Improved spectral recovery in samples

Abstract

A method has been developed to improve the stability of a color measurement system that uses a matrix transformation method to measure reflectance. The transformation matrix can be obtained by training the raw measurement matrix and the master reflectance matrix. The raw measurement matrix can be stacked with one or more variants of it, where each variant is some random noise added to part or all of the initial raw signal matrix. The same number of master reflectance matrices are also stacked to match the size and sample ordering of the raw measurement matrix. The resulting transformation matrix will be more stable and less sensitive to measurement noise.

Description

[0001] Cross-referencing of related patent applications

[0002] This application claims priority and benefit to U.S. Patent Application No. 17 / 034873, filed September 28, 2020, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to apparatus, systems, and methods for restoring the transmission or reflection properties of samples. Background Technology

[0004] It is often necessary to determine the transmission or reflection characteristics of an object. The color of a sample can be determined by measuring its transmission or reflection characteristics at different wavelengths. For example, it is known to measure light reflected from or transmitted through an object at wavelengths from 400 nm to 700 nm, typically in 10 nm intervals. However, to obtain accurate measurements of the object's spectrum, a color sensor must have a sufficient number of wavelength channels. Sensors with many wavelength channels (typically 31) produce high-accuracy measurements, but suffer from cost and complexity drawbacks. Conversely, sensors with fewer wavelength channels are cheaper and easier to manufacture, but their measurement accuracy is lower compared to sensors with more wavelength channels, resulting in less accurate measurements. Specifically, measurements obtained from a color measuring device with fewer wavelength channels are less accurate than those obtained from a color measuring device with a larger number of wavelength values.

[0005] For example, a 6-channel spectral sensor, such as the AS7262 manufactured by AMS USA in Cupertino, California, can be used to measure reflectance spectra. However, such a device cannot currently obtain full-spectral reflectance in the 400–700 nm range with 10 nm spacing.

[0006] One mechanism for compensating for insufficient measurement accuracy is the use of matrix transformations, as described in the co-owned U.S. Patent Application No. 15 / 934044, which is incorporated herein by reference in its entirety. As described therein, when using a low-precision measurement configuration, a range of known transmission or reflection color standards can be measured, and the full transmission or reflection spectrum of the sample can be recovered by using a matrix transformation. However, the use of such a matrix transformation can sometimes introduce measurement errors, noise, or artifacts, resulting in unsatisfactory recovered spectra.

[0007] There is a desire in the art to reconstruct full-spectrum information from just a few spectral channels. For example, the AMS AS7262 spectral sensor manufactured by AMS AG of Austria has six channels in the visible wavelength range. For instance, in U.S. Patent US10444074B1, "Spectral Reconstruction in a Sample," to Zhiling Xu et al., which is incorporated herein by reference in its entirety, a matrix transformation method with dual illuminators is used to obtain the full 400–700 nm spectrum with 10 nm intervals from six channels.

[0008] A major problem with using matrix transformations to recover spectral information is that the results are highly sensitive to noise. Obtaining a system with improved stability and less sensitivity to noise would be very helpful.

[0009] Similarly, in the co-owned U.S. Patent US10768098B2, "Spectral Reconstruction in a Sample," which is incorporated herein by reference in its entirety, a method for improving the stability of spectral reconstruction is taught; however, this method requires two or more rounds of measurement. In this new disclosure, a novel method is developed that requires only one round of measurement and achieves more stable spectral reconstruction.

[0010] Therefore, what is needed in the art is a system, method, and computer-implemented product that provides a color measurement system, which includes noise reduction capabilities and is not easily affected by noise or is not very sensitive to noise. In another implementation, when using matrix transformation methods related to spectral information restoration, a method is needed to maintain or improve the stability of the color measurement system.

[0011] In addition, it is necessary to address the problem of using matrices to correct the inaccuracies in color value estimation caused by the small number of measurement channels. Summary of the Invention

[0012] In the disclosure provided herein, the apparatus, system, and method describe the reconstruction of spectral data from a low-precision sensor configuration using a calibration matrix generated through a single round of measurements to obtain a set of calibration standards. An additional measurement matrix is ​​then generated by adding random noise to the measurement matrix. The measurement matrix is ​​then concatenated with its variant to produce a calibration matrix that, when used in conjunction with measurement data of the object being analyzed, can reconstruct the object's spectral data with greater precision and accuracy than using the low-precision measurement device alone.

[0013] More specifically, the aforementioned disclosure pertains to a system and method for improving the stability of a color measurement system that uses a matrix transformation method to measure reflectance. The transformation matrix can be obtained by training an original measurement matrix and a principal reflectance matrix. The original measurement matrix can be stacked with one or more variants, where each variant is some random noise added to the initial original signal matrix. An equal number of principal reflectance matrices are also stacked to match the size of the original measurement matrix. The resulting transformation matrix will be more stable and less sensitive to measurement noise.

[0014] In a specific implementation, a color measurement device is provided, comprising one or more evaluation data models trained on measurements obtained from a set of sample colors using a low-channel color measurement device. The trained model is configured to transform the measurements into values ​​produced when the same set of sample colors is measured by a color measurement device with more wavelength channels. In one arrangement, a color sensor is used to obtain at least one measurement of the set of sample colors under the same illuminator. Random noise generated by a computer program or other method is added numerically to the matrix of the initial measurement set to form a perturbation dataset. A set of random numbers can be applied to different units requiring calibration. The amplitude of the random noise is typically very small (e.g., 1%) compared to the signal from the samples. The measurement sets are concatenated to produce a more robust calibration matrix. A reference color measurement device with more wavelength channels is used to measure a set of color references. The measurement matrices obtained from measurements performed from the reference color measurement device are concatenated to match multiple measurement sets obtained by a color measurement device of the calibration standard. Since each row of the measurement matrix represents the result of one sample, random noise can be added to one or more rows of the measurement matrix, rather than adding noise to the measurements of all samples. Therefore, the connection matrix includes the initial measurement matrix and noise-added variants for some measurements. The reference measurement matrix needs to be connected in a similar manner to match the samples in the measurement matrix. Using the connection matrix, a transformation matrix can be derived. Using this transformation matrix, the restored spectral vectors for the new measurement samples can be obtained.

[0015] In another implementation, the transformation value is obtained by multiplying the pseudo-inverse of the device matrix by a matrix containing the values ​​of the main calibration measurement matrix. Here, the device matrix comprises connected first and second measurement matrices, where the first measurement matrix is ​​a set of calibration standards, and the second measurement matrix is ​​a matrix with the same measurement values ​​as the first measurement matrix but with random noise added to each measurement. Attached Figure Description

[0016] The invention is illustrated in the accompanying drawings, which are intended to be exemplary and not restrictive, wherein like reference numerals are intended to refer to like or corresponding parts, and wherein:

[0017] Figure 1A-1BThis invention illustrates apparatus and components that interface with one or more data communication networks according to one or more implementations of this application.

[0018] Figure 2A-2B A flowchart illustrating in detail the steps taken in a configuration of a color measurement system according to an embodiment of this application is shown.

[0019] Figure 3 This diagram illustrates a set of modules that detail the operational functions of a color measurement system according to a configuration based on the present invention.

[0020] Figure 4 This is a chart comparing the reflectance of one configuration of the calibration device in detail.

[0021] Figure 5 This is a chart comparing the reflectance of one configuration of the calibration device in detail. Detailed Implementation

[0022] Through overview and introduction, various embodiments of the apparatuses, systems, and methods described herein are all geared towards color measurement and analysis. The color of a sample or specimen can be determined by measuring its transmission or reflection characteristics at different wavelengths, for example, from 400 nm to 700 nm, at 10 nm intervals. To perform such measurements, a color measurement apparatus with a sufficient number of wavelength channels (e.g., a multi-channel color sensor) is required. However, color sensors with a large number of wavelength channels (e.g., 31 wavelength channels) can be expensive. Color sensors with fewer wavelength channels are more cost-effective, but the resulting color information is often less accurate. Various methods exist that use matrix transformations to provide estimates of information lost due to the limited range of wavelength channels. However, these methods may introduce noise or other artifacts into the measurement and calculation of spectral values, resulting in a reconstructed spectrum that is not entirely commensurate with the actual characteristics of the measured specimen.

[0023] Averaging measurements across a series of measurements using the same calibration standard and generating a transformation matrix based on these values ​​is one method to improve calibration or compensation values ​​used in spectral reconstruction. This apparatus, system, and method improve upon this approach by reconstructing a more accurate representation of the spectrum from sensors with only a few color channels using multiple matrices. In doing so, the apparatus, system, and method described herein overcome and address long-standing technical problems in an innovative and unconventional manner. Some, but not limited to, the apparatus, system, and method provide improved spectral accuracy and offer a mechanism for low-color-channel sensors to replicate the functionality provided by color measurements with a larger number of color channels.

[0024] For example, the apparatus, system, and method described herein provide a measurement device utilizing a transformation matrix or model constructed by connecting at least two measurement matrices under the same calibration standard under the same illuminator. By connecting the calibration measurement matrices, the color measurement device can reconstruct spectral data and generate measurement spectral data about the analyzed sample with higher measurement accuracy than the original measurements obtained from similar low-wavelength channel devices.

[0025] Referring now to the accompanying drawings, wherein similar reference numerals indicate similar elements, Figure 1 illustrates apparatus and components for obtaining color measurement data, interfaced via one or more data communication networks, according to one or more implementations of this application. As shown, Figure 1A A sample 102 is shown being analyzed by a color measuring device 103 or its sensor. Here, sample 102 can be any type or form of physical article having the color or spectral characteristics to be analyzed. In one implementation, sample 102 is a sample of material in production with reflective or transmissive properties. For example, sample 102 is a fabric sample, such as wool or a blended fabric. In another implementation, sample 102 is a sheet of transparent or translucent material. In yet another implementation, sample 102 is an object or item that is part of a larger structure or article, such as a dashboard of a car or a section of a structural wall. For example, sample 102 is a section or part of plaster, carp, building materials, shell, chassis, packaging, or other article.

[0026] Continue to refer to Figure 1A The color sample 102 is placed such that the color sample 102 can be illuminated by at least one (1) illuminator 106A.

[0027] In another implementation, for ease of explanation of the examples provided herein, sample 102 is illuminated by two or more distinct illuminators. In one or more implementations, illuminators 106A and 106B are commercially available light sources. For example, illuminators 106A-B are independent devices configurable to produce light with a specific spectral power distribution. For example, illuminators 106A-B are one or more discrete light-emitting elements, such as LEDs, OLEDs, fluorescent lamps, halogen lamps, xenon lamps, neon lamps, D65 lamps, daylight lamps, mercury lamps, metal halide lamps, HPS lamps, incandescent lamps, or other known or understood light sources. In one arrangement, both illuminators 106A and 106B are broadband LEDs.

[0028] In one or more implementations, the illuminator 106A-B includes a lens, filter, screen, housing, or other element (not shown) used in conjunction with the illuminator of the illuminator 106A-B to direct an illumination beam of a given wavelength to the sample 102.

[0029] In one implementation, the illuminators 106A-B may be operated or configured by an internal processor or other control circuitry. Alternatively, the illuminators 106A-B may be operated or configured by a remote processor or control device having one or more links or connections to the illuminators 106A-B. Figure 1A As shown, the illuminators 106A-B are directly connected to the color measuring device 103.

[0030] For example Figure 1A As shown, illuminators 106A-B are positioned relative to sample 102 and color measuring device 103 to provide a combination of 45 / 0, d / 8, or other illumination / acquisition geometries. However, in the case where sample 102 is a transmissive sample, the orientation of illuminators 106A-B relative to sample 102 and color measuring device 103 is such that the light beam is guided through sample 102 to color measuring device 103.

[0031] Continue to refer to Figure 1A Light reflected from (or transmitted in the case of a transmissive sample) on sample 102 is collected or measured by color measuring device 103. Here, color measuring device 103 can be a color sensor or an image acquisition device. For example, color measuring device 103 is a CMOS (Complementary Metal-Oxide-Semiconductor), CCD (Charge-Coupled Device), colorimeter, spectrophotometer, photodiode array, or other light-sensing device, and any associated hardware, firmware, and software necessary for their operation. In a specific implementation, color measuring device 103 is a 6-channel AMS spectral sensor, such as the AS7262 manufactured by AMS USA in Cupertino, California, USA.

[0032] In a specific implementation, the color measuring device 103 is configured to generate an output signal when light illuminates the color measuring device 103 or its photosensitive portion. As a non-limiting example, the color measuring device 103 is configured to output a signal in response to light reflected from the sample illuminating a photosensor or other sensor element that is a component of or associated with the color measuring device 103. For example, the color measuring device 103 is configured to generate a digital or analog signal corresponding to one or more wavelengths of light reflected from the sample 102 and illuminating a photosensor that is a component of the color measuring device 103. In one or more configurations, the color measuring device 103 is configured to output spectral information, RGB information, or another form representing multi-wavelength data of light reflected from or transmitted through the sample 102.

[0033] In one or more implementations, the color measuring device 103 described herein has fewer than 31 optical, NIR, or other wavelength channels for evaluating a given wavelength range. In another implementation, the color measuring device 103 has fewer than 15 wavelength channels for evaluating a given wavelength range. In a non-limiting example, the color measuring device 103 has six (6) wavelength channels for evaluating a given wavelength range.

[0034] In one non-limiting implementation, the color measuring device 103 is a camera or image recording device integrated into a smartphone, tablet, mobile phone, or other portable computing device. In another embodiment, the color measuring device 103 is an "off-the-shelf" digital camera or webcam that is connected to or communicates with one or more computing devices.

[0035] According to one embodiment, the color measuring device 103 is a standalone device capable of storing local data corresponding to measurements performed on sample 102 in an integrated or removable memory. In an alternative implementation, the color measuring device 103 is configured to transmit one or more measurement results to a remote storage device or processing platform, such as processor 104. In a configuration that invokes remote storage of image data, the color measuring device 103 is equipped with or configured with a network interface or protocol for communication over a network such as the Internet.

[0036] Alternatively, the color measuring device 103 connects to one or more computers or processors, such as processor 104, using standard interfaces such as USB, FireWire, Wi-Fi, Bluetooth, and other wired or wireless communication technologies suitable for transmitting measurement data.

[0037] The output signal generated by the color measuring device 103 is transmitted to one or more processors 104 for evaluation as a function of one or more hardware or software modules. As used herein, the term "module" generally refers to one or more discrete components that contribute to the effectiveness of the system, method, and manner described herein. Modules may include software elements, including but not limited to functions, algorithms, classes, etc. In one arrangement, the software module is stored as software 207 in the memory 205 of the processor 104. Modules also include hardware elements, substantially as described below. In one implementation, the processor 104 is located within the same device as the color measuring device 103. However, in another implementation, the processor 104 is remote or separate from the color measuring device 103.

[0038] In one configuration, processor 104 is configured to generate, calculate, process, output, or otherwise manipulate the output signal generated by color measuring device 103 via one or more software modules.

[0039] In one implementation, processor 104 is a commercially available computing device. For example, processor 104 may be a collection of computers, servers, processors, cloud-based computing elements, microcomputing elements, on-chip computers, home entertainment consoles, media players, set-top boxes, prototype devices, or “hobby” computing elements.

[0040] Furthermore, depending on the specific embodiment, processor 104 may include a single processor, multiple discrete processors, a multi-core processor, or other types of processors known to those skilled in the art. In a specific example, processor 104 executes software code on custom or commercially available hardware of a mobile phone, smartphone, laptop, workstation, or desktop computer configured to receive data or measurements acquired by color measuring device 103, either directly or via a communication link.

[0041] The processor 104 is configured to run a commercially available or custom operating system, such as a Microsoft Windows, Apple OSX, UNIX, or Linux-based operating system, in order to execute instructions or code.

[0042] In one or more implementations, processor 104 is also configured to access various peripheral devices and network interfaces. For example, processor 104 is configured to communicate with one or more remote servers, computers, peripheral devices, or other hardware via the Internet using standard or custom communication protocols and settings (such as TCP / IP).

[0043] Processor 104 may include one or more memory storage devices (memory). Memory is a persistent or non-persistent storage device (e.g., an IC memory element) that, in addition to one or more software modules, operates for storing an operating system. According to one or more embodiments, memory includes one or more volatile and non-volatile memories, such as read-only memory (“ROM”), random access memory (“RAM”), electrically erasable programmable read-only memory (“EEPROM”), phase-change memory (“PCM”), single in-line memory (“SIMM”), dual in-line memory (“DIMM”), or other memory types. As is known to those skilled in the art, such memory can be fixed or removable, for example, by using a removable media card or module. In one or more embodiments, the memory of processor 104 provides storage for application programs and data files. One or more memories provide program code that processor 104 reads and executes upon receiving a start or boot signal.

[0044] Computer memory may also include auxiliary computer memory, such as disk drives or optical disc drives or flash memory, which provide long-term storage of data in a manner similar to persistent storage devices. In one or more embodiments, the memory of processor 104 provides storage for application and data files when needed.

[0045] Processor 104 is configured to store data locally in one or more memory devices. Alternatively, processor 104 is configured to store data such as measurement data or processing results in a local or remotely accessible database 108. The physical structure of database 108 can be implemented as solid-state storage (e.g., ROM), hard disk drive system, RAID, disk array, storage area network (“SAN”), network attached storage (“NAS”), and / or any other suitable system for storing computer data. Furthermore, database 108 may include caches, including database caches and / or network caches. In addition to other systems for data structures and retrieval known to those skilled in the art, database 108 may also programmatically include flat file data storage, relational databases, object-oriented databases, hybrid relational object databases, key-value data storage such as Hadoop or MongoDB. Database 108 includes the necessary hardware and software to enable processor 104 to retrieve and store data within database 108.

[0046] In one implementation, Figure 1A The elements provided are configured to communicate with each other via one or more direct connections, such as via a common bus. Each element is also configured to communicate with other elements via a network connection or interface, such as a local area network (LAN) or data cable connection. In an alternative implementation, the color measuring device 103, processor 104, and database 108 are each connected to a network such as the Internet and are configured to communicate and exchange data using generally known and understood communication protocols.

[0047] In a specific implementation, the processor 104 is a computer, workstation, thin client, or portable computing device, such as an Apple iPad / iPhone® or Android® device or other commercially available mobile electronic device, which is configured to receive data from or output data to the database 108 and / or the color measuring device 103.

[0048] In one arrangement, processor 104 communicates with a local or remote display device 110 to transmit, display, or exchange data. In another arrangement, display device 110 and processor 104 are integrated into a single form factor, such as a color measuring device, which includes an integrated display device. In an alternative configuration, the display device is a remote computing platform, such as a smartphone or computer, configured with software to receive data generated and accessed by processor 104. For example, the processor is configured to send and receive data and instructions from the processor of the remote computing device. The remote computing device 110 includes one or more display devices configured to display data obtained from processor 104. Furthermore, display device 110 is also configured to send instructions to processor 104. For example, in the case where processor 104 and display device are wirelessly linked using a wireless protocol, instructions executed by the processor can be input to the display device. Display device 110 includes one or more associated input devices and / or hardware (not shown) that allow user access to information and to send commands and / or instructions to processor 104 and color measuring device 103. In one or more implementations, the display device 110 may include a screen, monitor, display, LED, LCD or OLED panel, augmented or virtual reality interface, or ink-based electronic display device.

[0049] Those skilled in the art will understand that additional features, such as power supply, power source, power management circuitry, control interface, relay, adapter and / or electronic components for power supply and interconnection, as well as other components for control of startup, are understood and comprehended as being incorporated.

[0050] Now go to Figure 2A and 3 The system operation described herein is an overview, in which processor 104 is configured to implement or evaluate the output of color measurement device 103. See details in the reference section. Figure 2A To obtain measurement results for sample 102 under one or more (e.g., two) illuminators, color measurement of sample 102 is performed under a first illuminator, as shown in illuminator activation step 202. Here, one or more control signals sent by processor 104 or a color measurement device in a specific implementation cause one of the illuminators 106A-B to be activated, thereby sending light with a given SPD to sample 102. In a specific configuration, one or more illumination modules 302 configured as code to execute within processor 104 configure processor 104 to activate the desired light source. In one or more configurations, illuminators 106A-B are broadband light sources or light sources comprising multiple sub-illuminators, each capable of emitting light with a given SPD. Here, one or more sub-modules of illumination module 302 configure processor 104 to select a desired wavelength or light source available for illuminators 106A-B.

[0051] For reference Figure 3 As shown, in one implementation, the user data module 301 is configured to receive selections, operating parameters, control flags, data, or other information required by the user from one or more input devices. For example, the user data module 301 is configured to receive data input by the user from the display device 110. In one implementation, the data may include the required process, program, or parameters to be implemented. The user data module 301 includes hardware and / or software that configures the processor 104 to receive and interpret the user-provided data.

[0052] In response to user data signaling the start of a data acquisition session, the lighting module 302 configures the processor 104 to activate one or more illuminators. In one arrangement, the lighting module 302 may include hardware and / or software that allows the processor 104 to receive instructions for operating one or more illuminators. The lighting module 302 configures the processor 104 to receive and interpret user input sent via the user data module 301. (See reference...) Figure 3 As shown, in one implementation, the lighting module 302 is configured to select illuminators based on user input. The lighting module 302 includes hardware and / or software that configures the processor 104 to provide control signals to one or more illuminator light sources. The lighting module 302 may also include hardware and / or software that allows the processor 104 to receive instructions for operating one or more illuminators. For example, the lighting module 302 configures the processor 104 to receive and interpret user input sent via the user data module 301. For example, upon receiving user input regarding the type, nature, or category of a sample, the lighting module 302 configures the processor 104 to automatically select one or more illuminators from available illuminators to illuminate the sample 102 based on internal rules, algorithms, or lookup tables that associate the sample type with the illuminator type or selection.

[0053] When illuminated by the first illuminator, the light reflected from the sample 102 is directed to the color measuring device 103. In response to the light reflected from the sample illuminating the sensing portion of the color measuring device 103, a signal or output including information about the sample 102 being analyzed is generated. As in measurement acquisition step 204, the output or signal is received by the processor 104.

[0054] Here, the measurement data acquisition module 304 configures the processor 104 to acquire or record the output of the color measurement device 103. The measurement data acquisition module 304 includes hardware and / or software that configures the processor 104 to acquire, store, protect, or create usable data. In one implementation, the output acquired by the processor 104 configured by the measurement data acquisition module 304 consists of a pixel data array, analog signals (or multiple analog signals), digital data streams, data files, serial encoding, binary data, or other suitable information containing information about the light reflected by the sample 102 and received by the color measurement device 103.

[0055] In another implementation, one or more submodules of the measurement data acquisition module 304 configure the processor 104 to convert, format, or otherwise adjust the data received from the color measurement device 103. For example, a submodule of the measurement data acquisition module 304 converts the data from raw binary data into a digital file.

[0056] In a specific implementation, the data acquired by the color measuring device 103 is stored in the memory of the processor 104. Alternatively, data relating to measurements performed on the sample 102 under any of the illuminators 106A-B is stored in a remote database 108 for later retrieval or processing. In yet another implementation, data regarding the specific brand, model, nameplate, and settings of the color measuring device 103 are stored along with the measurement data.

[0057] In another implementation, the characteristics of the illuminators 106A-B are also stored along with the measurement data. For example, the processor 104 is configured to activate the illuminators 106A-B to record the measurement data output by the color measuring device 103 and acquire the attributes of the illuminators 106A-B used. One or more submodules of the measurement data acquisition module 304 configure the processor 104 to retrieve data about the activated illuminators from a lookup table or database of the illuminators 106A-B. Through one or more additional submodules of the measurement data acquisition module 304, the processor 104 is configured to associate the characteristics of a specified illuminator 106A-B with the relevant measurement data.

[0058] As shown in the illuminator deactivation step 206, processor 104 is configured by one or more sub-modules of illumination module 302 to deactivate the illuminator. As described, the described devices, systems, and methods can be operated with a single illuminator. Therefore, starting from illuminator deactivation step 206, the processor can directly proceed to measurement vector generation step 212. However, in an alternative configuration using multiple illuminators, processor 104 is also configured by one or more sub-modules of illumination module 302 to activate additional illuminators, as indicated by the dashed lines pointing to additional illuminator activation step 208. Here, processor 104 is configured by one or more cooperating modules to determine appropriate or desired illuminators 106A-B. For example, user input stored or accessible by user data module 301 configures processor 104 to select a given illuminator 106A-B based on the type of material being analyzed.

[0059] Once the first measurement has been performed under the first illuminator and that illuminator has been deactivated, the second illuminator is activated as in the additional illuminator activation step 208. For example, upon receiving a ready or available flag from the color measuring device, the processor 104 is configured by the lighting module 302 (or its submodule) to activate the second illuminator, as shown in the additional illuminator activation step 208.

[0060] Processor 104 is configured to receive the output of color measuring device 103, which is generated when light reflected by sample 102 illuminates the photosensitive portion of color measuring device 103. When sample 102 is illuminated under a second illuminator, as shown in additional illuminator acquisition step 210, processor 104 is configured by measurement data acquisition module 304 to obtain the output of color measuring device 103 using the second illuminator.

[0061] In one or more specific implementations, additional illuminators are used to acquire additional data relating to sample 102 at different wavelengths. In this arrangement, processor 104 is configured to return to illuminator deactivation step 206 and continue executing additional illuminator acquisition steps 210, for example, where user data module 301 configures processor 104 to acquire measurement data under various illuminators 106A-B that can be used in the system or device described herein. Processor 104 cycles through steps 206-210 until each illuminator 106A-B has illuminated sample 102 and the corresponding data has been acquired and / or stored in local or remote memory 205.

[0062] Using measurement data obtained under at least a first illuminator (either directly from illuminator deactivation step 206 or via additional steps 208-210), as shown in step 212, a measurement vector is generated for the measured (reflectance or transmittance) values ​​of sample 102. For example, processor 104 is configured by measurement vector module 312 to access stored values ​​associated with measurement results obtained under one or more illuminators. Measurement vector module 312 includes hardware and / or software that uses necessary operating parameters to configure processor 104 to generate and / or compute matrices or other mathematical structures. In one or more additional implementations, measurement vector module 312 also configures processor 104 to access and / or store one or more matrices used to generate the restored spectrum of the sample.

[0063] As a non-limiting example, processor 104 is configured to use a measurement vector T = (t1...t2) / (t3) / (t4) / (t5) / (t6) / (t7) / (t8) / (t9) / (t1) / (t1) / (t2) / (t1) / (t2) / (t3 ...3) / (t3) / (t3) / (t3) / (t3) / (t3) / (t3) / (t3) / (t3) / (t3) / (t3) / (t3) / n 1) To generate the restored spectrum of the sample, where t n This is the sensor's raw count of samples on the nth channel obtained from the light or color measurement device 103. A constant value of 1 is added to the end of vector T for offset compensation. For example, measurement vector module 312 configures processor 104 to adjust or update the values ​​obtained by measuring sample 102 under one or more illuminators to introduce compensation values. For ease of explanation and description, the offset compensation value added to measurement vector T in measurement vector generation step 212 has a value of 1. However, it should be understood that in one or more implementations, measurement vector module 312 configures one or more processors 104 to obtain offset compensation values ​​from data storage. For example, a submodule of measurement vector module 312 configures processor 104 to obtain offset compensation values ​​for the measurement vector from a lookup list in another data storage location.

[0064] Continuing with the flowchart in Figure 2, the input vector T is multiplied by the transform value M to obtain the transformed spectrum at each wavelength. In one or more other implementations, as shown in transform matrix access step 214, the transform value M is a matrix obtained from a data store or database. For example, as shown in transform matrix access step 214, the processor 104, configured by transform matrix access module 314, accesses the appropriate transform matrix M from database 108.

[0065] Using the measurement vector T and the accessed transformation matrix M, the spectrum Rconvert of sample 102 can be obtained by evaluating the measurement vector with transformation matrix M according to the following formula:

[0066] (1)

[0067] Where Vp is the restored reflectance of the sample at wavelength p.

[0068] In an alternative implementation, the described system and method are configured to generate a transformation matrix M from one or more calibration standards. For example, the transformation matrix M may be generated before or after measuring sample 102. In one implementation, the transformation matrix M is generated as shown in transformation matrix generation step 216. Here, processor 104 is configured by transformation matrix calculation module 316 to perform the transformation matrix generation process. In the alternative configuration, the process for generating the transformation matrix M is... Figure 2A The color measurement steps outlined herein are separate and independent of its own.

[0069] For example, such as Figure 2B As shown, the process of generating the transformation matrix M can begin as a set of sub-steps of the transformation matrix generation step 216. In an alternative configuration, the transformation matrix M is generated as a separate process, such that the transformation matrix is ​​output to a database or data store for later access from the processor 104 in a process independent of the spectral restoration process.

[0070] continue Figure 2B The process outlined in the flowchart involves generating the transformation matrix M by obtaining the measurement results of a set of calibration standards using a control or master measuring instrument. As a non-limiting example, as shown in master instrument acquisition step 250, the reflectance of at least 12 BCRA sheets is obtained using a master or control instrument. In one or more instances, the master or control instrument is configured with a greater number of measurement channels than the color measuring device 103 used to obtain the measurement vector T. In one implementation, the master or control instrument is an "800 spectrophotometer" manufactured and sold by Datacolor Inc. However, alternative color measuring or spectrophotometers are understandable and perceptible. In one implementation, the master or control spectrophotometer has 31 color measurement channels.

[0071] Continuing this example, the control or master measuring device is used to obtain measurements of 12 BCRA sheets. The measurement results of the BCRA sheets obtained through the master measuring device are used to generate the principal reflectivity matrix R:

[0072] (2)

[0073] Where r m,p It is the reflectivity of film m at wavelength p.

[0074] In one implementation, the value of R is stored in one or more data storage devices. For example, in one or more arrangements, data obtained by a control or master color measuring device is stored in a database or data storage location accessible to the processor 104 or another computing device. In a specific implementation, one or more submodules of the transformation matrix calculation module 316 configure the processor 104 to access the data storage location and store R for later use or access.

[0075] For example Figure 1B and 2B As shown, the BCRA sheet measured using the main instrument in step 250 is also measured using the same or similar color measuring device 103 used for measuring sample 102.

[0076] As another example, the color measurement device 103 is used to measure a BCRA sheet and includes at least one (1) illuminator, under which the BCRA sheet is measured (as in the first illuminator color calibration step 252). In the case of using an additional illuminator, such as a second illuminator, the BCRA sheet is measured under the second illuminator (as in the second illuminator color calibration step 254). As another example, the sensor used for the light measurement device (such as color measurement device 103) includes six sensor channels, using two illuminators to provide 12 channels of sensor results. Therefore, when using... Figure 1B When measuring each BCRA sheet in the configuration shown, the results for each BCRA sheet can be used to generate a calibration response matrix with 12 rows. However, typically, the calibration response matrix can be described by S:

[0077] (3)

[0078] Where S is the sensor response matrix, and s m,n Let m be the sensor reading in channel n. In one or more further implementations, as shown in offset calibration step 256, the transformation matrix calculation module 316 includes one or more submodules that configure a processor (e.g., but not limited to processor 104) to apply an offset compensation value to the end of each row of the sensor response matrix. As specified in Equation 3, the offset compensation value added to the end of the row is a constant value of 1. However, it is understood that the processor (e.g., but not limited to processor 104) is configured to introduce other offset values ​​into the response matrix. Furthermore, although the offset value in Equation 3 is the same offset value used to obtain the measurement vector T, the described method does not necessarily require adding the same value to the end of each row of the corresponding vector and matrix. Therefore, different offset values ​​can be used in the measurement vector T and the response matrix S.

[0079] As shown in step 258 of the transformation matrix generation, the matrices R and S obtained in steps 250-252 (and in some implementations 254) are used to obtain the transformation matrix M according to the following formula:

[0080] (4)

[0081] For example, one or more processors are configured by the transformation matrix calculation module 316 to access the stored values ​​of R and S and generate M according to Equation 4. Once matrix M is generated, it is output for the restoration measurement process (as shown in restoration spectrum calculation step 218) or stored in one or more data storage devices for later use or retrieval. For example, as shown in restoration spectrum calculation step 218, once the transformation matrix M is generated, it can be used to restore the spectrum of a sample under an illuminator using Equation 1.

[0082] It is understandable that, such as Figure 4 As shown in the graph, the restored spectrum using Equation 4 provides a method that allows the reconstruction of reflectance data using calibration data. However, the accuracy of the restored spectrum can still be improved when using the transformation matrix M to obtain new reflectance information for new samples. Figure 4 In the graph, the red star line shows the principal reflectance of several BCRA sheets measured using the master or control instrument (as in step 250). The green circle line shows the restored reflectance of the same BCRA sheet measured using calibration sensor data, and the blue diamond line shows the restored reflectance of the same BCRA sheet measured using new sensor data. Figure 4 As shown in the chart, the new sensor data did not produce the same accurate results as the calibrated sensor data.

[0083] Therefore, to address the instability of the matrix and obtain more accurate sample measurement results, additional data can be added to the calibration dataset. As shown in step 260 of adding the color calibration matrix, a new set of calibration data is added to the sensor response matrix. For example, the transformation matrix calculation module 316 or its submodules are configured to generate a more robust calibration response matrix S2 by including the additional data. This additional data is obtained by using data from the BCRA sheet measured in step 252 (and in some implementations 254) to obtain a second response matrix. For example, the data obtained in step 252 (and in some implementations 254) can be copied and then adjusted to generate an additional matrix for calibration calculations. By increasing the amount of information within the calibration response matrix, the stability of the calibration matrix is ​​improved. Furthermore, by improving the stability of the calibration matrix, more accurate return spectral measurements of the analyzed sample can be obtained without increasing the number of sensor channels or illuminators.

[0084] As an example, a processor (e.g., but not limited to processor 104) is configured by one or more sub-modules of the transformation matrix calculation module 316 to generate a more robust calibration matrix using a standard BRCA chip according to the following formula:

[0085] (5)

[0086] Where S2 is the new, more robust sensor response matrix, s m,n It is the sensor reading of chip m in channel n in the calibration matrix, while x m,n This is the same sensor reading of Sm.n with random noise added to the measurement results obtained in this round of measurements. In one specific implementation, the random noise is added by one or more sub-modules of the transformation matrix calculation module 316. For example, the transformation calculation module 316 is configured to add random noise in the form of random values ​​added to each initial measurement set in order to generate a second disturbed measurement dataset. That is, for each measurement result in the measurement matrix, the corresponding disturbed measurement value can be generated by adding random values ​​to each corresponding measurement result.

[0087] In one or more implementations, the transformation matrix calculation module 316 or its submodules are configured to add random values ​​such that the difference between the measured value and the corresponding disturbed measurement is approximately 1%. However, in one or more implementations, the difference between the measured value in the first measurement set and the corresponding disturbed measurement value can be between 0.05% and 2% of the measured value in the first measurement set.

[0088] In a practical implementation, more than one disturbed measurement set can be generated. In this configuration, the random values ​​added to each disturbed set are different. For example, the first disturbed measurement set will have a first random value for the measurement added to the first measurement set, while the second disturbed measurement set will have a second random value for the measurement added to the first measurement set, such that the first value and the second value are different.

[0089] In another implementation, the disturbed measurement set may consist of only one or more rows from the actual measurement set containing the added random noise. In this case, the reference measurement matrix and a portion thereof need to be concatenated in a similar manner to match the samples in the measurement matrix.

[0090] Here, a processor (such as, but not limited to, processor 104) configures a color measurement device 103 to perform a first measurement of the BCRA sheet under first and second illuminators, and then obtains a second round of measurements by adding random values. As shown in the above equation, the constant value added at the end of each row provides an offset calibration value for the matrix. As shown in offset calibration step 256, the offset value can be selected from the data storage location or other database and added to the end of each row of the robust matrix. To clarify, the robust sensor response matrix S2 described above is generated using measurements from a measurement device with 6 channels and two different illuminators to provide a total of 12 measurement channels for reference. Those skilled in the art will understand that alternative sensors (with more or fewer measurement channels) and illuminators will change the structure of the robust sensor response matrix.

[0091] Now proceeding to transformation matrix generation step 258, transformation matrix calculation module 316 is configured to generate the transformation matrix using a more robust sensor response matrix. However, to provide a stable transformation matrix, the principal reflectivity matrix is ​​updated to match the more robust sensor response matrix. For example, where the more robust sensor response matrix includes two (2) rounds of BCRA sheet measurements, the robust principal reflectivity matrix is ​​defined as follows:

[0092] (6)

[0093] Where R is the principal reflectivity matrix obtained in the step, as shown in equation (2).

[0094] Continuing with transformation matrix generation step 258, using these more robust matrices (R2 and S2), a more robust transformation measurement matrix can be generated according to a revised form of Equation 3. For example, transformation matrix calculation module 316 configures a processor (such as, but not limited to, processor 104) to generate a more robust transformation matrix M according to the following formula:

[0095] (7)

[0096] Once a more robust transformation matrix is ​​generated according to transformation matrix generation step 258, this transformation matrix is ​​provided as input to Equation 1 to calculate the restored spectrum of any sample 102, as in the restored spectrum calculation step 218. For example, sample restoration module 318 configures a processor (such as processor 104) to use the transformation matrix of Equation 7 and the measurement vector obtained in measurement vector generation step 212 to generate the obtained sensor response to the measured sample 102.

[0097] Once the restored spectrum is obtained according to the restored spectrum calculation step 218, the data values ​​related to the spectral restoration process (e.g., the restored spectrum, as well as the measurement matrix and transformation matrix) are output by the processor configured by the output module 320 to one or more output devices, as shown in the output step 220. In one implementation, the restored spectrum is output to the display device 110 of a smartphone or tablet.

[0098] In this configuration, measurements and outputs are performed by one or more processors linked to or connected to the color measuring device 103. The color measuring device 103 is in turn linked to one or more computers or mobile computing platforms. For example, the color measuring device 103 and illuminators 106A-B are housed in a single housing or device. The color measuring device 103 is configured with one or more data links to exchange data with the mobile computer or computing platform. For example, the color measuring device is configured to communicate with the mobile computing device using Bluetooth or other wireless protocols to allow the transmission of acquired data and measurements about sample 102 to the mobile device for further analysis or calculation. The results of such analysis or calculation are displayed on one or more screens or display devices of the mobile computer or mobile computing platform.

[0099] In another implementation, the output module 320 configures the restored spectrum to be output to the display device 110, which is part of the color measurement device 103.

[0100] Those skilled in the art will understand, such as Figure 5 As shown in the chart, while calibration using single-round raw measurement data is unstable, calibration using noise-adjusted single-round raw data handles noise well and recovers the reflectance of the BCRA sheet better than pure single-round results. Therefore, by concatenating repeated measurements with added random noise, the transformation matrix becomes more stable, and the recovered spectrum obtained using vector T becomes more accurate.

[0101] As described herein, the system, method, and apparatus offer improvements to color measurement techniques. Based on the provided features and disclosure, when used in conjunction with multiple light sources, the color sensor can achieve measurement results that surpass those achievable with measurement devices having a limited number of wavelength channels. Furthermore, by using single-round master calibration measurement data and device calibration measurement data, and introducing random noise into the calibration data, a more robust and stable transformation matrix can be achieved. This, in turn, allows for more accurate color spectral reconstruction. This improvement in the field of color measurement is not a conventional or customary approach and is intended to address technical problems existing in the art.

[0102] As another example, the method described herein can be compared with alternative methods for obtaining reconstructed spectral information. Furthermore, the following implementation provides a concrete method for the subject matter described herein. For example, the advantages of the dual-matrix method described herein can be quantified relative to other methods for reconstructing spectral data. For example, a color measurement system is provided in one or more implementations, comprising: at least one illuminator configured to generate a light beam having a spectral power distribution (SPD) at a sample; a color measurement device configured to receive light reflected from the sample by the at least one illuminator at its photosensitive portion; and one or more processors having a memory and configured to receive an output signal from the color measurement device, and to calculate a restored reflectance spectrum of the sample using the output signal generated at least when the sample is irradiated by the at least one illuminator, wherein the restored reflectance spectrum is calculated by generating a sample vector and a vector offset calibration value using data output from the color measurement device and obtaining a product of the sample vector and the calibration value, wherein the calibration value is a matrix calculated using at least one matrix of a connection device calibration matrix and at least one matrix of a connection control calibration matrix, wherein the connection device calibration matrix comprises an unperturbed measurement matrix of measurements made up of a set of calibration standards, and at least one perturbed device measurement matrix, the at least one perturbed device measurement matrix corresponding to at least one row of the unperturbed measurement matrix and having noise added to the unperturbed measurement matrix.

[0103] The system of any of the aforementioned implementations, wherein the connection device calibration matrix includes a matrix offset calibration value located at the end of each row of the respective device calibration matrix.

[0104] The system of any of the aforementioned implementations, wherein the vector offset calibration value included at the end of each row of the corresponding matrix is ​​1.

[0105] In any of the aforementioned implementations, the difference between the measured values ​​of a given element of the undisturbed matrix and the corresponding element of the undisturbed matrix is ​​less than 1% of the measured value.

[0106] In any of the aforementioned implementations, each measurement in at least one row of the undisturbed measurement matrix is ​​noise-added.

[0107] For any of the aforementioned implementations, the system is denoted by the connection matrix S:

[0108] S =

[0109] s m,n It is the sensor reading of chip m in channel n during a round of measurement of multiple calibration chips, x m,n It is random noise added to each measurement. m,n The same sensor readings, where W is a constant.

[0110] The system of any of the aforementioned implementations, where W is greater than or equal to 1.

[0111] In any of the aforementioned implementations, the calibration matrix M is calculated according to the following formula:

[0112]

[0113] Where R corresponds to: , where r m,p It is the reflectance of multiple calibration plates m at wavelength p obtained using the main measuring device.

[0114] The system of any of the aforementioned implementations, wherein the main measuring device has more wavelength channels than the light sensing portion of the color measuring device.

[0115] The system of any of the aforementioned implementations, wherein the master calibration matrix is ​​obtained from a measurement device having a number of wavelength channels less than or equal to about 31 spectral channels.

[0116] In any of the aforementioned implementations, the reflectance spectrum r of the sample is calculated according to the following formula:

[0117]

[0118] Where T is the measurement vector and M is the calibration matrix.

[0119] The system of any of the aforementioned implementations further includes a remote computing device configured to communicate with the color measuring device and receive the calculated restored reflectance spectrum of the sample.

[0120] A method for identifying the color characteristics of a sample, the method comprising:

[0121] The sample measurement values ​​of the sample being analyzed are acquired using a sample color sensing device under at least one illuminator.

[0122] At least one processor with memory and configured to execute code is used to generate restored color values ​​of the samples using at least one sample measurement and calibration value, wherein the calibration value is a matrix derived using at least one matrix connecting calibration matrices and one matrix connecting a master calibration matrix, wherein each matrix connecting calibration matrices includes a first measurement matrix of measurements of a plurality of reference pieces under at least one illuminator, and a second measurement matrix generated by adding random noise to each element of at least one row of the first measurement matrix, wherein each connected calibration matrix includes a constant value representing offset calibration; and

[0123] Output at least the calculated color attributes.

[0124] The restored color value of the sample is calculated according to the following formula:

[0125]

[0126] Where T is the sample measurement vector of the obtained sample measurement, and M is the calibration matrix.

[0127] In any of the aforementioned implementations, the connection matrix is ​​obtained by acquiring measurements of a first measurement matrix under at least one illuminator using a calibrated color sensor having the same number of wavelength channels as the sample color sensor, and generating a second measurement matrix by adding a constant value to at least one row of the first measurement matrix.

[0128] In any of the aforementioned implementations, the calibration value M is derived according to the following formula:

[0129]

[0130] Here, S is a matrix of multiple connection calibration matrices, and R is a matrix of multiple connection master calibration matrices.

[0131] In any of the aforementioned implementations, the generation of restored color values ​​also includes the master calibration value in the processor remote access memory.

[0132] While this specification contains numerous specific details of embodiments, these details should not be construed as limiting the scope of any embodiment or the content that may be claimed, but rather as descriptions of features that may be specific to particular embodiments. Certain features described in separate embodiment contexts within this specification may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment context may also be implemented separately in multiple embodiments or in any suitable sub-combination. Furthermore, although features may be described above as functioning in certain combinations, or even initially claimed in this way, in some cases one or more features from a claimed combination may be removed from the combination, and the claimed combination may be for sub-combinations or variations thereof.

[0133] Similarly, although operations are described in a specific order in the accompanying drawings, this should not be construed as requiring such operations to be performed in the specific order or sequence shown, or requiring all of the operations shown to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the above embodiments should not be construed as requiring such separation in all embodiments; rather, it should be understood that the described program components and systems can generally be integrated into a single software product or packaged into multiple software products.

[0134] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that, when used in this specification, the terms “comprising” and / or “including” specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0135] It should be noted that the use of ordinal terms such as "first," "second," and "third" to modify claim elements in claims does not imply any priority, rank, or order of one claim element relative to another, nor does it imply a chronological order of the execution of method actions. Rather, they serve merely as labels to distinguish one claim element with a specific name from another element with the same name (except for the use of ordinal terms). Furthermore, the phrases and terms used herein are for descriptive purposes only and should not be considered limiting. The use of "comprising," "including," or "having," "containing," "involving," and variations thereof is intended to include items listed herein and their equivalents, as well as additional items.

[0136] Specific embodiments of the subject matter described in this specification have been described. Other embodiments are within the scope of the appended claims. For example, the actions recited in the claims can be performed in different orders and still achieve the desired results. As an example, the processes described in the drawings do not necessarily require the specific order or sequence shown to achieve the desired results. In some embodiments, multitasking and parallel processing can be advantageous.

[0137] Publications and references representing known registered trademarks of the various systems cited in this application are incorporated herein by reference. Reference to any of the foregoing publications or documents does not constitute an admission that any of the foregoing content is relevant prior art, nor does it constitute any admission of the content or dates of such publications or documents. All references cited herein are incorporated by reference to the same extent that each individual publication and reference is expressly and individually indicated to be incorporated by reference.

[0138] While the invention has been specifically shown and described with reference to its preferred embodiments, those skilled in the art will understand that various changes in form and detail may be made therein without departing from the spirit and scope of the invention. Therefore, the invention is not defined by the discussion that has appeared above, but by the appended claims, the corresponding features referenced in those claims, and their equivalents.

Claims

1. A color measurement system, comprising: At least one illuminator is configured to generate a beam with a spectral power distribution (SPD) at the sample. A color measuring device is configured to receive light generated by the at least one illuminator and reflected by the sample on its light-sensing portion; as well as One or more processors, having a memory and configured to receive an output signal from the color measuring device, and to calculate the restored reflectance spectrum of the sample using the output signal generated at least when the sample is illuminated by the at least one illuminator, wherein the restored reflectance spectrum is calculated by generating a sample vector and a vector offset calibration value using data output from the color measuring device and obtaining the product of the sample vector and the calibration value. The calibration value is a matrix calculated using at least one matrix of a connection device calibration matrix and at least one matrix of a connection control calibration matrix, wherein the connection device calibration matrix includes an unperturbed measurement matrix of measurements made from a set of calibration standards, and at least one perturbed device measurement matrix, the at least one perturbed device measurement matrix corresponding to at least one row of the unperturbed measurement matrix and having noise added to the unperturbed measurement matrix.

2. The system of claim 1, wherein the connection device calibration matrix includes a matrix offset calibration value located at the end of each row of the respective connection device calibration matrix.

3. The system of claim 2, wherein the vector offset calibration value included at the end of each row of the corresponding connection device calibration matrix is ​​1.

4. The system of claim 1, wherein the difference between the measured value of a given element of the undisturbed measurement matrix and the measured value of the corresponding element of the disturbed device measurement matrix is ​​less than 1% of the measured value.

5. The system of claim 3, wherein each measurement value of at least one row of the undisturbed measurement matrix is ​​added with noise.

6. The system of claim 2, wherein the connection device calibration matrix is ​​represented by S. S = s m,n It is the sensor reading of chip m in channel n during a round of measurement of multiple calibration chips, x m,n It is random noise added to each measurement. m,n The same sensor readings, and W is a constant.

7. The system of claim 6, wherein W is greater than or equal to 1.

8. The system of claim 5, wherein the calibration matrix M is calculated according to the following formula: in, R corresponds to: , where r m,p It is the reflectance of multiple calibration plates m at wavelength p obtained using the main measuring device.

9. The system of claim 8, wherein the main measuring device has more wavelength channels than the light-sensing portion of the color measuring device.

10. The system of claim 8, wherein the calibration matrix R is obtained from a measuring device having a number of spectral channels less than or equal to 31.

11. The system according to claim 7, wherein the reflectance spectrum of the sample is calculated according to the following formula: in, T is the measurement vector, and M is the calibration matrix.

12. The system of claim 1 further includes a remote computing device configured to communicate with the color measuring device and receive the calculated restored reflectance spectrum of the sample.

13. A method for identifying the color characteristics of a sample, the method comprising: The sample measurement values ​​of the sample being analyzed are acquired using a sample color sensing device under at least one illuminator. At least one processor with memory and configured to execute code is used to generate restored color values ​​of samples using at least one sample measurement and calibration value, wherein the calibration value is a matrix derived using at least one matrix of multiple connected calibration matrices and one matrix of multiple connected master calibration matrices, wherein each of the multiple connected calibration matrices includes a first measurement matrix of measurements of multiple reference pieces under the at least one illuminator, and a second measurement matrix generated by adding random noise to each element of at least one row of the measured first measurement matrix, wherein each of the connected calibration matrices includes a constant value representing offset calibration; as well as Output at least the calculated color attributes.

14. The method of claim 13, wherein the restored color value of the sample is calculated according to the following formula: , Where T is the sample measurement vector of the obtained sample measurement, and M is the calibration matrix.

15. The method of claim 13, wherein, The connection calibration matrix is ​​obtained by using a calibration color sensing device with the same number of wavelength channels as the sample color sensing device to acquire the measurement values ​​of the first measurement matrix under the at least one illuminator, and generating a second measurement matrix by adding a constant value to at least one row of the first measurement matrix.

16. The method of claim 14, wherein the calibration value M is derived according to the following formula: ， in, S is a matrix of multiple connection calibration matrices, and R is a matrix of multiple connection master calibration matrices.

17. The method of claim 15, wherein generating the restored color value further includes the master calibration value of the processor remote access memory.

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