients with weak blue and yellow colors. The color matching function of the individual blue-yellow blind color weakness can be expressed by the method, and the color matching function of the whole population can also be obtained by statistics. Data on the color matching functions of the bluish-yellow weak patients were examined using a special pseudo-isochromogram Test (pseudoisochromic images), or a special color arrangement Test such as the Farnsworth-Munsell Hue Test (Farnsworth-Munsell Hue Test), or a color blindness mirror (anomalascope).

For the second category of patients in the determination (abnormal additive cone abnormality of the preretinal medium) with weak blue-yellow color, one way to express the color matching function is to express the standard color matching function by a yellowing function according to the abnormal sensitivity of the patient to different light wavesH(λ)The transition is performed. The resulting color matching function for the second type of weak blue-yellow patients is:

,

wherein the color sense transition function,,Are used to describe the sensitivity of the L, M, S cones to anomalies in the respective wavelengths of transmitted light, including anomalies in the detection of the cones themselves and anomalies in the perception of color behind the cones, e.g. anomaly analysis of color perception by the brain. The designer can change the design under the appropriate conditionsFor example when the functions differ little or for simplicity of the design of the optical device.Is a collection of color perception transition functions and epiretinal medium abnormality functions.

In the target group of the present inventionH(λ)Not genetically dependent, but rather aging or disease. Which comprises the following steps: diabetes, glaucoma, macular degeneration, Alzheimer's disease, Parkinson's disease, multiple sclerosis, alcoholism, leukemia and sickle cell anemia.

In the designH(λ)The abnormalities represented by the preretinal media may change over time or as the disease worsens or diminishes. These changes are partially or fully predictable. Therefore, in the present invention, the designer can design appropriate H (λ) for different people with color weakness and color blindness. For example, the designer may well design an optical device to embody H (λ) with the most recently measured patient color perception data. The designer may also design the optical instrument to counteractPredictable color perception changes or other different H (λ) combinations, such as average H (λ) for the next decade or different H (λ) for the left and right eyes.

Thirdly, adjusting a color matching function of the user;

the resulting cone-light wave sensitivities of the blue-yellow blind color-weaknesses were adjusted by the designer according to the standard observer color matching function of the international commission on illumination. The method of adjustment includes shifting the sensitivity peaks of the color matching function and changing the overall sensitivity distribution. These results become the corresponding color matching functions for each patient with different degrees of color blindness and weakness.

These cone sensitivities are also a function, such as a normal distribution or a Weibull distribution (Weibull). The designer may adjust the peaks, shapes and other parameters therein. On the basis of which the result of the direct measurement can be reflected by adjusting the parameters in this function.

Fourthly, optimization

Optimizing a mapping from a standard color matching function to a color matching function of a first or second class of blue-yellow weakly color blind patient using an artificial intelligence optimizerH(λ)Or。

The artificial intelligence optimization type calibrator can automatically calibrate the linear, nonlinear, convex or non-convex type of the optimal mapping and is used for selecting a proper optimizer to actually optimize the target. For example, the optimization objective is to design a best fit yellowing function using three tested color matching functions of blue-yellow and weakly achromate and standardH(λ)Or the set of color sense transformation function and epiretinal medium abnormality function that best matches. An artificial intelligence optimization type calibrator verifies the optimization type of the target. Then useAdapted to this type of optimisation to obtain the optimumH(λ)Or。

In the field of optics and in the field of colorimetry,H(λ)orCorresponding to a spectrum for changing normal people into achromatopsia patients. Therefore, the invention has the advantage that the color blindness and color weakness glasses are designed through artificial intelligence and high-level optimization, and the color blindness and color weakness glasses are offset or corrected through the transmission spectrum of the glassesH(λ)OrResulting in color blindness and weakness. For normal persons, debilitatedH(λ)OrCorresponds to a spectrum that weakens the color perception in vision (color perception). Better color sense can make people see more saturated and vivid colors. Therefore, all methods for designing an optical device for correcting color weakness and color blindness (methods for judging the type and weight of color weakness and color blindness are not included) in the present invention can also be used for designing an optical device for improving the color sense of normal people.

Optimized type verification involves calculating and judging the values of the Hessian Matrix (Hessian Matrix) and the associated feature scalar (eigenvalue). It also includes the use of a rapid gradient descent or gradient ascent method to verify the existence of locally optimal solutions.

The invention comprises a plurality of artificial intelligence optimization methods. For example, linear optimization simplistic method (simplex), convex optimization interior point method (interior point) and secondary gradient method (sub-gradient method), and non-convex optimization simulated annealing (simulated annealing), genetic algorithm (genetic algorithm), dynamic dimension search (dynamic dimensional search) and the inventive novel macro annealing (large dimensional annealing). The invention also encompasses intelligent mixed integer programming.

Based on the above optimization method and settingH(λ)With a single value at each wavelength, the following is a matching function with the standard color using a blue-yellow weak patient versus a weighting function { w }H(λ)Expression of (2). Because of the fact thatSo H (λ) andis consistent with the design method (mathematical formula) of (a). The solution of H (λ) is detailed here as an example.

An intuitive solution is in the functional form:

wherein the weight function w isA weighted average of the ratio of the color matching function to the standard color matching function for a patient with weak blue and yellow at a particular wavelength.

And

an example is solved as

Another example solution is that the weighting function is based on the distance of the patient with weak blue and yellow to the standard color matching function.

The third example solution is that the weighting function is based on the value of the color matching function itself for the blue-yellow weak patient's gap product with the standard color matching function.

In addition to intuitive solutions, one that minimizes differences in color perception derived with strict logicH(λ)The solution is built on the following optimized form:

the limiting conditions are as follows:

γ is the weight function, u and v are the CIELUV color space, blue-yellow-weak (color blind) refers to variables derived from this type of patient, I is the ith Munsell color, H refers to variables derived from the yellowing function, N is the number of Munsell colors, { X, Y, Z } is the tristimulus value, I is the light source spectrum, C is the color intensity of the light source, and_iis the spectrum of the ith munsell color,is a function of the standard color matching function,is a color matching function for patients with weak blue and yellow colors.

Fifth, design the optical device

The resulting optimized color matching functions for blue-yellow weakly-blind patients are used to optimize various primary design criteria for color perception, such as blue-yellow difference distance, white point location. The design indexes also comprise a series of color sense indexes such as color gamut areas of saturated colors and soft colors, red-green color differences, chromaticity and the like.

Is optimally designedH(λ)OrTwo products will result:

(1) an optical device is designed, which includes a direct offsetH(λ)OrTransmission spectrum ofΨ(lambda) and (D) is such that. Wherein

,Ψ(λ)Less than or equal to 1, wherein C is a constant,Ψis the transmission spectrum of the optical device that corrects or improves the color perception.

(2) Will be provided withApplied to the spectral power distribution M (λ) such that a new spectral power distribution is obtainedWhereinM _old (λ)The spectral power distribution before change is a function of the optical body spectrum and the transmission spectrum of the optical device. Is designed outM _new (λ)This spectral power distribution can then be used and the design approach of the transmitted spectrum through [ an optical device that optimizes human color perception]Designing desired spectrum, and designing method using colorant and optical device containing colorant as effective component]To achieve the desired lightAn apparatus for learning.

The invention uses [ a design method of optical device transmission spectrum for optimizing human color perception ] to design functional optical device for changing and optimizing human color perception, including glasses, to correct the ability of user group (especially group with similar characteristics) of first and second blue-yellow weak color blindness to distinguish insensitive color, and improve color perception of sensitive color region; or correcting the ability of the individual with achromatopsia and dyschromatia to distinguish insensitive colors and improving the color sense of sensitive color areas.

The above indexes of the functional optical lens for changing and optimizing human color perception, including the transmission spectrum of the glasses, include but not limited to blue-yellow chromatic aberration, color gamut area, color gamut shape, red-green chromatic aberration, color shift, chromaticity, white point position, UVA/B/C ultraviolet radiation blocking and high-intensity purple blue light blocking.

For example, when maximizing the blue-yellow difference under the conditions of controlling the white point movement and maintaining the red-green difference, one optimization is:

the variables of the above formula are constrained as follows:

where D is the color difference, R is the red group, G is the green group, B is the blue group, Y is the yellow group, < u, v > is the position of the color in the color space, M is the number of colors of the blue group, N is the number of colors of the yellow group, M is the number of colors of the red group, N is the number of colors of the green group, ε is a differential amount, wp is the new white point position and wp, and 0 is the original white point position.

It is clear that,

the invention uses a design method of an optical device with a colorant as an effective component to design an optimal functional optical device, comprising glasses, to improve human color perception, to correct the distinguishing capability of a user group (particularly a group with similar characteristics) with first and second blue-yellow weak color blindness on insensitive colors, and to improve the color sense of a sensitive color area; or correcting the ability of the individual with achromatopsia and dyschromatia to distinguish insensitive colors and improving the color sense of sensitive color areas.

The design of functional optical devices utilizes a variety of colorants (including dyes, pigments) including design of their concentration, fluorescence effects and formulation combinations. The design of the optical device also includes the level, thickness, and refractive index of its substrate. The design of the optical device also includes surface wave films (or film layers) for protecting against abrasion, cutting, water and securing or enhancing other optical, chemical, physical functions and qualities, such as antireflection coatings.

The optical device design method can improve blue-yellow dichroism blindness. Since this dichromatic color blindness occurs when any color sense transition function is zero or minimal, within the above range of accommodating tristimulus cone anomalies.

The present invention may be designed and produced, including but not limited to:

1) transparent or light-transmitting devices, including glasses, windows;

2) light transmission devices of various types of depth and coloration, including sunglasses;

3) the device is provided with various coloring or light-transmitting devices for blocking high-energy violet blue light or ultraviolet light;

4) prescription light transmission devices include various types of myopia, hyperopia, astigmatism, contact lenses, and presbyopic glasses.

In the present invention, the light transmission device (e.g., glasses, window) is designed to have a special function while maintaining high light transmission. The invention uses [ a design method of optical device transmission spectrum for optimizing human color vision perception ] to design the transmission spectrum of the light transmission equipment and simultaneously maintains high light transmission.

On the basis of this, the present invention uses [ a method of designing an optical device containing a colorant as an active ingredient ] to select a colorant and its concentration in combination to achieve a desired transmission spectrum and achieve high light transmittance. A method for designing an optical device with colorants as the active ingredient can also be used for further design screening to meet other design requirements such as cost, device thickness, gradation, and lens curvature.

In the present invention, the colored light-transmitting device (e.g., sunglasses) is designed to have a reduced light-transmitting property while having its special function. The invention uses [ a design method of optical device transmission spectrum for optimizing human color vision perception ] to design the transmission spectrum of the colored light-transmitting equipment and simultaneously reduce the light transmission. On the basis of this, the present invention uses a method of designing an optical device having a colorant as an active ingredient ] to select a colorant and its concentration in combination to achieve a desired transmission spectrum and achieve low light transmittance. A method for designing an optical device with colorants as the active ingredient can also be used for further design screening to meet other design requirements such as cost, device thickness, gradation, and lens curvature.

In the present invention, a tinted light transmission device (e.g., a sunglass) may use a neutral-density filter to achieve primary or additional light absorption, reflection, or scattering, while using a colorant as described above to achieve its particular function.

In the present invention, the various colored, non-colored light transmission devices described above should be used in conjunction with additional film materials to achieve desired in-process, physical, chemical, thermodynamic, optical, protective, aesthetic, device quality and other properties and requirements.

The optical device designed by the method has special functions. For example, the optical device enables a normal person wearer to experience the color perception of the color-blind color-weakest person and design a product which is suitable for the color perception correlation of the true color-blind color-weakest person. The above optical means of altering the perception of normal human color may be used in other special functions, for example to better identify the blue-yellow color or and pattern for the wearer, such as in military applications to distinguish camouflage.

The invention comprises the design of the transmission spectrum with relatively high absorption at 440-540nm to realize the functions of improving the distinguishing capability of the color blindness and color weakness users on insensitive colors, the color sense of the sensitive color regions and optimizing the color sense perception of normal users.

The invention comprises the design of the transmission spectrum with relatively high absorption in any wave band within 440-540nm so as to realize the functions of improving the capability of distinguishing insensitive colors by users with color blindness and color weakness, the color sense of sensitive color areas and optimizing the color sense perception of normal users.

The invention comprises the design of the transmission spectrum with relatively high absorption at 556-626nm so as to realize the functions of improving the capability of distinguishing insensitive colors by users with color blindness and color weakness, the color sense of sensitive color areas and optimizing the color sense perception of ordinary users.

The invention comprises the design of the relatively high-absorption transmission spectrum in any waveband within 556-626nm so as to realize the functions of improving the capability of distinguishing insensitive colors of users with color blindness and color weakness, the color sense of sensitive color areas and optimizing the color sense perception of normal users.

The present invention includes the selection of colorants (dyes, pigments) and their concentrations and combinations to achieve the designed transmission spectra, including the spectral changes (including absorption, fluorescence, astigmatism) in the 440-540nm and 556-626nm regions.

The present invention includes the selection of colorants (dyes, pigments) and their concentrations and combinations to achieve the designed transmission spectrum including the spectral changes outside the 440-540nm and 556-626nm regions to achieve their desired color perception metrics. For example, colorants can be used to absorb the spectrum in the region of 630-780nm to maintain the position index of the white point.

The present application utilizes 820 a variety of different colorants as a library of designs. Colorants used include cyanine dyes (cyanine), triarylmethane dyes (triarylmethane), coumarins (coumarins), fluorones (e.g., rhodamine), xanthenes (xanthenes), including sulfonated colorants, oxazines (oxazines), pyrenes (pyrene), and derivatives of the above colorants.

The dielectric layer thickness of the optical device (e.g., lens) of the examples herein is 0.03 to 90 mm; the number of the dielectric layers is 1-300; the respective colorant concentration is from 0.02 to 5000 micromolar.

(II) design method for optimizing transmission spectrum of optical device for human color vision perception

The present invention also provides a systematic way to design the transmission spectrum that the optical device needs to provide to the user for the color perception that the user of the optical device needs. Optical devices include lenses, spectacles, contact lenses, screens, windshields, and various windows, among other transmissive devices that produce changes to human vision. The glasses include contact lenses, sunglasses, presbyopic glasses, myopia glasses, distance glasses, bifocal glasses, trifocal glasses, progressive multifocal glasses, astigmatic glasses, intraocular lenses and plano glasses. The lenses include camera lenses, intraocular lenses, extraocular lenses, and ocular surface contact lenses.

First, for the optical body spectrum

The invention may encompass the spectrum of light emitters, including natural light, artificial light, theoretical light, and integrated light. Such as CIE D65.

The invention may encompass the spectrum of light reflectors, including natural, artificial, theoretical, and synthetic antiperspirants. For example 1269 Munsell (Munsell) standard colours.

The invention may also encompass the spectrum of phosphors, including phosphors that are natural, artificial, theoretical, and comprehensive. Such as fluorescein (fluorescein).

Second, for color sensation parameter

The present invention may include a human standard color perception (color perception) space of the International Commission on Illumination (CIE), such as 1976 CIELUV, or other color perception space as a color perception space to describe human color perception parameters.

The present invention includes Color Matching Functions (CMFs) of 1931 degree standard observers and 1964 degree standard observers, or other observers, published by the international commission on illumination, to define and describe the human color perception sensitivities to different light waves (which are expressed as foreign colors when they enter the brain), i.e., their positions in the human color perception space (e.g., CIELUV).

The invention comprises calculating various human color perception parameters in a color space. Human color perception is expressed, for example, in 1931 CIE XYZ and 1976CIE LUV color spaces by color gamut, white point position, and color perception parameters such as color difference, color shift, etc. of some colors, of the munsell saturated color set and the mutex color set.

Third, the specific design method of the transmission spectrum of the optical device

1. Determining the spectrum of the incident light by selecting the light body; including luminophors, reflectors, luminophors and comprehensive luminophors. The designer can choose any light emitter, reflector, or phosphor as the light source. Including the light source spectrum set by the designer himself. Such as CIE group a, C, D light sources, light sources for open air detection, any single or group munsell color.

2. The present invention includes a method of selecting a color system based on the goals of visual optimization or control, i.e., color perception parameters such as color gamut, color difference, color shift, and white point location. For example, 10 to 30 of the most common saturated colors in the munsell system are selected to make up the saturated color circle, and 10 to 30 of the most common muted colors in the munsell system are selected to make up the muted color circle. The areas surrounded by the color circles are respectively a saturated color gamut (fig. 6, saturated color gamut) and a soft color gamut (fig. 6, soft color gamut).

3. Human color perception parameters such as color gamut, color difference, color shift, etc. are simulated and calculated (i.e., expressed as respective functions) for the transmittance (T) of light waves at respective wavelengths (λ) in the light wave transmission spectrum. And optimizing and constraining an objective function in a linear or nonlinear mode of artificial intelligence to achieve the required human color sensation index. The designer inputs the color sensation index to be achieved in the objective function.

For example, when it is desired to maximize the blue-yellow difference using the gamut perimeter, one objective function is:

，

where C is the perimeter, T (λ) is the transmission spectrum, < U, V > is a function of T (λ) for the positioning of colors in the CIELUV color space, and I is the selected set of color points that make up the color circle.

For example, when it is desired to maximize the blue-yellow difference using the gamut area, one objective function is:

，

wherein, A is the area of the substrate,<u_wp,v_wp>is the position of the White Point (White Point) in the color space, X is the cross product, and I is the set of color points selected to make up the color circle.

The white point is the balance point of the human eye color perception. The white point shift is therefore an important non-linear parameter to measure the change in the color perception balance point of the human eye. One expression of white point displacement, for example, is the Euclidean distance (Euclidean distance).

"WP" is the new white point, "WP, 0" is the objective white point seen by the ordinary person, and ε is the set constraint value. The white point shift can be limited to any value as required by the design (a white point shift distance of 0 or its maximum distance to the color space edge can be specified). For example, the white point shift is 0.01 units in CIELUV, the human eye cannot perceive the color change. It is also possible to specify a white point shift of 0, but this reduces the feasible area of optimization.

For example, when it is desired to maximize the blue-yellow difference using chromaticity, one objective function is:

n is the number of selected Munsell colors and wp is the location of the white dot, and chromaticity is understood to be an expression of the vividness of the color, the more vividness it is located away from the white dot.

One expression for multi-objective optimization design is to sum the individual objectives together with a linear relationship using a weighted average. Optimization will yield pareto fronts at different weighted averages.

Integrated goal =

A multi-objective optimization of color perception may be in the form of:

the combined goal of maximizing the blue-yellow difference and the gamut area. The constraint for the variable w is 0 ≦ w ≦ 1.

The optical device designer can select the optimized or controlled index of the parameter of the monochromatic color perception. For example by changing the transmission spectrum to maximize the color gamut, and for example to change the perception of color perception of a certain color, such as moving blue to violet.

The designer can select the optimized or controlled bi-level color sensation parameter index. For example, the circumference of the soft color circle is maximized within a range in which the circumference of the saturated color circle is controlled to be enlarged by 15%.

The designer can select multiple color sensation parameter indexes to optimize or control. For example, in a range where the shift of the color perception white point is kept to zero, the area of the soft color circle is maximized while the area of the saturated color circle is maximized, and the color difference of the yellow-blue region is controlled to be shrunk to less than 10%.

Designers can directly constrain the design range of the optical device spectrum. For example, the transmittance of light wavelengths of 540-600 nm is below 20%.

Allowing the maximum and minimum transmission to be limited at a certain nanometric wavelength or range of nanometric wavelengths. For example 2% may guarantee a minimum transmission of color information. For safety reasons, setting a minimum transmission of 2% ensures that the light concerned is not completely blocked. A maximum value, such as 99% maximum transmittance, is set to remove some over-fluorescent materials to avoid strong effects and changes of over-fluorescent light on the actual color, and also to avoid visual safety during night under strong light (e.g., car lights).

4. Artificial intelligence selects the optimal optimization method, which comprises defining the color sensation parameter index to be controlled as a constraint item.

The judgment of the optimal optimization mode of the artificial intelligence comprises the steps of judging the nature of optimization and constraint targets, and judging whether the optimization and constraint targets are linear, convex and multi-objective. For example, after the designer has selected and confirmed the optimization and control objectives, the model automatically (including the designer's manual) performs property verification on the optimization objectives and constraints. Wherein the property verification comprises calculating and judging a hessian matrix (hessian matrix) and associated eigenvalues (eigenvalue). The characterization also includes the use of a rapid gradient descent or gradient ascent method to identify the existence of locally optimal solutions.

The following is an expression of the hessian matrix and eigenvalues,fis an optimization objective or constraint function and OD is the transmission spectrum of the optical device.

As above, for a problem of minimum optimization, if the eigenvalue is semi-positive, it means thatThen, thenfIs convex. The same method can be used for applications with different optical device transmissivities in different nanometer ranges.

The optimization method of artificial intelligence comprises the following steps: linear optimization simplex method (simplex), convex optimization interior point method (interior point) and secondary gradient method (subvariant method), non-convex optimization simulated annealing (simullatedanealization), genetic algorithm (genetic algorithm), dynamic dimensional search (dynamic dimensional search), large dimensional annealing (large dimensional annealing), and the like.

5. The spectrum of the optical device is designed by screening a suitable optimization method through an artificial intelligence selection method. For example, based on the results of the optimized property verification, a simple method is used to achieve linear targets and constraints, a secondary gradient method is used to achieve convex targets and constraints, and a genetic algorithm plus dynamic dimension search or macroannealing is used to achieve non-convex targets and constraints.

The invention creates a non-convex optimization method for macrodimensional annealing. For theThere are 400 unit wavelengths that need to be optimized (380 — 780nm full visible range), and at the beginning of each iteration (loop calculation), the optimization program selects some nanometers for optimization. The nano selection range is large in initial iteration, but with the progress of iteration, the required selection range is reduced, and the reduction speed is directly related to the number of iteration; e.g. speed equal to。

For optical device transmittance at any wavelength within the range, the neighborhood is searched using a probability function, such as a normal distribution, to randomly change it to construct a new temporal transmission spectrum as the current candidate solution. The standard deviation of the probability function here includes the number of iterations based. The smaller the standard deviation of this wavelength as the number of iterations approaches the end sound, the smaller the amplitude of its changed transmittance.

The current solution candidate needs to verify some maximum minimum boundary conditions, such as minimum and maximum transmittance. If the boundary constraint is exceeded, the current candidate solution is reselected until the constraint is satisfied.

Current solution candidates, including the transmittance of the OD (optical device) at invariant wavelengths and the transmittance that varies, are evaluated in sub-equations to determine their changes to the optimization target and to determine whether all constraints are met

The optimal target value of the current candidate solution and the cost of exceeding the constraint are calculated to become the total cost. The total cost of the current candidate solution is compared to the existing optimal total cost.

If the total cost of the current candidate solution is less than the total cost of the existing optimal solution, then the current candidate solution will become the new existing optimal solution and be used for the next round of computation.

If the total cost of the current candidate solution is greater than the total cost of the existing optimal solution, then the current candidate solution has a probability of being tentatively accepted as the new solution for the next round of computation. This probability will be chosen by the designer. This probability will typically be relatively large in early cycle calculations. The probability of accepting a greater total cost decreases with increasing cycle times, often to 0%. The purpose of this is to give the existing solution the opportunity to jump out of the local optimal solution to find a global optimal solution.

The number of solving cycles may be a predetermined value. When the computing resources are sufficiently powerful, the algorithm can ensure convergence to a globally optimal solution. In practice, it can converge rapidly to a good solution, namely the transmission spectrum of the optical device.

The good solution is stored and used as the initial solution of the optimization program later, so that the requirement on computing resources, such as time, can be greatly reduced.

6. Spectral post-processing, such as spectral smoothing, is performed. Smoothing includes breakpoint selection plus smoothing of linear, parabolic, or spline functions (spline). Of course, smoothing may be omitted as desired.

7. Drawing and displaying data of the design result. Such as spectra, color perception, color difference, color shift, chromaticity, white point, and data display.

8. Storing the design result.

Later stage drawing, data display etc. function, the convenience is looked over at any time with the designer and is used.

The invention provides a series of transmission spectra to achieve the purpose of improving various indexes of human color perception, and the comprehensive characteristics of various spectrum achievements are as follows: the optical wavelength region of 440-.

The three examples shown in fig. 7 represent an important phenomenon for designing the transmission spectrum. The transmittance exhibits a "high, low, high" pattern. (1) In 380-780 nm, two transmission valleys, 440-510-530-610, (2) three peaks to the left of the first valley (peak one), two valleys in the middle (peak two), the right of the second valley (peak three), and (3) the left of peak one and the right of peak three have no significant color perception. However, the left side of the first peak is ultraviolet light, so that the transmittance is low, and the eye is protected, but the transmittance of the nanometer is invisible to the human eye, so that the color sense is not influenced. A trough in transmittance means that the transmittance of at least one nanometer of light wavelengths within the specified range of light wavelengths is less than 50%, or at least 5% less than the transmittance of any single nanometer of light wavelengths outside the specified range of light wavelengths.

10. The design method of the present invention enables brightness measurement (lens brightness) for each transmission spectrum. The measuring method is achieved by calculating a brightness function. For example, the luminance parameter L in the CIELUV color space is a function of Y/Yn. Y and Yn are color matching functions.

11. The human perceived transmitted light intensity is directly calculated from the visible range in the transmitted spectrum.

Wherein, I₀I is the brightness of the incident light, I is the brightness of the transmitted light after passing through the optical device, λ₁To lambda₂M is the spectral power distribution for the visible wavelength range.

FIG. 8 shows a set of smoothing results for the resulting transmission spectra.

Example one

1. The light uses CIE D65.

2. Parameter selection

1) The color sensation parameters are selected from white point displacement and color gamut area, and the maximum white point displacement is 0.01.

2) The optimization goal is to increase the blue-yellow difference with a maximized gamut area.

3) The transmission spectra of a series of hypothetical Optical Devices (OD) were manually input into the design software for the Optical Devices (OD). The input spectrum has a visible range of 380 nm to 780 nm.

4) Parameters such as white point displacement, color gamut and the like are optimized and controlled by applying Euclidean distance in a color space with uniform color perception. The displacement constraints of the white point are:

wherein, WP is a new white point, and WP,0 is an objective white point which is seen by common people with eyes.

When maximizing the gamut area, the objective function is:

since gamut area is based on a derivative of the distance measurement method, these calculations are most suitably based on a color space with a uniform color perception, as well as calculating the white point shift.

5) Within 380-780 nm, 2% is selected as the minimum transmittance in order to ensure certain color information. While a colorant with a maximum of 99% is provided to reduce the fluorescence.

6) Selecting a set of colors of saturation, Munsell

{830,751,670,555,495,432,330,261,202,138,27,1231,1161,1094,1001,929}

7) Selecting soft circle, Munsell color group

{850,794,742,690,635,577,520,456,387,314,248,151,121,83,12,1251,1183,1114, 1049,984}. Wherein the subdued color circle comprises the well-known Farnsworth D15 color set for visual defects.

8) The sensitivity of the L, M and S cones to different visible light waves was represented using the color matching function of the 10Deg standard observer from CIE 1964.

9) The CIELUV color space is used for measuring and drawing the relationship between various visible lights such as color groups, color sensation parameters and the like and human color sensation.

3. Tristimulus values (X, Y, Z) were calculated, representing color in CIELUV.

Wherein,M(λ)is the distribution of the spectral energy that is,MC ₈₅₀is the 850 th munsell color,T(λ)is the transmission spectrum of the optical device that needs to be optimized.

Setting MC₈₅₀The coordinates in CIELUV are<>

The coordinate value for defining the white point position of the naked eye of a normal person is<>

And (4) determining optimization iteration, and calculating optimization in a cycle of one million to one million times.

4. The design enters the optimization phase.

1) The following is an expression of the hessian matrix,fis an objective function for maximizing the color gamut area, and OD is the transmitted light spectrum of the optical device.

Andrespectively 380 nm light wave and 780nm light wave. The hessian matrix in each 40nm wavelength range (i.e. from 380 nm to 780nm, 10 points are taken at 40nm intervals), and then 10 columns of hessian matrix are obtained. For each 33.3% reduction in transmission (i.e., four transmissions of 100%, 66%, 33%, 0%), the hessian matrix was evaluated for 4 times¹⁰=1,048,576。

2）.There are positive and negative numbers. The hessian matrix and eigenvalues (eigenvalue) are automatically calculated using differential equations. The obtained characteristic value is not positive or semi-negative definite, and the automatic judgment optimization is non-convex.

3) Automatic recognition of a 400 nm transmittance resolution of 1% is 100⁴⁰⁰And (5) optimizing the dimension. The macroannealing process is automatically initiated.

4) In the initial number of cycles, 100 wavelengths were selected for transmittance optimization at their wavelengths, and the magnitude of the change was selected by a random function, the change being mostly within-30% to + 40%. This time the current candidate solution is re-selected to ensure that the constraint is met, beyond the constraint transmissivity range of 5% -90%.

5) After a number of cycles, only five of the wavelengths have undergone a change in transmittance, and this new transmission spectrum is then calculated by the subroutine.

6) The subroutine calculates and compares the difference in the total cost (optimization objective) of this new current candidate solution and the existing optimal solution. By calculating the total cost, the new current candidate solution is 5% larger than the existing optimal solution.

7) Based on the change in the target value, the number of cycles, and other parameters, a 68% probability of accepting the current candidate solution is calculated.

8) The algorithm accepts this new provisional solution and uses it for the next calculation round. After 90% of the set number of cycles is completed, the probability of accepting a new higher target solution is reduced to less than 1%.

9) Therefore, when the algorithm finishes all the cyclic calculation requirements, the final transmission spectrum is the optimal solution, and the optimization target and the constraint condition are met. Fig. 7 shows 3 examples of transmission spectra. The transmission spectra for examples 1, 2 and 3 in fig. 7 were calculated using one million, three million and one million cycles, respectively.

The expansion range of the color gamut area is 17% -63%.

The white point shift ranges from 0.002 to 0.0099.

The 850 th color of the munsell pastel group is 10B 5/4, the 794 th color is 5B 5/4, the 742 th color is 10BG 5/4, the 690 th color is 5BG 5/4, the 635 th color is 10G 5/4, the 577 th color is 5G 5/4, the 520 th color is 10GY 5/4, the 456 th color is 5GY 5/4, the 387 th color is 10Y 5/4, the 314 th color is 5Y 5/4, the 248 th color is 10YR 5/4, the 151 th color is 2.5YR 5/4, the 121 th color is 10R 5/4, the 83 th color is 7.5R 5/4, the 12 th color is 2.5R 5/4, the 1251 th color is 10RP 5/4, the 1183 th color is 5RP 5/4, the 1114 th color is 10P 5/4, the 1049 th color is 5P 5/4, and the 984 th color is 10PB 5/4. B is blue, BG is cyan, G is green, GY is greenish yellow, Y is yellow, YR is yellow-red, R is red, RP is magenta, P is violet, and PB is violet blue.

In the present invention, the red group consists of 151 th, 83 th and 1251 th munsell colors. The green group consists of 577,520 th and 456 th munsell colors. The blue group consists of the 850 th, 794 th and 984 th munsell colors. The yellow group consists of the 387,314 and 248 munsell colors.

(III) method for designing optical device containing colorant as effective component

The design method of the invention includes the absorption of light by the colorant and the influence of fluorescence generated by the colorant due to light absorption on the transmission spectrum of the base layer. On this basis, the method of the present invention also establishes a colorant library (i.e., a colorant database) and a series of optimization ways for selecting and correlating the above variables with the overall transmission spectrum (i.e., the final transmission target) of the optical transmission device. The invention also provides an artificial intelligence method for selecting an optimization mode according to the optimization target and the constraint index. Optimization goals and constraints include absorption/transmission spectra, transparency, object thickness, layering, colorant formulation, raw material costs, industrial difficulties, etc.

As shown in FIGS. 10-11, the design method of the present invention gives the model multiple degrees of freedom (i.e., variables). The effective structure of the optically transmissive device can be a single base layer (i.e., a matrix transmissive layer) or can be composed of multiple base layers. In each of the base layers, i.e., the matrix transmission layers, there may be a single colorant or a plurality of colorants present as the effective light-absorbing component. The thickness of each matrix layer can be freely regulated, and the type and concentration of the colorant in each matrix layer can also be independently regulated.

Specifically, the integrated transmitted light (spectrum) of a substrate (nth layer) in the optical device is composed of the integrated incident light transmitted through the substrate (nth layer) and the fluorescence generated by the substrate by absorbing the integrated incident light. The integrated transmitted light of this base layer (nth layer) is incident on the next base layer (n +1 th layer) according to the incident path of the incident light. The incident light and the fluorescence generated by the next base layer (n +1 th layer) due to the absorption of the incident light are linearly added to the incident light by the shape coefficient vector to become the integrated incident light of the base layer (n +1 th layer) thereof. The overall transmission (spectrum) of the multi-layer matrix in the optical transmission device is calculated according to the matrix layers passing through the incident path of the incident light in sequence, and the comprehensive transmission (spectrum) of the final layer is passed through.

In addition, the absorption and transmission of light by each substrate also depends on the type, density, and optical device layer thickness of the colorant or colorant group. For example, the fluorescence of the same layer of colorant a will be absorbed locally by the same layer of colorant B and locally by the next layer of colorant C.

Similarly, the transmission spectrum of the whole lens can be accurately simulated by vector superposition of all the base layers in the lens as shown in FIGS. 10-11. Another characteristic of the model is that the colorant can be screened from a colorant database according to the final overall transmission spectrum result of the lens. The optimization goals and constraint indicators are achieved by selecting colorants or colorant sets in an artificial intelligence manner.

Shape coefficient model

The matrix layer is multi-directional by absorbing the fluorescence generated by the combined incident light. The physical position and geometric characteristics of the substrate that generates fluorescence and the substrate (or human eye) affected by the fluorescence directly affect the intensity of the fluorescence received by the substrate (or human eye) affected by the fluorescence. This fluorescence intensity directly affects the transmission spectrum of the optical device in the manner described above. The fluorescence intensity will affect the color perception.

This effect is expressed in the present application by a shape factor model (view factor). FIG. 12 is a model used in the present invention to describe the fluorescence generated by a lens received by the human eye. The model is expressed and simulated by a shape Factor (F) based on the physical positional relationship and geometric features of the human eye and the lens. In particular the pupil of the eye may approximate the shape of a disc. The pupil geometry in the shape factor can also be estimated with a square disk, since the area of the pupil is small. Since the lens geometry is usually mostly circular or rectangular, the lens geometry can also be estimated using a circular or square disk. Then, the shape coefficient of the fluorescence from the lens to the pupil can be calculated by a shape coefficient model (as shown in fig. 12) by considering the distance between the human eye and the lens. This shape factor is a factor less than 1, such as 0.18.

And determining the fluorescent shape coefficient (view factor) of the colorant according to the relative position and distance between the geometry of the optical device and the human eye and the geometry of the pupil of the human eye. The fluorescence shape factor (view factor) may be an absolute shape factor (ratio of fluorescence of the optical device to light absorbed by the human eye), for example, 10^-5To 0.1; the relative fluorescence shape factor may be based on the shape factor of the light transmitted by the optical device. For example, 0.3 to 0.7 layers.

How this application uses absolute shape coefficients for calculations is described by two examples as follows.

Example one

The absolute shape factor is obtained by calculating the percentage of light waves passing through the lens to the pupil.

1. Assuming that the sunglass lens is circular, radius R₁Is 2 cm.

2. In the low light state with sunglasses, assume the radius R of the pupil of the eye₂Is 2.5 mm.

3. The distance H between the pupil and the sunglasses is 6 mm.

4. In conjunction with the calculation in FIG. 12, the absolute shape factor is F₁₂=0.014。

5. Using this absolute shape factor for fluorescence, the above absolute shape factor F₁₂Based on the fact that light propagates through the lens in the direction of the eye, whereas fluorescence is non-directional, so that the absolute shape factor of the fluorescencef ₁₂= 0.5F ₁₂= 0.007. Therefore, the fluorescence reaching the pupil accounts for 0.007 of all the fluorescence generated by the lens.

6. Since in reality the lens will concentrate the light to the pupil. Assuming a lens radius of 4.5mm, the absolute shape factor f of the fluorescence₁₂=0.023。

Example two

The absolute shape factor is obtained by calculating the percentage of light waves passing through the lens to the underlying lens.

Assume that the two base layers are 0 apart and the shapes are the same size. At this time, the absolute shape factor of the fluorescence is. We therefore define the absolute shape factor of the fluorescence for this case to be 0.5.

The present application can also be calculated by relative fluorescence coefficients.

The relative fluorescence coefficient is based on the absolute shape coefficient of the contrast ambient light and the absolute shape coefficient of the fluorescence generated by the optical device. Relative form factor in the lens application usedWherein R is₁₂Is the relative form factor of the fluorescence light through the optics to the human eye. This is mainly because fluorescence is emitted nondirectionally and ambient light is unidirectional through the optical device to the human eye. This relative form factor can be varied when other means are used, such as mirrors, etc. The relative shape factor and the absolute shape factor are used to more accurately predict the effect of changes in the transmission spectrum of the optical instrument due to fluorescence on human color perception.

Second, design of base layer and colorant of optical transmission device

1. The present invention comprises an electronic database of colorants from which to screen colorants. The available parameters of the colorant are all present therein. Including absorption and fluorescence spectroscopy, quantum yield, excitation, cost, lightfastness, heat resistance, chemical stability (e.g., polymerization stability), vendor, toxicity. The designer can make modifications and restrictions on the data of any colorant or the entire colorant that changes, adds, deletes, keeps secret, must use, must not use, etc.

For example, the molar extinction coefficient and fluorescence spectra of malachite green in water, ethanol and other related solutions are well known. The quantum yield of this colorant was almost 0. This colorant is very low in price but poor in light stability. Its median lethal dose LD₅₀The concentration is 80 mg/Kg. It can react poorly with the other two chemicals a and B. It is an awkward colorant based on its poor stability and related factors. Therefore, in the colorant database, the data for malachite green is as follows:

{ colorant = malachite green, molar extinction = [380:780, { ME } ], fluorescence spectrum = [380:780, { FS } ], quantum yield =0.001, light fastness =1.0, half lethal mass =80, and conflict chemistry = { a, B }, use = N }.

2. The integrated incident light of the substrate is obtained by the linear superposition of the integrated transmitted light of the substrate of the previous substrate (or the light source when the optical transmission device is composed of a single substrate) and the vectors of fluorescence generated by all the colorants in the substrate of the current substrate, and the specific algorithm is as follows:

wherein,the composite incident light of the nth layer matrix;

is the integrated transmitted light of the n-1 layer matrix;

Is fluorescence generated by the n-1 th layer of the substrate absorbing the fluorescence of the n-th layer of the substrate, and has a shape factor ofWherein i is the index of the colorant;is the total number of colorants in the matrix of the nth layer; because of the fact thatSmall enough to be ignored, so that the fluorescence of the n-1 th layer of the substrate due to absorption of the n-th layer of the substrate can be ignoredThe content of the active carbon is not counted a little,

therefore:

3. the total transmitted light in each matrix layer is based on the change in the total incident light of the layer by the plurality of colorants in the matrix of the layer. This change is calculated by the logarithmic addition method of the absorption of light by the individual colorants, the logarithmic addition formula being as follows:

wherein,is the overall transmitted light of the nth layer of matrix,

the transmission spectrum of all colorants of the n-th layer matrix.

This is based on the fact that the individual colorants are homogeneously dissolved in the matrix material in the nth layer of matrix. Other non-uniform melting may be applied in a more complex manner to the design process of the present application.

4. The absorption of light by each colorant in the matrix is optically simulated according to beer-lambert absorption law by the combined incident light of the matrix of the layer and the molar extinction (molar extinction) of this colorant, its density in the matrix, and the thickness of the matrix.

Wherein,

τ_nis the thickness of the n-th layer of the substrate,

is the concentration of colorant i in the matrix of the n layers,

is the molar extinction coefficient of colorant i.

5. The fluorescence generated by each colorant in the matrix is the fluorescence left after the fluorescence generated by absorbing the comprehensive incident light of the matrix of the layer is mutually absorbed by other colorants in the layer, and the optical simulation is carried out by the reduction and the influence generated by the release spectral characteristics of the colorants, the comprehensive incident light intensity, the quantum yield and the physical and geometric characteristics of the matrix of the layer and the human eye.

Thus, the fluorescence at wavelength λ intrinsic to the n-base layer can be expressed as:

wherein,

first order Central moment (first) being the ratio of the remaining fluorescence in the stromal layer n moment arm）；

Is the total number of colorants in the matrix of the nth layer;

is the form factor of the n-th to n + 1-th substrates.

6. The integrated transmission spectrum of the multi-layered matrix is sequentially calculated according to the matrix layers through which the incident light passes (e.g., n layers of transmitted light are n +1 layers of incident light). The integrated transmission spectrum of the entire optical transmission device is the integrated transmission spectrum through the last layer.

Optimization of base layer and coloring agent of optical transmission device

1. An optimized cost function (cost function) of the design spectrum is defined. For example, the difference between the design spectrum and the target spectrum, including the difference function. In one embodiment, the difference function minimization is equal to the optimization cost function, with the design outcome being the optical device spectrum closest to the target spectrum.

Wherein

N is the number of layers of the substrate in the optical device;

TS is an abbreviation for transmission spectrum;

TS_Targetand TS_DesignTarget and designed transmission spectra, respectively;

u is the total number of unique colorants;

SR is the variation vector of two adjacent spectral regions;

j is the number of SR spectral regions;

j is the index of the SR region;

γ₁and gamma₂A cost parameter;

α and β are constants;

a is a colorant usage amount limit;

b is a constant;

η is the number of 1 nanometer unit light waves in the SR spectral region (e.g., SR is 401nm to 405nm, which η is 5);

SP is the index of the target and design.

The optimized cost function defining the design spectrum may also contain any other components, such as reducing the cost of colorants used in the design spectrum, plus the differentiation of the design spectrum from the target spectrum.

γ₁The cost parameter grows as the amount of colorant used exceeds the design limit. Also, the true cost of a colorant, such as a colorant X of $ 5 per gram, can be integrated into the cost parameter as follows:

wherein, P_iIs the price of colorant i per weight;

m_iis the molecular weight of colorant i;

c_i,nis the concentration of colorant i in the matrix n;

V_nis the volume of matrix n.

2. Optical device design goals of single or composite nature are selected for multi-objective optimization, where multi-objective optimization can be performed using scaling or using model search reduction (epsilon) -constraints.

The multi-objective optimization function allows designers to achieve Pareto Optimal (Pareto Optimal) optical device design. For example, the design spectrum minimizes the difference from the target spectrum, reduces costs, and selects good colorants (fastness to light, heat, chemical stability).

Since all the objective functions can be expressed under one system, various objectives can be linked. Such as the combined cost of the colorant, light stability and low toxicity, are expressed in terms of scalar weights w.

TotalCost Function =

For example:

Total Cost Function =

wherein, the Total Cost Function is a comprehensive target (comprehensive Cost);

TS_Targetis the target transmission spectrum;

dye Cost is the true Cost of the colorant;

photostability is Photostability.

The pareto front of an optical device design is constructed by concatenating values of various synthetic optimization objectives resulting from various weights w. This pareto front and related solutions give designers the optical device design options required, for example, for colorant formulations, substrate levels, per substrate layer thicknesses, optical device thickness, light stability, heat resistance, etc.

3. The actual artificial intelligence optimization method comprises the following steps: linear optimization simplistic (simplex), convex optimization interior point (interior point) and secondary gradient (subvariant method), non-convex optimization simulated annealing (Simlational annealing), genetic algorithm (genetic algorithm), dynamic dimension search (dynamic dimensional search), and the like. A hybrid optimization method of artificial intelligence initiation may also be employed. This optimization allows the designer to choose an optimization method that is appropriate for the properties of each parameter. For example, mixed integer programming defines the number of colorant selections, or range of numbers, e.g., less than 8, as integers, while optimizing the concentration of each colorant as an integer.

Non-convex optimization employs a heuristic algorithm. A number of optimization objectives and constraints in this application are non-convex. Such as minimizing the difference between the target and design transmission spectra, and optimally utilizing the colorant parameters to construct the transmission spectra.

When the non-convex optimization is determined, a heuristic algorithm is automatically selected and initiated. There are some lists of heuristics that can be chosen for different targets. In the invention, when a large number of variables are optimized, the design method can automatically use the heuristic algorithm of the macro-dimensional annealing created by the method. The following are some brief descriptions of this method.

1) The complexity of the problem to be optimized by the invention is derived from the following four groups of passes to be optimized: (1) the number of substrate layers, (2) the thickness of each substrate layer, (3) the colorant used in each substrate layer, and (4) the concentration of each colorant in the substrate layers.

2) The range of feasible solutions is large at the beginning of the program and then decreases as the program runs at a desired rate, e.g., directly related to the number of loop solutions.

3) For each variable to be optimized, the search neighborhood is randomly changed by using a probability function, such as normal distribution, so as to construct a new candidate solution. As with the thickness of the stroma layer 16, the current solution is 0.83 mm, to the extent that the candidate solution is the current solution plus a probabilistic thickness difference: the probability of the difference in thickness being less than plus or minus 0.1 mm is 68%, the probability of being less than plus or minus 0.23mm is 95%, and the probability of being less than plus or minus 0.41 mm is 99%.

4) Checking whether the candidate solution satisfies the design constraints, e.g. checking whether the candidate stroma n-thickness satisfies the maximum and minimum thicknesses of the stroma. If the candidate solution exceeds this maximum or minimum constraint boundary, the candidate solution values are reselected to satisfy the relevant design constraints.

5) The candidate solution, including the invariant variables and the changed variables, is evaluated to determine its changes to the optimization objective and to determine whether all constraints are satisfied.

6) And calculating the target value of the candidate solution and adding any cost (penalty function) exceeding the limiting condition into a total cost value. The total cost value difference of the candidate solution and the current solution is compared.

7) If the candidate solution cost is less than the current solution, the candidate solution is accepted as the new current solution and used for the next round of candidate solution and total cost calculation.

8) If the cost of the candidate solution is greater than the current solution, then the candidate solution has a probability of being accepted as the temporary current solution and used for the next round of computation. This probability function will be set by the designer. This probability will typically be relatively large in early cycle calculations. The probability of accepting the temporary current solution decreases as the number of cycles increases, often being 0. The purpose of this is to make the local optimal solution have a probability to jump out of the local optimal solution quickly in the next round of solving to find the global optimal solution.

9) The number of times of the loop solution is a preset value. The algorithm terminates when this number of loop solutions is reached or when the cost variation is less than a threshold.

10) When the computing resources are strong enough, the algorithm can ensure convergence to a global optimal solution. In practice, it can converge quickly to a good solution.

11) The better solution can be stored and used as an initial value of a subsequent optimization sub-function, so that the requirement on computing resources, such as time, can be greatly reduced.

And (4) judging the optimal optimization mode of the artificial intelligence, namely judging whether the optimization and constraint target properties are linear, convex or multi-objective. For example, after the designer has selected and confirmed the optimization and control objectives, the model automatically (including the designer's manual) performs property verification on the optimization objectives and constraints. Wherein the property verification comprises calculating and judging the values of the Hessian Matrix (Hessian Matrix) and the associated feature scalar (eigenvalue). The characterization also includes the use of a rapid gradient descent or gradient ascent method to identify the existence of locally optimal solutions.

And judging the type of the optimization method. Optimization is divided into two major categories, convex optimization and non-convex optimization. For example, linear optimization is a convex optimization method; while non-convex optimization includes optimization objectives (cost functions) and/or constraint functions and the local extrema and global extrema involved therein. Convex optimization has an efficient optimization method, but convex optimization generally has no method for ensuring a global extremum.

Convexity optimization refers to a function that is convex or concave. If the characteristic value of the Hessian matrix of the function is 0 or positive number, the Hessian matrix is a semi-positive matrix; if the eigenvalue is 0 or negative, it is a semi-negative matrix; both the semi-positive matrix (convexity) and the semi-negative matrix (concavity) belong to convexity optimization; however, if any function includes convex and concave portions, the function is non-convex optimized, and its hessian matrix has negative and positive eigenvalues.

One feature of the optimization part of the present invention is to use artificial intelligence method to determine the optimization category. One of the methods is to determine whether the hessian matrix of the optimization problem is a semi-positive definite matrix. The semi-positive hessian matrix means that the optimization problem belongs to the convex optimization category. When a convex optimization property is determined, the optimization type to which it pertains may also be determined. For example, if the hessian matrix is constant over the entire feasibility region, its dependent optimization category is quadratic.

The following is an expression of the hessian matrix,

where f is an optimization objective or constraint function;

c is the colorant concentration;

is the concentration of the colorant and its matrix layer.

For example:

wherein,is a colorant k₁In the matrix layer k₂To (3) is added.

One method for judging whether the Hessian matrix is positive or negative is to determine whether the eigenvalue of the Hessian matrix is 0 or positive. The algorithm in this application specifically approximates the derivative using a difference equation and periodically evaluates the second partial derivative of lambda,

,

wherein Λ is the eigenvalue and d is the number of the hessian matrix eigenvalues.

For a minimization problem, if the Hessian matrix is a semi-positive definite matrix, it means that all its eigenvalues are equalThat means that the optimization for all concentration variables is convex.

Inputting a single spectral target as a constraint index, for example, the spectral target ranges from 460 nm to 500 nm transmittance of 2% to 5%; and a transmission below 80% at 300-800 nm.

And selecting the optimal optimization method by adopting an artificial intelligence mode. Optimization goals and constraints include the number, thickness, and refractive index of the base layers, the type, amount, concentration, and manufacturing cost of the colorants in each base layer, and the thickness, refractive index, total number of colorants, and manufacturing cost of the overall optical device

For example, the optimization cost function:

wherein, TS_TargetIs the target transmission spectrum;

dye Cost is the true Cost of the colorant;

photostability is Photostability.

Limiting function

The above example is a multi-objective optimization for optimizing transmission spectra, colorant cost, and colorant optical stability, where for the number of substrate layers N, each substrate layer thicknessTotal thickness of all matrix layersAnd the concentration of each colorant in the matrix layer defines a maximum-minimum boundary by a limiting function.

When the designer inputs the categories and parameters of the multi-objective optimization and restriction functions, the software automatically calculates the characteristic values of the hessian matrix of the multi-objective optimization function including 0, positive and negative values. The software also automatically calculates the eigenvalue of the hessian matrix of the restriction function, which is 0 because of the nature of its linear function. The software decides that this overall optimization is non-convex based on these eigenvalues, whereupon and due to the optimization parameters, the software initiates the optimization method of macroannealing. One million calculations gave the optimal solution for the algorithm to be the number of substrate layers N =56, 17 different colorants used, 207 different colorant concentrations, 28 different substrate layers and a total lens thickness of 2.51 nm.

4. Drawing and displaying data of the design result. Including, for example, the combined transmission spectrum of the optical device, the transmission spectrum of each of the individual layers and multilayers of the optical device, the absorption and fluorescence spectra of the colorants, and the refractive index of the optical device.

5. Storing the design result. Later stage drawing, functions such as data display, the convenience is looked over at any time with the designer and is utilized.

Fig. 13-15 show three examples of achieving the target spectrum for an optically transmissive device using the design method of the present invention.

Where the solid line is the target transmission spectrum and the different dashed lines are the actual transmission spectra achieved by the different formulations. The colorant database used contains 820 a number of different colorants. A variety of colorants are used in each formulation, including but not limited to cyanine dyes (cyanine), triarylmethane dyes (triarylmethane), coumarins (coumarins), fluorones (e.g., rhodamine), xanthenes (xanthenes), oxazines (oxazines), pyrenes (pyrene), or derivatives thereof. The number of the substrate layers is 1 to 300, and the thickness of each layer is 0.03 to 90 mm; the concentration is 0.02 to 5000 micromoles per liter (umol/L).

The invention provides a series of transmission spectra to achieve the purpose of improving various indexes of human color perception, and the comprehensive characteristics of various spectrum achievements are as follows: the optical wavelength region within 420-510 nm is a relatively low transmission region, the optical wavelength region within 525-625 nm is a relatively low transmission region, or both the optical wavelength regions within 420-510 nm and 525-625 nm are relatively low transmission regions. Other light wavelength regions should have average mid-to-high transmission spectra in the 380-780 nm range. Fig. 13-15 are examples.

A relatively low transmission is a transmission of less than 50% of light wavelengths of at least one nanometer within the specified light wave range, or at least 5% less than the transmission of light wavelengths of any single nanometer outside the specified light wave range.

The white point shift is a parameter representing the color of the optics itself. The blue-yellow color difference, the red-green color difference and the color gamut area are parameters representing the color perceptibility achieved by the human eye with the optical device.

The table of fig. 5 discloses eleven lens examples created with the present invention, listing the lens data against the naked eye in terms of white point shift, blue-yellow aberration, red-green aberration, and color gamut area:

1. the moving range of the white point of the first lens is less than or equal to 0.042, the increasing range of the blue-yellow aberration is less than or equal to 41.6%, the increasing range of the red-green aberration is less than or equal to 36.1%, and the increasing range of the color gamut area is less than or equal to 40.2%. The transmission spectrum of lens one is the transmission spectrum of "example one" depicted in fig. 7.

2. The moving range of the white point of the second lens is less than or equal to 0.038, the increasing range of the blue-yellow aberration is less than or equal to 23.9%, the increasing range of the red-green aberration is less than or equal to 55.3%, and the increasing range of the color gamut area is less than or equal to 54.4%. The transmission spectrum of lens two is the transmission spectrum of "example two" depicted in fig. 7.

3. The white point movement range of the lens III is less than or equal to 0.029, the increase range of the blue-yellow aberration is less than or equal to 31.6%, the increase range of the red-green aberration is less than or equal to 11.7%, and the increase range of the color gamut area is less than or equal to 49.9%. The transmission spectrum of lens three is the transmission spectrum of "example three" depicted in fig. 7.

4. The moving range of the white point of the lens four is less than or equal to 0.032, the increasing range of the blue-yellow color difference is less than or equal to 41.8%, the increasing range of the red-green color difference is less than or equal to-14.8% (namely the reducing range of the red-green color difference is more than or equal to 14.8%), and the increasing range of the color gamut area is less than or equal to 22.4%. The transmission spectrum of lens four is that of the "smooth" example depicted in fig. 8.

5. The moving range of the white point of the lens five is less than or equal to 0.009, the increasing range of the blue-yellow aberration is less than or equal to 7.0%, the increasing range of the red-green aberration is less than or equal to 7.4%, and the increasing range of the color gamut area is less than or equal to 28.7%. The transmission spectrum of lens five is the transmission spectrum of "colorant formulation a" depicted in fig. 13.

6. The moving range of the white point of the lens six is less than or equal to 0.033, the increasing range of the blue-yellow aberration is less than or equal to 19.9%, the increasing range of the red-green aberration is less than or equal to 55.6%, and the increasing range of the color gamut area is less than or equal to 57.5%. The transmission spectrum of lens six is the transmission spectrum of "colorant formulation B" depicted in fig. 13.

7. The moving range of the white point of the lens seven is less than or equal to 0.022, the increasing range of the blue-yellow color difference is less than or equal to 31.6%, the increasing range of the red-green color difference is less than or equal to-7.4% (namely, the reducing range of the red-green color difference is more than or equal to 7.4%), and the increasing range of the color gamut area is less than or equal to 23.8%. The transmission spectrum of lens seven is the transmission spectrum of "colorant formulation a" depicted in fig. 14.

8. The white point moving range of the lens eight is less than or equal to 0.016, the increase range of the blue-yellow aberration is less than or equal to 14.6%, the increase range of the red-green aberration is less than or equal to-1.8% (namely the reduction range of the red-green aberration is more than or equal to 1.8%), and the increase range of the color gamut area is less than or equal to 25.8%. The transmission spectrum of lens eight is the transmission spectrum of "colorant formulation B" depicted in fig. 14.

9. The white point moving range of the lens nine is equal to or less than 0.013, the increase range of the blue-yellow aberration is equal to or less than 11.5%, the increase range of the red-green aberration is equal to or less than 0.8%, and the increase range of the color gamut area is equal to or less than 25.8%. The transmission spectrum of lens nine is the transmission spectrum of "colorant formulation C" depicted in fig. 14.

10. The moving range of the white point of the lens is equal to or less than 0.000, the increasing range of the blue-yellow aberration is equal to or less than 13.2%, the increasing range of the red-green aberration is equal to or less than 85.9%, and the increasing range of the color gamut area is equal to or less than 31.7%. The transmission spectrum of lens ten is the transmission spectrum of "colorant formulation a" depicted in fig. 15.

The moving range of the white point of the lens eleven is less than or equal to 0.014, the increasing range of the blue-yellow color difference is less than or equal to 15.1%, the increasing range of the red-green color difference is less than or equal to-4.7% (namely, the reducing range of the red-green color difference is more than or equal to 4.7%), and the increasing range of the color gamut area is less than or equal to 22.6%. The transmission spectrum of lens eleven is that of "colorant formulation B" depicted in fig. 15.

Claims

1. An optical device for altering human color vision perception, operative to enhance human color vision perception, comprising: the transmission spectrum of the optical device is realized by matching the coloring agent and the concentration thereof, at least one substrate layer is generated on the optical device to contain the coloring agent, and the transmission spectrum is characterized in that: including a transmission spectrum having a relatively low transmittance in any wavelength band from 420 nm to 510 nm, or a transmission spectrum having a relatively low transmittance in any wavelength band from 525 nm to 625 nm, or a transmission spectrum having a relatively low transmittance in any wavelength band in both light wave ranges from 420 nm to 510 nm and from 525 nm to 625 nm.

2. An optical device for altering human color vision perception, operative to enhance human color vision perception, comprising: the transmission spectrum of the optical device is realized by matching the coloring agent and the concentration thereof, at least one substrate layer is generated on the optical device to contain the coloring agent, and the transmission spectrum is characterized in that: including a transmission spectrum having a relatively low transmittance in any of the wavelength bands from 440 nm to 540nm, or a transmission spectrum having a relatively low transmittance in any of the wavelength bands from 556 nm to 626nm, or a transmission spectrum having a relatively low transmittance in any of the two light wave ranges from 440 nm to 540nm and from 556 nm to 626 nm.

3. An optical device for altering human color vision perception, operative to enhance human color vision perception, comprising: the transmission spectrum of the optical device is realized by matching the coloring agent and the concentration thereof, at least one substrate layer is generated on the optical device to contain the coloring agent, and the transmission spectrum is characterized in that: the transmittance exhibits a "high, low, high" mode: in 380 nm to 780nm, there are two transmission valleys, the first valley is located in 440 nm to 510 nm, the second valley is located in 530 nm to 610 nm; there are three transmittance peaks to the left of the first valley, in the middle of the first valley and the second valley, and to the right of the second valley, respectively.

4. An optical device for altering human color perception according to any one of claims 1-3, wherein: the optical device has at least one substrate layer having a thickness of 0.0251 mm to 90 mm.

5. An optical device for altering human color perception according to any one of claims 1-3, wherein: the number of substrate layers of the optical device is 1 to 300.

6. An optical device for altering human color perception according to any one of claims 1-3, wherein: in the CIELUV color space, the white point of the optics is shifted between 0.000 and 0.042 using the CIE D65 illuminant.

7. An optical device for altering human color perception according to any one of claims 1-3, wherein: the blue-yellow difference produced by the optic in CIELUV color space expands less than 41.8% using the CIE D65 illuminant.

8. An optical device for altering human color perception according to any one of claims 1-3, wherein: the red-green color difference produced by the optical device in the CIELUV color space was expanded by less than 85.9% using the CIE D65 illuminant.

9. An optical device for altering human color perception according to any one of claims 1-3, wherein: the color gamut area expansion produced by the optical device in CIELUV color space is less than 63% using CIE D65 illuminant.

10. An optical device for altering human color perception according to any one of claims 1-3, wherein: the variation range of the color gamut area of the optical device uses a Munsell color group as a sample color, including Munsell soft color circles; the munsell soft color circle is composed of 850,794,742,690,635,577,520,456,387,314,248,151,121,83,12,1251,1183,1114, 1049, and 984 munsell colors.

11. An optical device for altering human color perception according to any one of claims 1-3, wherein: the optical device is a transmissive device that produces changes to human vision.

12. An optical device for optically modifying human color perception according to claim 11, wherein: the transmission device comprises lenses, glasses, a screen, a windshield and various windows.

13. The optical device for altering human color perception according to claim 12, wherein: the glasses include contact lenses, sunglasses, presbyopic glasses, myopia glasses, distance glasses, bifocal glasses, trifocal glasses, progressive multifocal glasses, astigmatic glasses, intraocular lenses and plano glasses.

14. The optical device for altering human color perception according to claim 12, wherein: the lenses include camera lenses, intraocular lenses, extraocular lenses, and ocular surface contact lenses.