Gray response calibration method and system for optical system of feature spectral imaging

Abstract

The application discloses a kind of optical system gray scale response calibration method and system for characteristic spectral imaging, the method comprises: in different sky background brightness condition, the gray scale image of characteristic spectrum photographed by optical system is obtained, the image photographed by spectral detection system and the spectral data captured by spectral detection system;Wherein, there is common marker in gray scale image and image;The capture range of the spectral data is determined in the image;Wherein, the capture range is determined by the coordinates of the brightest point in the image;Based on common marker, the area of capture range in gray scale image is determined;The average gray scale value of area is calculated;Linear fitting is carried out to average gray scale value and spectral data, to complete the gray scale response calibration of optical system.The method can directly use the observation angle of observation position in target identification process to calibrate, relative to the existing calibration method, need to consider observation angle, solar zenith angle and other factors, operation is more simple.

Description

Technical Field

[0001] This application relates to the field of relative radiometric calibration technology, and in particular to a method and system for grayscale response calibration of optical systems for characteristic spectral imaging. Background Technology

[0002] Target recognition refers to the process of distinguishing a specific target (or a type of target) from other targets (or other types of targets).

[0003] Among these, the spectrum emitted by a light source or reflected by an object contains a wealth of information and is widely used in the field of target recognition. For example, visible light covers a wide region from 390nm to 780nm, and the distribution of spectral intensity within this wavelength range can reflect the natural attributes of the light source, object, and scene. Therefore, spectral acquisition technology has become an effective means of target recognition in scientific research and engineering applications.

[0004] Furthermore, before acquiring spectral data, it is often necessary to calibrate the optical system in real time. However, existing calibration methods, such as calibrating radiance, need to consider factors such as the observation angle and the solar zenith angle, which are complex to operate and not convenient for real-time calibration of the optical system.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides a grayscale response calibration method and system for optical systems based on characteristic spectra. This method can directly use the observation angle of the observation position during target recognition for calibration, which is simpler to operate compared to existing calibration methods that require consideration of factors such as observation angle and solar zenith angle.

[0007] In a first aspect, this application provides a gray-scale response calibration method for optical systems used in characteristic spectral imaging, the method comprising:

[0008] Under different sky background brightness conditions, grayscale images of characteristic spectra captured by the optical system, images captured by the spectral detection system, and spectral data captured by the spectral detection system are obtained; among them, grayscale images and images contain common markers;

[0009] Determine the capture range in the image to capture spectral data; wherein, the capture range is determined by the coordinates of the brightest point in the image;

[0010] Based on common markers, determine the region in the grayscale image that can be captured;

[0011] Calculate the average gray value of the region;

[0012] Linear fitting is performed on the average gray value and spectral data to complete the gray response calibration of the optical system.

[0013] Optionally, based on shared markers, the region in the grayscale image to be captured is determined, including:

[0014] Obtain the relative positional relationship between common markers in the image and the capture area;

[0015] Based on the relative positional relationship between common markers in the image and the capture area, the region of the capture area in the grayscale image is determined.

[0016] Optionally, acquire grayscale images captured by the optical system, including:

[0017] Acquire a grayscale image of a characteristic spectrum in the range of 390nm to 780nm captured by an optical system.

[0018] Optionally, acquire a grayscale image of a characteristic spectrum in the 390nm to 780nm range captured by the optical system, including:

[0019] Acquire a grayscale image of the characteristic spectrum at 589 nm captured by the optical system.

[0020] Optionally, the spectral data includes the relative spectral intensities of the characteristic spectra.

[0021] Optionally, the capture range is the detection range of the spectrometer in the spectral detection system, or smaller than the detection range of the spectrometer in the spectral detection system.

[0022] Optionally, linear fitting is performed on the average gray value and spectral data to complete the gray response calibration of the optical system, including:

[0023] The least squares method is used to perform linear fitting on the average gray value and spectral data to complete the gray response calibration of the optical system.

[0024] Secondly, this application provides a gray-scale response calibration system for optical systems used in characteristic spectral imaging, the system comprising:

[0025] The acquisition module is used to acquire grayscale images of characteristic spectra captured by the optical system, images captured by the spectral detection system, and spectral data captured by the spectral detection system under different sky background brightness conditions; wherein, the grayscale images and the images contain common markers;

[0026] The capture range determination module determines the capture range of the captured spectral data in the image; wherein, the capture range is determined by the coordinates of the brightest point in the image;

[0027] The region determination module determines the region within the grayscale image that can be captured, based on shared markers.

[0028] The calculation module calculates the average gray value of the region.

[0029] The linear fitting module performs linear fitting on the average gray value and spectral data to complete the gray response calibration of the optical system.

[0030] Thirdly, this application provides a gray-scale response calibration system for optical systems used in characteristic spectral imaging, the system comprising:

[0031] The spectral detection system is used to capture images under different sky background brightness conditions, capture spectral data under different sky background brightness conditions, and determine the capture range of the spectrometer in the image; wherein, the capture range is determined by the coordinates of the brightest point in the image;

[0032] An optical system is used to capture grayscale images of characteristic spectra under different sky background brightness conditions. Based on common markers present in the image and the grayscale image, the region of the capture range in the grayscale image is determined, the average grayscale value of the region is calculated, and the average grayscale value and spectral data are linearly fitted to complete the grayscale response calibration of the optical system.

[0033] Fourthly, this application provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the above-described method for grayscale response calibration of an optical system for characteristic spectral imaging. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0035] Figure 1 A flowchart illustrating a grayscale response calibration method for optical systems used in characteristic spectral imaging, provided in this application embodiment;

[0036] Figure 2 A connection diagram of an optical system grayscale response calibration system for characteristic spectrum imaging provided in this application embodiment;

[0037] Figure 3 A schematic diagram of the structure of an optical system grayscale response calibration system for characteristic spectrum imaging provided in this application embodiment;

[0038] Figure 4This is a schematic diagram of the structure of an optical system grayscale response calibration system for characteristic spectral imaging provided in an embodiment of this application. Detailed Implementation

[0039] To make the above and other features and advantages of this application clearer, the application is further described below with reference to the accompanying drawings. It should be understood that the specific embodiments given herein are for the purpose of explanation to those skilled in the art and are exemplary only, not restrictive.

[0040] In the following description, numerous specific details are set forth to provide a thorough understanding of this application. However, it will be apparent to those skilled in the art that the specific details are not required to practice this application. In other instances, well-known steps or operations have not been described in detail to avoid obscuring this application.

[0041] To facilitate real-time calibration of optical systems, this application provides a grayscale response calibration method for optical systems used in characteristic spectral imaging. This method eliminates the need to consider factors such as observation angle and solar zenith angle, and can calibrate the optical system using the observation angle of the observation position during target recognition.

[0042] The following is combined Figures 1-4 Provide a detailed explanation, such as Figure 1 As shown, this application provides a grayscale response calibration method for optical systems used in characteristic spectral imaging. The method includes:

[0043] Step S11: Under different sky background brightness conditions, acquire grayscale images of characteristic spectra captured by the optical system, images captured by the spectral detection system, and spectral data captured by the spectral detection system; wherein, the grayscale images and images contain common markers.

[0044] Specifically, such as Figure 2 As shown, the optical system is connected to instruments and equipment. A nanoscale filter is installed in front of the camera and connected to a data processing device (such as a laptop computer) to store the captured grayscale images. The spectral detection system includes a beam splitter system that splits the incident light in two, one for imaging by the camera of the spectral detection system and the other for capturing spectral data by the spectrometer. The camera and spectrometer of the spectral detection system are also connected to the data processing device (such as a laptop computer) to store the images and spectral data. Furthermore, to ensure that common markers exist in the images and grayscale images, the optical axes of the optical system and the spectral detection system must be kept as parallel as possible.

[0045] When it is necessary to calibrate the optical system, the observation angle of the observation position during the target recognition process can be used, and the light source in the sky background can be used as the input light source for the optical system and the spectral detection system. At the same time, direct sunlight should be avoided to prevent overexposure of the image.

[0046] Therefore, under different sky background brightness conditions, grayscale images of the characteristic spectrum captured by the optical system, images captured by the spectral detection system, and spectral data captured by the spectral detection system can be acquired, and the captured grayscale images, images, and captured spectral data can be stored in the data processing device. The spectral data includes the relative spectral intensity of the light source. The grayscale image of the characteristic spectrum can be a grayscale image of a characteristic spectrum within the visible light range of 390nm to 780nm; in a specific application example, a grayscale image of the characteristic spectrum at 589nm can be selected. In this application example, the optical system can acquire a grayscale image of the 589nm characteristic spectrum emitted by the NA in the aircraft's exhaust plume, and, combined with the calibration data of the optical system, deduce the spectral intensity of the 589nm characteristic spectrum emitted by the NA in the aircraft's exhaust plume, thus achieving target identification of the aircraft's exhaust plume.

[0047] Step S13: Determine the capture range for capturing spectral data in the image; wherein the capture range is determined by the coordinates of the brightest point in the image.

[0048] During the calibration process, since the range of spectral data captured by the spectrometer in the spectral detection system is smaller than the range of the image captured by the camera in the spectral detection system, it is necessary to determine the range of spectral data captured by the spectrometer in the image. Specifically, the coordinates of the brightest point in the image are found. The coordinates of this brightest point are also the center of the range captured by the spectrometer. Then, based on the field of view of the spectrometer, the range of spectral data captured by the spectrometer in the image can be determined.

[0049] Step S15: Based on common markers, determine the region in the grayscale image that can be captured.

[0050] After determining the spectrometer's capture range in the image, to calibrate the optical system, it's necessary to determine the region of the capture range located in the grayscale image. This can be done using common markers in both the image and the grayscale image. The determination method can be based on the relative positional relationship between the common markers in the image and the capture range. This relative positional relationship can be the relative coordinates of a point on the common marker and the center point of the capture range, or it can be the straight-line distance between the point on the common marker and the center point of the capture range, along with the angle between the line connecting the two points and a fixed direction.

[0051] It should be noted that the above-mentioned capture range can be the range of the spectrometer's field of view, or it can be smaller than the range of the spectrometer's field of view. For example, you can choose a range with the center of the capture range (the brightest point in the image) as the center and a radius of 100 pixels.

[0052] Step S17: Calculate the average gray value of the region.

[0053] After determining the region within the grayscale image based on shared markers, the average grayscale value of the region can be calculated based on the grayscale value of each pixel within the region.

[0054] Step S19: Perform linear fitting on the average gray value and spectral data to complete the gray response calibration of the optical system.

[0055] The least squares method is used to perform linear fitting on the average gray value and spectral data to complete the gray response calibration of the optical system.

[0056] In one specific embodiment, a clear, cloudless daytime sky is selected as the light source, and direct sunlight is avoided to prevent overexposure of the image. Figure 2 As shown, connect the various instruments and equipment.

[0057] The camera with an optical system featuring a 589nm filter in front of the lens was mounted on a tripod, connected to a power source, and a network cable was connected to the computer to acquire grayscale images. The beam splitter system of the spectral detection system was also mounted on a tripod, connected to an optical fiber to the spectrometer, and a data cable was connected to input the images and spectral data into the computer. The beam splitter system of the spectral detection system was placed to one side of the camera in the optical system, ensuring that the optical system and the spectral detection system were as close as possible and that their optical axes were as parallel as possible.

[0058] The camera in the optical system captures a grayscale image of the distant sky background, the camera in the spectral detection system captures an image of the distant sky background, and the spectrometer in the spectral detection system captures spectral data. The coordinates of the highest brightness are found in the image captured by the camera in the spectral detection system, and the range of spectral data captured by the spectrometer in the image is determined.

[0059] Based on the common markers in the grayscale images captured by the optical system and the images captured by the spectral system, the area where the spectrometer captures spectral data is marked in the grayscale image of the optical system. If there are no obvious common markers at a distance, objects such as long poles can be placed at a distance, ensuring that the long poles or other objects are within the imaging areas of both the optical system and the spectral detection system.

[0060] Based on the spectral data collected by the spectrometer under different sky background brightness conditions, the spectral data corresponding to the 589nm characteristic spectrum is obtained; at the same time, based on the grayscale image of the 589nm characteristic spectrum collected by the optical system, the average grayscale value of the marked area in the grayscale image is calculated.

[0061] For the spectral data of the 589nm characteristic spectrum collected under different sky background brightness conditions, the average gray value of the marked area in the grayscale image calculated at the same time is linearly fitted to complete the grayscale response calibration of the optical system in the 589nm characteristic spectrum.

[0062] like Figure 3 As shown, this application provides a grayscale response calibration system for optical systems used in characteristic spectral imaging. The system includes:

[0063] The acquisition module 301 is used to acquire grayscale images of characteristic spectra captured by the optical system, images captured by the spectral detection system, and spectral data captured by the spectral detection system under different sky background brightness conditions; wherein, the grayscale images and the images contain common markers.

[0064] Specifically, such as Figure 2 As shown, the optical system is connected to instruments and equipment. A nanoscale filter is installed in front of the camera and connected to a data processing device (such as a laptop computer) to store the captured grayscale images. The spectral detection system includes a beam splitter system that splits the incident light in two, one for imaging by the camera of the spectral detection system and the other for capturing spectral data by the spectrometer. The camera and spectrometer of the spectral detection system are also connected to the data processing device (such as a laptop computer) to store the images and spectral data. Furthermore, to ensure that common markers exist in the images and grayscale images, the optical axes of the optical system and the spectral detection system must be kept as parallel as possible.

[0065] When it is necessary to calibrate the optical system, the observation angle of the observation position during the target recognition process can be used, and the light source in the sky background can be used as the input light source for the optical system and the spectral detection system. At the same time, direct sunlight should be avoided to prevent overexposure of the image.

[0066] Therefore, under different sky background brightness conditions, the acquisition module 301 can acquire grayscale images of the characteristic spectrum captured by the optical system, images captured by the spectral detection system, and spectral data captured by the spectral detection system, and store the captured grayscale images, images, and captured spectral data in the data processing device. The spectral data includes the relative spectral intensity of the light source. The grayscale image of the characteristic spectrum can be a grayscale image of a characteristic spectrum within the visible light range of 390nm to 780nm; in a specific application example, a grayscale image of the characteristic spectrum at 589nm can be selected. In this application example, the optical system can acquire a grayscale image of the 589nm characteristic spectrum emitted by the NA in the aircraft's exhaust plume, and, combined with the calibration data of the optical system, deduce the spectral intensity of the 589nm characteristic spectrum emitted by the NA in the aircraft's exhaust plume, thus achieving target identification of the aircraft's exhaust plume.

[0067] The capture range determination module 302 determines the capture range of the captured spectral data in the image, wherein the capture range is determined by the coordinates of the brightest point in the image.

[0068] During the calibration process, since the range of spectral data captured by the spectrometer in the spectral detection system is smaller than the range of the image captured by the camera in the spectral detection system, it is necessary to determine the range of spectral data captured by the spectrometer in the image. Specifically, the coordinates of the brightest point in the image are found. The coordinates of this brightest point are also the center of the range captured by the spectrometer. Then, based on the field of view of the spectrometer, the range of spectral data captured by the spectrometer in the image can be determined.

[0069] The region determination module 303 determines the region in the grayscale image that is to be captured based on common markers.

[0070] After determining the spectrometer's capture range in the image, to calibrate the optical system, it's necessary to determine the region of the capture range located in the grayscale image. This can be done using common markers in both the image and the grayscale image. The determination method can be based on the relative positional relationship between the common markers in the image and the capture range. This relative positional relationship can be the relative coordinates of a point on the common marker and the center point of the capture range, or it can be the straight-line distance between the point on the common marker and the center point of the capture range, along with the angle between the line connecting the two points and a fixed direction.

[0071] It should be noted that the above-mentioned capture range can be the range of the spectrometer's field of view, or it can be smaller than the range of the spectrometer's field of view. For example, you can choose a range with the center of the capture range (the brightest point in the image) as the center and a radius of 100 pixels.

[0072] Calculation module 304 calculates the average gray value of the region.

[0073] After determining the region in the grayscale image that can be captured based on common markers, the calculation module 304 can calculate the average grayscale value of the region based on the grayscale value of each pixel in the region.

[0074] The linear fitting module 305 performs linear fitting on the average gray value and spectral data to complete the gray response calibration of the optical system.

[0075] The linear fitting module 305 uses the least squares method to perform linear fitting on the average gray value and spectral data to complete the gray response calibration of the optical system.

[0076] like Figure 4 As shown, this application also provides a grayscale response calibration system for optical systems used in characteristic spectral imaging, the system comprising:

[0077] The spectral detection system 402 is used to capture images under different sky background brightness conditions, capture spectral data under different sky background brightness conditions, and determine the capture range of the spectrometer in the image; wherein, the capture range is determined by the coordinates of the brightest point in the image.

[0078] Optical system 401 is used to capture grayscale images of characteristic spectra under different sky background brightness conditions. Based on common markers existing in the image and the grayscale image, it determines the area in the grayscale image where the capture range is located, calculates the average grayscale value of the area, and performs linear fitting on the average grayscale value and spectral data to complete the grayscale response calibration of the optical system.

[0079] This application provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the above-described method for grayscale response calibration of an optical system for characteristic spectral imaging.

[0080] It should be understood that the specific features, operations, and details described herein with respect to the methods of this application can also be similarly applied to the apparatus and system of this application, or vice versa. Furthermore, each step of the methods of this application described above can be performed by a corresponding component or unit of the apparatus or system of this application.

[0081] It should be understood that the various modules / units of the apparatus of this application can be implemented wholly or partially through software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in the processor of a computer device in hardware or firmware form or independent of the processor, or it can be stored in the memory of a computer device in software form for the processor to call to execute the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module. In one embodiment, a computer device is provided, which includes a memory and a processor. The memory stores computer instructions executable by the processor. When executed by the processor, the computer instructions instruct the processor to perform the steps of the methods of the embodiments of this application. The computer device can be broadly defined as a server, a terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include non-volatile storage media and internal memory. The non-volatile storage media may store an operating system, computer programs, etc. The internal memory provides an environment for the operation of an operating system and computer programs stored in a non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method described in this application.

[0082] This application can be implemented as a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, causes the steps of the methods of embodiments of this application to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed by one or more computer devices or processors in a distributed manner. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0083] Those skilled in the art will understand that the method steps of this application can be performed by a computer program instructing related hardware, such as a computer device or processor. The computer program can be stored in a non-transitory computer-readable storage medium, and its execution causes the steps of this application to be performed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0084] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for calibrating the gray scale response of an optical system for spectral imaging of features, characterized by, The method comprises the following steps: calibrating an optical system by using an observation angle of an observation position in a target recognition process, aligning a sky background, and taking a light source in the sky background as an input light source of the optical system and a spectral detection system. In different sky background brightness conditions, a gray scale image of a characteristic spectrum photographed by the optical system, an image photographed by the spectral detection system, and spectral data captured by the spectral detection system are obtained; wherein the gray scale image and the image have a common marker; A capture range for capturing the spectral data is determined in the image; wherein the capture range is determined by a coordinate of a brightest point in the image, and the capture range is centered on the brightest point in the image; Based on the common marker, a region of the capture range in the gray scale image is determined; An average gray scale value of the region is calculated; The average gray scale value and the spectral data are linearly fitted to complete gray scale response calibration of the optical system, and the spectral data include relative spectral intensity of the light source.

2. The method for optical system grey response calibration for spectral imaging of features according to claim 1, characterized in that, The method comprises the following steps: An relative position relationship between the common marker in the image and the capture range is obtained; Based on the relative position relationship between the common marker in the image and the capture range, the region of the capture range in the gray scale image is determined.

3. The method for optical system grey response calibration for spectral imaging of features according to claim 1, characterized in that, The method comprises the following steps: A gray scale image of a characteristic spectrum in a range of 390 nm to 780 nm photographed by the optical system is obtained.

4. The method for optical system grey response calibration for spectral imaging of features according to claim 3, characterized in that, The method comprises the following steps: A gray scale image of a characteristic spectrum of 589 nm photographed by the optical system is obtained.

5. The method for optical system grey response calibration for spectral imaging of features according to claim 1, wherein, The spectral data include relative spectral intensity of the characteristic spectrum.

6. The method for optical system grey response calibration for spectral imaging of features according to claim 1, wherein, The capture range is a detection range of a spectrometer of the spectral detection system, or is smaller than the detection range of the spectrometer of the spectral detection system.

7. The method for optical system grey response calibration for spectral imaging of features according to claim 1, wherein, The method comprises the following steps: The average gray scale value and the spectral data are linearly fitted by using a least square method to complete the gray scale response calibration of the optical system.

8. An optical system gray scale response calibration system for spectral imaging of features, characterized by, The system is used for calibrating an optical system by using an observation angle of an observation position in a target recognition process, aligning a sky background, and taking a light source in the sky background as an input light source of the optical system and a spectral detection system, and comprises the following steps: The spectral detection system is used for photographing images in different sky background brightness conditions, capturing spectral data in different sky background brightness conditions, and determining a capture range of a spectrometer for capturing the spectral data in the images; wherein the capture range is determined by a coordinate of a brightest point in the image, and the capture range is centered on the brightest point in the image. An optical system for taking gray scale images of feature spectra under different sky background brightness conditions, determining the area of the captured range in the gray scale image based on the images and common markers present in the gray scale images, calculating the average gray scale value of the area, and linearly fitting the average gray scale value and the spectral data, including the relative spectral intensity of the light source, to complete the gray scale response calibration of the optical system.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by a processor to implement the optical system gray scale response calibration method for feature spectral imaging according to any one of claims 1-7.

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