A filter rapid detection system and method thereof

Abstract

This invention provides a rapid filter detection system and method. The system includes a xenon lamp; a filter wheel bracket for supporting and fixing the filter wheel; a filter wheel controller for receiving commands and controlling the rotation of the filter wheel; a motor for providing power to the filter wheel; a filter wheel on which multiple filters are mounted; a lens for focusing and imaging, projecting light through the filters onto a camera; a camera for capturing light passing through the lens and filters and converting it into a digital image; an image workstation for processing image data transmitted from the camera and performing image analysis and processing operations; and a darkroom for housing the xenon lamp, filter wheel bracket, filter wheel, filter wheel controller, motor, filter wheel, lens, and camera. This invention can quickly detect the spectral information of installed filters and quickly determine whether they are installed correctly, thereby improving detection efficiency and meeting the requirements of large-scale detection.

Description

Technical Field

[0001] This invention relates to the field of filter testing technology, specifically to a rapid filter testing system and a rapid filter testing method using this system. Background Technology

[0002] In vivo optical imaging of small animals is the most common imaging method for small animal experiments, including bioluminescence imaging and fluorescent labeling imaging. Bioluminescence imaging technology refers to the oxidation reaction of luciferase protein, produced by the expression of the reporter gene luciferase gene, with its small substrate luciferin in the presence of oxygen and Mg2+ ions, consuming ATP and releasing some of the chemical energy into visible light energy. Images are then formed in vitro using a sensitive CCD device. Bioluminescence is essentially a type of chemical fluorescence. Firefly luciferase releases a wide range of visible light photons during the oxidation of its specific substrate luciferin, with an average wavelength of 560 nm (460–630 nm), including an important red light component with wavelengths exceeding 600 nm. In mammals, hemoglobin is the main component absorbing visible light, absorbing most of the visible light in the blue-green wavelength range. Water and lipids mainly absorb infrared light, but both have poor absorption capacity for red to near-infrared light with wavelengths of 590-800 nm. Therefore, although some red light with wavelengths exceeding 600 nm is scattered and consumed, most of it can penetrate mammalian tissue and be detected by highly sensitive CCDs. Excitation fluorescence imaging works by exciting fluorescent groups to a high-energy state, which then emits light. The principle of excitation fluorescence imaging can be described as follows: When external light shines on biological tissue containing fluorescent groups, the fluorescent groups absorb the light energy, causing electrons to transition to an excited state. As the electrons return from the excited state to the ground state, they release fluorescence. This fluorescence is more red-endpointed than the absorbed light, meaning the emitted fluorescence has lower energy than the absorbed external light. The fluorescence propagates within the tissue, with some reaching the body surface. The fluorescence emitted from the body surface is received by the detector, thus forming a fluorescence image.

[0003] Small animal live imaging equipment mainly consists of a dark chamber, excitation light source, filters, lenses, a high-sensitivity camera, and an image processing workstation. Among these, the filters are a crucial component, typically installed in front of the optical lens or between the lens and the camera. They filter out unwanted optical signals during imaging, resulting in more accurate images. Small animal live imaging equipment uses a set of bandpass filters with a wavelength range of 400nm-900nm. Using the wrong filter during imaging can lead to imaging failure or incorrect image output. To ensure the accuracy of images and data, it is essential to ensure that each filter is installed correctly after installation.

[0004] According to the principle of the three primary colors, all visible light can be composed of red (R), green (G), and blue (B) in appropriate proportions. When using a color camera for imaging, the visible light collected by each sensor can be represented using RGB values. The proportions of the three primary colors (red, green, and blue) differ for different wavelengths of visible light, resulting in different RGB values ​​obtained by the color camera. Based on the principles of spectroscopy and colorimetry, and using the 1931 CIE standard colorimetric system and a backpropagation (BP) neural network, a model can be established to map RGB values ​​to light wavelengths, thus obtaining the light wavelength through the RGB values. Therefore, the RGB values ​​collected by the camera can be input into the RGB-spectral wavelength relationship model to derive the visible light bands captured by the camera and passing through the filter.

[0005] After the filter is installed, its correct installation cannot be checked visually. However, the correct installation can be determined by detecting the spectral information transmitted through the filter to match its model. Currently, the method for detecting filter spectral information involves using a spectrometer to measure the spectral range and characteristics transmitted through the filter. While this method can accurately determine the filter's spectral range, it is cumbersome, slow, and inconvenient for installation and testing, especially after mass production, failing to meet the demand for rapid testing. Summary of the Invention

[0006] To address the various problems existing in the current methods of detecting optical filters, the present invention aims to provide a rapid optical filter detection system and method. This system and method can quickly detect the spectral information of an installed optical filter and quickly determine whether it is installed correctly, thereby improving detection efficiency and meeting the requirements of large-scale detection.

[0007] The present invention achieves the above objectives through the following technical solutions:

[0008] A rapid filter inspection system, comprising:

[0009] The xenon lamp, as a light source, is used to provide illumination for the detection system;

[0010] The filter wheel bracket is used to support and fix the filter wheel to ensure that the filter wheel can rotate smoothly and select the appropriate filter.

[0011] The filter wheel controller receives instructions and controls the rotation of the filter wheel to select the correct filter.

[0012] An electric motor is used to power the filter wheel, enabling it to rotate.

[0013] A filter wheel, on which multiple filters are mounted, is used to selectively transmit or block light of a specific wavelength, so as to achieve automatic switching of the filters;

[0014] A lens is used for focusing and imaging, projecting filtered light onto the camera.

[0015] A camera is used to capture light passing through a lens and filters and convert it into a digital image;

[0016] An image workstation is used to process image data transmitted from a camera and perform image analysis and processing operations.

[0017] The dark box is used to house the xenon lamp, filter wheel bracket, filter wheel, filter wheel controller, motor, filter wheel, lens, and camera, providing a light-free environment to avoid interference from external light on the detection system.

[0018] A rapid detection method for optical filters, comprising the steps described above, using a rapid detection system for optical filters, and including the following steps:

[0019] The image workstation starts up and is ready to receive data; the camera initializes and switches to RGB mode, ready to acquire images.

[0020] Turn on the xenon lamp as the light source, position the filter wheel to the initial position, and send the filter position information to the image workstation;

[0021] Light passing through the filter enters the lens, and the camera captures and outputs RGB image data to the image workstation, which receives and saves the image data and associates it with the location information.

[0022] After acquiring the image of the first filter, the image workstation communicates with the filter wheel controller via serial communication to control the filter wheel to move to the next filter position, repeating the acquisition process until the image data of all filters have been acquired.

[0023] After data acquisition is completed, the image workstation processes the image data acquired from each filter in sequence. Using the mapping relationship model between RGB and spectral wavelength, the acquired RGB values ​​are reconstructed into corresponding spectral information. The obtained spectral information is compared with the spectral information in the database to select filters that meet the requirements. The types and proportions of RGB values ​​of the filters that meet the requirements are statistically analyzed. The statistical analysis results are fitted with the spectral characteristic curve of the filter to confirm the filter model.

[0024] After all filter models have been confirmed, the test results will be compared with the installation information, and the test results will be printed.

[0025] According to the present invention, a rapid detection method for optical filters is provided, which establishes a mapping model between RGB and spectral wavelengths, including:

[0026] Based on the RGB system, three ideal primary colors are selected to replace the actual three primary colors, thereby representing the spectral tristimulus values ​​in the CIE-RGB system. The chromaticity coordinates r, g, and b all become positive values;

[0027] To find the tristimulus values ​​X, Y, Z of a known wavelength, in actual calculations, the summation Σ is used instead of the integral ∫, expressed as the following formula:

[0028]

[0029] Where K is the adjustment factor. S(λ) represents the relative spectral power distribution of a certain light source under test. These are the tristimulus values ​​of the spectrum.

[0030] According to the rapid detection method for filters provided by the present invention, the CIE-XYZ spectral chromaticity coordinates x, y, z are calculated and expressed by the following formula:

[0031]

[0032] Among them, an artificial neural network is used to establish a mapping relationship model between the r, g, and b values ​​and the color tristimulus values ​​(X, Y, Z) defined by the CIE standard colorimetric system.

[0033] According to the fast filter detection method provided by the present invention, when establishing a mapping relationship model using an artificial neural network, the specific steps include:

[0034] Collect r, g, b values ​​and corresponding X, Y, Z tristimulus values ​​as training samples;

[0035] Design an artificial neural network structure, including an input layer, a hidden layer and an output layer. The input layer accepts r, g and b values ​​as input, and the output layer outputs X, Y and Z tristimulus values.

[0036] The artificial neural network is trained using training samples. By adjusting the weights and bias parameters in the network, the network output is made to approximate the real X, Y, Z tristimulus values.

[0037] The trained artificial neural network is tested using validation samples to evaluate its performance; if the test results meet the requirements, the network is then used in actual color conversion applications.

[0038] According to the present invention, a rapid detection method for optical filters includes the following steps when acquiring the spectral characteristic curves of different optical filters:

[0039] The RGB values ​​of all filters are collected, with 10 collections for each filter. Based on this data, the spectral characteristic curve of the filter is fitted. Thus, each filter has a specific spectral characteristic curve.

[0040] The existing filter to be tested is mounted on the filter wheel and irradiated with a xenon lamp. The xenon lamp has a spectral wavelength range of 400nm-1000nm, which includes the continuous visible light band and the near-infrared region I band.

[0041] When the light from the xenon lamp shines on the filter, because the filter is a bandpass filter, only light of a specific wavelength can pass through the filter. The light of that specific wavelength passes through the lens and is then captured by the camera.

[0042] The camera transmits the acquired image data to the image workstation. The camera has two channels: one is an RGB channel, used to acquire visible light spectral information, and the other is an IR channel, used to acquire near-infrared spectral information.

[0043] According to the present invention, a rapid filter detection method is provided, which uses an image workstation to count the RGB values ​​and the proportion of each RGB value in each image data, and reconstructs all spectral bands and the proportion of each band based on the RGB values;

[0044] Record 10 sets of image data, fit the spectral characteristic curve of the filter based on these 10 sets of data, and store it in the database.

[0045] According to the present invention, a rapid filter detection method is provided, wherein comparing the obtained spectral information with spectral information in a database to select filters that meet the requirements includes:

[0046] The obtained spectral information and the spectral information in the database are standardized to ensure that they can be compared on the same scale.

[0047] Use registration algorithms or other alignment methods to align the data so that the peaks and troughs of different spectral data can be aligned.

[0048] The correlation coefficient between the calculated spectral information and the spectral information in the database is set. A threshold is set. When the correlation coefficient is greater than the threshold, the filter is considered to meet the requirements. The closer the correlation coefficient is to 1, the higher the similarity between the two spectral information.

[0049] According to the rapid detection method for optical filters provided by the present invention, the statistical analysis of the types and proportions of RGB values ​​of qualified optical filters includes:

[0050] For each selected filter, extract its corresponding RGB value and remove duplicates from the extracted RGB values;

[0051] As needed, RGB values ​​can be categorized into different color ranges or categories;

[0052] Count the number of RGB values ​​in each color range or category. For each color range or category, calculate the proportion of its RGB values ​​in the total dataset. The proportion is calculated by dividing the number of RGB values ​​in a certain color range or category by the total number of RGB values.

[0053] Use charts to visualize the types of RGB values ​​and their proportions.

[0054] According to the rapid detection method for filters provided by the present invention, when fitting the statistical analysis results with the spectral characteristic curve of the filter, the fitting effect is evaluated by calculating the goodness of fit.

[0055] The fitted spectral characteristic curve is compared with the spectral characteristic curve of a known filter model. This comparison includes whether the reflectance / transmittance at key wavelengths is consistent, as well as the similarity of the overall spectral shape.

[0056] Therefore, compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The system of this invention consists of a xenon lamp, a filter wheel bracket, a lens, a camera, and a computer. It uses RGB values ​​to measure the visible light band of the filter, matches it with existing filter data models to confirm the filter model, and then compares it with the installation information to confirm whether the installation is correct.

[0058] 2. The method provided by this invention can improve the detection efficiency of filters used in small animal live imaging equipment, and can quickly detect whether the installed filters are correctly installed, thus meeting the requirements of batch testing; it also reduces the cost of the testing equipment and improves the automation of the testing equipment.

[0059] 3. This invention utilizes a mapping model between RGB and spectral wavelengths to reconstruct the corresponding spectral information from the collected RGB values, thus improving the accuracy of spectral information reconstruction. By comparing the reconstructed information with spectral information in a database, suitable filters can be accurately selected.

[0060] 4. This invention can detect all filters installed on the filter wheel, ensuring that none are missed. It performs detailed image data acquisition and processing on each filter, ensuring the comprehensiveness of the detection.

[0061] 5. The automated detection of this invention reduces labor and time costs, improves detection efficiency, and the accurate detection results help reduce experimental failures or equipment malfunctions caused by poor filter performance, thereby reducing overall costs.

[0062] 6. This invention compares the test results with the installation information and prints the test results, making the test results more intuitive and easier to understand. This allows users to quickly understand the performance and model of the filter, providing convenience for subsequent use and maintenance.

[0063] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of an embodiment of a rapid filter detection system according to the present invention.

[0065] Figure 2 This is a schematic diagram illustrating the principle of an embodiment of a rapid filter detection system according to the present invention.

[0066] Figure 3 This is a schematic diagram of the filter wheel in an embodiment of a rapid filter detection system of the present invention.

[0067] Figure 4 This is a schematic diagram of the structure of the filter wheel bracket in an embodiment of a rapid filter detection system of the present invention.

[0068] Figure 5 This is a schematic diagram of the spectral characteristics of visible light in an embodiment of a rapid detection method for filters according to the present invention.

[0069] Figure 6 This is a CIE1931 XY chromaticity diagram in an embodiment of a rapid filter detection method of the present invention.

[0070] Figure 7 This is a flowchart of an embodiment of a rapid detection method for optical filters according to the present invention. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0072] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0073] According to the principle of three primary colors, any light visible to the human eye can be created by mixing red, green, and blue monochromatic colors in appropriate proportions. For example... Figure 5 As shown, visible light in each band can be formed by mixing two or three of the three primary colors, and the proportions of the three primary colors are different for each band of visible light.

[0074] The RGB color model is a color standard that uses variations in the red, green, and blue color channels and their superposition to create a wide variety of colors. When using a color camera to capture images, the visible light collected by each sensor can be represented using RGB values.

[0075] An embodiment of a rapid filter detection system

[0076] See Figures 1 to 4 This embodiment provides a rapid filter detection system, comprising:

[0077] The xenon lamp 1, serving as a light source, is used to provide illumination for the detection system;

[0078] Filter wheel bracket 2 is used to support and fix the filter wheel to ensure that the filter wheel can rotate smoothly and select the appropriate filter.

[0079] The filter wheel controller 3 is used to receive instructions and control the rotation of the filter wheel to select the correct filter.

[0080] Motor 4 is used to provide power to the filter wheel, enabling it to rotate;

[0081] The filter wheel 5 is equipped with multiple filters for selectively transmitting or blocking light of a specific wavelength, so as to achieve automatic switching of the filters.

[0082] Lens 6 is used for focusing and imaging, projecting filtered light onto the camera;

[0083] Camera 7 is used to capture light passing through lens 6 and the filter, and convert it into a digital image;

[0084] Image workstation 8 is used to process image data transmitted from camera 7 and perform image analysis and processing operations.

[0085] Dark box 9 is used to house xenon lamp 1, filter wheel bracket 2, filter wheel 5, filter wheel controller 3, motor 4, filter wheel 5, lens 6, and camera 7, providing a light-free environment to avoid interference from external light on the detection system.

[0086] Specifically, the rapid filter inspection system consists of a xenon lamp 11, a filter wheel bracket 2, a filter wheel controller 3, a motor 4, a filter wheel 5, a lens 6, a camera 7, an image workstation 8, an dark box 9, a camera data cable, and a serial communication cable. After the filter is installed, the filter wheel 5 is mounted as follows: Figure 4 The filter wheel bracket 2 shown has 12 mounting positions for the filter wheel 5 used by the small animal live imaging device, accommodating up to 12 filters. Each filter mounting position is numbered from 0 to 11. Position 0 is the initial position; the device will be in position 0 after initialization. After the filters are installed on the filter wheel 5, the filter information for each position is recorded.

[0087] An embodiment of a rapid detection method for optical filters

[0088] See Figures 5 to 7 This invention provides a rapid detection method for optical filters. The method uses the aforementioned rapid detection system for optical filters and includes the following steps:

[0089] Image workstation 8 starts up and is ready to receive data; camera 7 initializes and switches to RGB mode, ready to acquire images.

[0090] Turn on the xenon lamp 1 as the light source, position the filter wheel 5 to the initial position, and send the filter position information to the image workstation 8;

[0091] Light passing through the filter enters the lens 6, is captured by the camera 7 and outputs RGB image data to the image workstation 8, where the image workstation 8 receives and saves the image data and associates it with the location information.

[0092] After completing the acquisition of the first filter image, the image workstation 8 communicates with the filter wheel controller 3 via serial communication to control the filter wheel 5 to move to the next filter position, repeating the acquisition process until the image data of all filters have been acquired.

[0093] After data acquisition is completed, image workstation 8 processes the image data acquired from each filter in turn, and reconstructs the corresponding spectral information from the acquired RGB values ​​using the mapping relationship model between RGB and spectral wavelength. The obtained spectral information is compared with the spectral information in the database to select filters that meet the requirements. The types and proportions of RGB values ​​of the filters that meet the requirements are statistically analyzed. The statistical analysis results are fitted with the spectral characteristic curve of the filter to confirm the filter model.

[0094] After all filter models have been confirmed, the test results will be compared with the installation information, and the test results will be printed.

[0095] In this embodiment, a mapping model between RGB and spectral wavelengths is established, including:

[0096] like Figure 6 As shown, the CIE 1931-XYZ system is based on the RGB system, but uses three ideal primary colors to replace the actual three primary colors, thus representing the spectral tristimulus values ​​in the CIE-RGB system. The chromaticity coordinates r, g, and b all become positive values;

[0097] To find the tristimulus values ​​X, Y, Z of a known wavelength, in actual calculations, the summation Σ is used instead of the integral ∫, expressed as the following formula:

[0098]

[0099] Where K is the adjustment factor. S(λ) represents the relative spectral power distribution of a certain light source under test. These are the tristimulus values ​​of the spectrum.

[0100] In this embodiment, the 1931CIE-XYZ spectral chromaticity coordinates x, y, z are calculated and expressed as follows:

[0101]

[0102] The relationship between the r, g, b values ​​and the color tristimulus values ​​(X, Y, Z) defined by the 1931 CIE standard colorimetric system is generally non-linear. Therefore, a certain non-linear transformation calculation is required to establish a model for conversion. This embodiment uses an artificial neural network to establish a mapping model between the r, g, b values ​​and the color tristimulus values ​​(X, Y, Z) defined by the CIE standard colorimetric system.

[0103] In this embodiment, the process of establishing a mapping relationship model using an artificial neural network specifically includes:

[0104] Collect r, g, b values ​​and corresponding X, Y, Z tristimulus values ​​as training samples;

[0105] Design an artificial neural network structure, including an input layer, a hidden layer and an output layer. The input layer accepts r, g and b values ​​as input, and the output layer outputs X, Y and Z tristimulus values.

[0106] The artificial neural network is trained using training samples. By adjusting the weights and bias parameters in the network, the network output is made to approximate the real X, Y, Z tristimulus values.

[0107] The trained artificial neural network is tested using validation samples to evaluate its performance; if the test results meet the requirements, the network is then used in actual color conversion applications.

[0108] In this embodiment, the process of acquiring the spectral characteristic curves of different filters includes:

[0109] The RGB values ​​of all filters are collected, with 10 collections for each filter. Based on this data, the spectral characteristic curve of the filter is fitted. Thus, each filter has a specific spectral characteristic curve.

[0110] The existing filter to be tested is installed on the filter wheel 5 and irradiated with xenon lamp 1. The spectral wavelength range of xenon lamp 1 is 400nm-1000nm, which includes the continuous visible light band and the near-infrared region I band.

[0111] After the light from the xenon lamp 1 shines on the filter, since the filter is a bandpass filter, only light of a specific wavelength can pass through the filter. The light of the specific wavelength passes through the lens 6 and is then captured by the camera 7.

[0112] Camera 7 transmits the acquired image data to image workstation 8. Camera 7 has two channels: one is an RGB channel, used to acquire spectral information of visible light, and the other is an IR channel, used to acquire spectral information of the near-infrared region.

[0113] The image workstation 8 was used to statistically analyze the RGB values ​​and the proportion of each RGB value in each image data, and to reconstruct all spectral bands and the proportion of each band based on the RGB values.

[0114] Record 10 sets of image data, fit the spectral characteristic curve of the filter based on these 10 sets of data, and store it in the database.

[0115] In this embodiment, after constructing a mapping model between RGB values ​​and spectral information, for a new RGB value input, the trained model is used to reconstruct the spectrum, outputting complete spectral information, including the spectral reflectance or transmittance of each band. Based on the reconstructed spectral information, the proportion of each band is calculated, which can be achieved by dividing the spectral reflectance or transmittance of each band by the sum of the spectral reflectance or transmittance of all bands.

[0116] In this embodiment, the obtained spectral information is compared with the spectral information in the database to select filters that meet the requirements, including:

[0117] The obtained spectral information and the spectral information in the database are standardized to ensure that they are compared on the same scale. The standardization process includes data normalization, noise reduction, and smoothing to improve the accuracy and stability of the comparison.

[0118] Data alignment is achieved using registration algorithms or other alignment methods, ensuring that the peaks and troughs of different spectral data are aligned. Because spectral data may contain offsets or differences, data alignment is necessary to ensure the accuracy of the comparison.

[0119] The correlation coefficient between the calculated spectral information and the spectral information in the database is set. A threshold is set. When the correlation coefficient is greater than the threshold, the filter is considered to meet the requirements. The closer the correlation coefficient is to 1, the higher the similarity between the two spectral information.

[0120] In this embodiment, regarding database updates: the spectral information in the database is updated regularly to ensure the accuracy and reliability of the comparison. When adding or deleting spectral information, standardization and data alignment should be performed again.

[0121] In this embodiment, the statistical analysis of the types and proportions of RGB values ​​of the compliant filters includes:

[0122] For each selected filter, extract its corresponding RGB value and remove duplicates from the extracted RGB values;

[0123] As needed, RGB values ​​can be categorized into different color ranges or categories (such as reds, greens, blues, etc.).

[0124] Count the number of RGB values ​​in each color range or category. For each color range or category, calculate the proportion of its RGB values ​​in the total dataset. The proportion is calculated by dividing the number of RGB values ​​in a certain color range or category by the total number of RGB values.

[0125] Use charts to visualize the types of RGB values ​​and their proportions.

[0126] In this embodiment, when fitting the statistical analysis results to the spectral characteristic curve of the filter, the fitting effect is evaluated by calculating the goodness of fit (such as R² value, mean squared error MSE, etc.). A suitable fitting method is selected based on the characteristics of the data and the fitting requirements. Commonly used fitting methods include least squares method and nonlinear regression.

[0127] The fitted spectral characteristic curve is compared with the spectral characteristic curve of a known filter model. This comparison includes whether the reflectance / transmittance at key wavelengths is consistent, as well as the similarity of the overall spectral shape.

[0128] In addition to spectral characteristics, other factors such as the size, material, and transmittance range of the filter also need to be considered.

[0129] Specifically, the imaging band of the small animal live imaging device is 400nm-900nm, with the visible light band being 400nm-780nm and the near-infrared band being 780nm-900nm. The filters used in the device are between 400nm and 900nm. First, the RGB values ​​of all filters need to be collected, with 10 samples collected for each filter. Based on this data, a spectral characteristic curve of the transmitted light through the filter is fitted. Thus, each filter has a specific spectral characteristic curve. For example... Figure 1 and 2 As shown, the xenon lamp 1, filter wheel 5, lens 6, and camera 7 need to be placed in the dark box 9 to avoid interference from external light. The existing filter to be tested is mounted on filter wheel 5, and the xenon lamp 1 is used for illumination. The spectral wavelength range of xenon lamp 1 is 400nm-1000nm, including the continuous visible light band and the near-infrared I region band. After the light from xenon lamp 1 illuminates the filter, since the filter is a bandpass filter, only light of a specific wavelength can pass through it. This specific wavelength of light passes through lens 6 and is then captured by camera 7. Camera 7 has two channels: one is an RGB channel, which can acquire spectral information of visible light, and the other is an IR channel, which can acquire spectral information of the near-infrared I region. Camera 7 transmits the acquired image data to image workstation 8. Image workstation 8 calculates the RGB values ​​and the proportion of each RGB value in each image data, and reconstructs all spectral bands and the proportion of each band based on the RGB values. The system records 10 sets of image data, fits the spectral characteristic curves of the filter based on these 10 sets of data, and stores them in the database.

[0130] like Figure 3 As shown, Figure 3The filter wheel 5 is used in the small animal live imaging equipment. The filter wheel 5 has 12 positions and an aperture of 50±2mm, allowing for the installation of 12 filters. Each filter position is labeled from 0 to 11. The filters are installed in their designated positions according to production requirements, and the installation information is input into the image workstation 8.

[0131] During testing, filter wheel 5 is mounted on filter wheel bracket 2. The movement of filter wheel 5 is controlled by filter wheel controller 3. It is initially initialized to position 0. Xenon lamp 1 illuminates the filter. After passing through the filter, the light waves reach lens 6 and camera 7. Image workstation 8 controls camera 7 to acquire the image. Camera 7 then transmits the data to image workstation 8. Image workstation 8 saves the received image data and simultaneously controls filter wheel 5 to move to the next filter mounting position, repeating the acquisition process until all filters are acquired. After all filter images have been acquired, image workstation 8 analyzes the image data obtained from each filter. First, the RGB values ​​are reconstructed to reconstruct the corresponding spectral information, and the obtained spectral information is compared with the spectral information in the database to obtain filters that meet the requirements. Then, the types and proportions of RGB values ​​are statistically analyzed and fitted with the spectral characteristic curves of the filters that meet the requirements, thereby confirming the filter model. After all filter models have been confirmed, they are compared with the installation information. If the data matches, the message "Installation Correct!" is output; if the data does not match, the message "Installation Incorrect!" is output, and the information of the incorrectly installed filter is printed out.

[0132] In summary, the system of this invention consists of a xenon lamp 1, a filter wheel bracket 2, a lens 6, a camera 7, and a computer. It uses RGB values ​​to measure the visible light band passing through the filter, matches it with existing filter data models to confirm the filter model, and then compares it with installation information to confirm correct installation. The method provided by this invention can improve the filter detection efficiency of small animal live imaging equipment, quickly detect whether installed filters are correctly installed, meet the requirements of batch testing, reduce the cost of testing equipment, and improve the automation of testing equipment.

[0133] Furthermore, this invention utilizes a mapping model between RGB and spectral wavelengths to reconstruct the corresponding spectral information from the collected RGB values, thereby improving the accuracy of spectral information reconstruction. By comparing this information with spectral information in a database, suitable filters can be precisely selected.

[0134] Furthermore, the present invention can detect all filters installed on the filter wheel 5, ensuring that none are missed, and performs detailed image data acquisition and processing on each filter to ensure comprehensive detection.

[0135] Furthermore, the automated detection of this invention reduces labor and time costs, improves detection efficiency, and the accurate detection results help reduce experimental failures or equipment malfunctions caused by poor filter performance, thereby reducing overall costs.

[0136] Furthermore, this invention compares the test results with the installation information and prints the test results, making the test results more intuitive and easier to understand. This allows users to quickly understand the performance and model of the filter, providing convenience for subsequent use and maintenance.

[0137] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A rapid detection method for optical filters, characterized in that, This method employs a rapid filter detection system for detection, which includes a xenon lamp as a light source to provide illumination for the detection system; and a filter wheel bracket to support and fix the filter wheel to ensure that the filter wheel can rotate smoothly and select a suitable filter. A filter wheel controller receives instructions and controls the rotation of the filter wheel to select the correct filter; a motor provides power to the filter wheel to enable its rotation; the filter wheel is equipped with multiple filters to selectively transmit or block light of specific wavelengths to achieve automatic filter switching. The lens is used for focusing and imaging, projecting light through the filter onto the camera; the camera is used to capture the light passing through the lens and filter and convert it into a digital image; the image workstation is used to process the image data transmitted from the camera and perform image analysis and processing operations; the dark box is used to house the xenon lamp, filter wheel bracket, filter wheel controller, motor, filter wheel, lens and camera, providing a light-free environment to avoid interference from external light on the detection system; The method includes the following steps: The image workstation starts up and prepares to receive data. The camera initializes and switches to RGB mode, ready to acquire images. The xenon lamp is turned on as the light source, the filter wheel is positioned at its initial position, and the filter position information is sent to the image workstation. Light passing through the filter enters the lens, and the camera acquires and outputs RGB image data to the image workstation, which receives and saves the image data, as well as associating it with the position information. After acquiring the image of the first filter, the image workstation communicates with the filter wheel controller via serial communication to control the filter wheel to move to the next filter position, repeating the acquisition process until the image data of all filters has been acquired. After data acquisition is completed, the image workstation processes the image data acquired from each filter in turn, and reconstructs the corresponding spectral information from the acquired RGB values ​​using the mapping relationship model between RGB and spectral wavelength. The obtained spectral information is compared with the spectral information in the database to select the filters that meet the requirements. Statistical analysis was performed on the types and proportions of RGB values ​​for eligible filters. This included extracting the corresponding RGB values ​​for each selected filter and removing duplicates; classifying the RGB values ​​into different color ranges or categories as needed; counting the number of RGB value types in each color range or category; calculating the proportion of RGB values ​​in the total dataset for each color range or category by dividing the number of RGB values ​​in a particular color range or category by the total number of RGB values; and visualizing the types and proportions of RGB values ​​using charts. The statistical analysis results are fitted to the spectral characteristic curve of the filter to confirm the filter model; the fitting effect is evaluated by calculating the goodness of fit; the fitted spectral characteristic curve is compared with the spectral characteristic curve of a known filter model, including whether the reflectance / transmittance at key wavelengths is consistent and the similarity of the overall spectral shape. After all filter models have been confirmed, the test results will be compared with the installation information, and the test results will be printed.

2. The method according to claim 1, characterized in that, Establish a mapping model between RGB and spectral wavelengths, including: Based on the RGB system, three ideal primary colors are selected to replace the actual three primary colors, thereby representing the spectral tristimulus values ​​in the CIE-RGB system. , , The chromaticity coordinates r, g, and b all become positive values; To find the tristimulus values ​​X, Y, Z of a known wavelength, in actual calculations, the summation Σ is used instead of the integral ∫, expressed as the following formula: Where K is the adjustment factor. S(λ) represents the relative spectral power distribution of a certain light source under test. , , These are the tristimulus values ​​of the spectrum.

3. The method according to claim 2, characterized in that: The CIE-XYZ spectral chromaticity coordinates x, y, z are calculated and expressed by the following formula: Among them, an artificial neural network is used to establish a mapping relationship model between the r, g, and b values ​​and the color tristimulus values ​​defined by the CIE standard colorimetric system.

4. The method according to claim 3, characterized in that: When using artificial neural networks to establish mapping relationship models, the specific steps include: Collect r, g, b values ​​and corresponding X, Y, Z tristimulus values ​​as training samples; Design an artificial neural network structure, including an input layer, a hidden layer and an output layer. The input layer accepts r, g and b values ​​as input, and the output layer outputs X, Y and Z tristimulus values. The artificial neural network is trained using training samples. By adjusting the weights and bias parameters in the network, the network output is made to approximate the real X, Y, Z tristimulus values. The trained artificial neural network is tested using validation samples to evaluate its performance; if the test results meet the requirements, the network is then used in actual color conversion applications.

5. The method according to claim 1, characterized in that: When acquiring the spectral characteristic curves of different filters, the following should be included: The RGB values ​​of all filters are collected, with 10 collections for each filter. Based on this data, the spectral characteristic curve of the filter is fitted. Thus, each filter has a specific spectral characteristic curve. The existing filter to be tested is mounted on the filter wheel and irradiated with a xenon lamp. The xenon lamp has a spectral wavelength range of 400nm-1000nm, which includes the continuous visible light band and the near-infrared region I band. When the light from the xenon lamp shines on the filter, because the filter is a bandpass filter, only light of a specific wavelength can pass through the filter. The light of that specific wavelength passes through the lens and is then captured by the camera. The camera transmits the acquired image data to the image workstation. The camera has two channels: one is an RGB channel, used to acquire visible light spectral information, and the other is an IR channel, used to acquire near-infrared spectral information.

6. The method according to claim 5, characterized in that: The image workstation is used to statistically analyze the RGB values ​​and the proportion of each RGB value in each image data, and to reconstruct all spectral bands and the proportion of each band based on the RGB values; Record 10 sets of image data, fit the spectral characteristic curve of the filter based on these 10 sets of data, and store it in the database.

7. The method according to claim 1, characterized in that, The step of comparing the obtained spectral information with the spectral information in the database to select filters that meet the requirements includes: The obtained spectral information and the spectral information in the database are standardized to ensure that they can be compared on the same scale. Use registration algorithms or other alignment methods to align the data so that the peaks and troughs of different spectral data can be aligned. The correlation coefficient between the calculated spectral information and the spectral information in the database is set. A threshold is set. When the correlation coefficient is greater than the threshold, the filter is considered to meet the requirements. The closer the correlation coefficient is to 1, the higher the similarity between the two spectral information.

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