A venous imaging apparatus and a control method of a venous imaging apparatus

Abstract

The application discloses a vein imaging device and a control method thereof. The vein imaging device comprises a near-infrared light source module, a first polarizer, an image sensor, a second polarizer, an inertial measurement unit, a processing unit and a projection module, wherein the polarizing directions of the first and second polarizers are orthogonal. The processing unit calculates a predicted displacement according to motion posture data of the inertial measurement unit and system delay, and performs a reverse geometric transformation on the extracted vein vessel features to generate a compensated projection image for the projection module to project. The application filters out surface noise through orthogonal polarized optical design, improves the imaging signal-to-noise ratio, and solves the projection misalignment problem caused by device movement and system delay through active motion compensation based on posture prediction, realizing clear, stable and real-time alignment of the vessel projection.

Description

Technical Field

[0001] This application relates to the field of medical auxiliary equipment technology, and more specifically, to a vein imaging device based on near-infrared optical imaging and projection technology and a control method for the vein imaging device. Background Technology

[0002] A vein imaging device is a medical auxiliary device used to display the distribution of superficial subcutaneous blood vessels in real time and non-invasively. It typically utilizes the characteristic that near-infrared light has a high absorption rate for hemoglobin. By collecting infrared light reflected from the skin tissue, the image of the blood vessels is projected back onto the skin surface after image processing to assist medical staff in performing procedures such as vein puncture.

[0003] However, existing handheld imaging technology faces a contradiction in spatiotemporal precision: the conflict between the high computational demands for deep vascular enhancement and the zero-latency requirement for handheld projection. To obtain clear vascular images in complex skin environments such as hairy areas and dark skin, complex ISP noise reduction and enhancement algorithms must be employed. This inevitably leads to a significant increase in system latency (Tdelay), causing the projected image to lag behind hand movements (ghosting or misalignment). Conversely, simplifying the algorithm to pursue real-time performance fails to effectively filter out specular reflection noise from the skin surface, resulting in blurred blood vessels. Simple hardware upgrades cannot overcome this physical limitation because hand tremors (typically at 5-10Hz) have extremely low tolerance for latency (<35ms). Therefore, a system architecture that can physically eliminate the impact of time delay on spatial positioning while retaining the computational power required for complex image processing is urgently needed.

[0004] Secondly, there is the issue of interference from surface noise on the skin. Hair, scars, wrinkles, or oil reflections on the patient's skin can create high-contrast specular reflections. When captured by the image sensor, these reflected lights generate high-frequency noise, interfering with the effective identification of scattered light from deep veins, thus reducing the signal-to-noise ratio and image clarity. Although some devices attempt to remove this noise using purely software algorithms (such as filtering and morphological processing), this not only significantly increases the computational load on the processing unit, further exacerbating projection delay, but also has limited effectiveness in handling complex situations such as dense hair.

[0005] Therefore, how to effectively suppress motion artifacts, reduce projection delay, and reduce surface noise interference from the source to provide clear, stable, and real-time aligned vascular projection images is a technical problem that urgently needs to be solved in the field of vein imaging technology. Summary of the Invention

[0006] The purpose of this application is to provide a vein imaging device and a control method for the vein imaging device, so as to solve the technical problems in the prior art of misalignment between the projected image and the actual blood vessel position caused by device movement and low imaging signal-to-noise ratio caused by light reflected from the skin surface.

[0007] To achieve the above objectives, this application provides a vein imaging device, comprising: a near-infrared light source module for emitting near-infrared light; a first polarizer disposed in the optical path of the near-infrared light source module; an image sensor for acquiring the near-infrared light reflected by skin tissue to generate an image frame; a second polarizer disposed in the optical path of the image sensor, wherein the polarization direction of the second polarizer is orthogonal to the polarization direction of the first polarizer; an inertial measurement unit for acquiring motion posture data of the vein imaging device in real time; and a processing unit electrically connected to the image sensor and the inertial measurement unit, wherein the processing unit is configured to: extract vein features based on the image frame acquired by the image sensor; and calculate... The system calculates the predicted displacement of the vein imaging device; performs a geometric transformation opposite to the predicted displacement on the extracted vein features to generate a compensated projection image; and includes a projection module electrically connected to the processing unit for projecting the compensated projection image onto the skin tissue surface. The processing unit is configured to use a Kalman filter algorithm to calculate the predicted displacement. Specifically, the processing unit is configured to: use the angular velocity and linear acceleration data output by the inertial measurement unit as input to the Kalman filter algorithm; use the displacement and rotation angle of the vein imaging device in three-dimensional space as a state vector; and iteratively calculate the predicted value of the state vector at the end of the system delay time period through the state prediction step of the Kalman filter algorithm, thereby obtaining the predicted displacement.

[0008] To achieve the above objectives, this application also provides a control method for a vein imaging device, comprising the following steps: emitting near-infrared light polarized by a first polarizer through a near-infrared light source module; acquiring the near-infrared light reflected by skin tissue and filtered by a second polarizer through an image sensor to generate an image frame, wherein the polarization direction of the second polarizer is orthogonal to the polarization direction of the first polarizer; acquiring the motion posture data of the vein imaging device in real time through an inertial measurement unit; extracting vein features based on the image frame; calculating the predicted displacement of the vein imaging device according to the motion posture data and a preset system delay; performing a geometric transformation opposite to the predicted displacement on the extracted vein features to generate a compensated projection image; and projecting the compensated projection image onto the surface of the skin tissue through a projection module.

[0009] The technical solution provided in this application, by setting mutually orthogonal first and second polarizers on the optical path, utilizes the physical principle that specular reflection light from the skin surface maintains polarization while diffuse reflection light from subcutaneous tissue undergoes depolarization. This effectively filters out noise interference from the skin surface and hair at the optical level, significantly improving the signal-to-noise ratio of the input image. Simultaneously, by integrating an inertial measurement unit to acquire the device's motion attitude in real time, and combining this with a preset system delay, the processing unit can predict the device's displacement within the delay time and perform pre-defined, inverse geometric transformation compensation on the projected image. This attitude prediction-based active correction strategy effectively counteracts projection misalignment caused by device movement and system delay, ensuring that the projected image is locked onto the actual blood vessel location in real time and accurately. The FPGA-based hardware acceleration architecture guarantees extremely low latency throughout the entire "acquisition-processing-compensation-projection" chain, providing a smooth, ghosting-free visual experience. In summary, this application solves the two major technical pain points of motion artifacts and surface noise, significantly improving the positioning accuracy and imaging quality of vein imaging devices in dynamic scenes. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a system structure block diagram of the vein imaging device provided in an embodiment of the present invention.

[0012] Figure 2 This is a flowchart of the control method for a vein imaging device provided in an embodiment of the present invention.

[0013] Figure 3 This is a schematic diagram of the motion compensation data processing flow provided in an embodiment of the present invention.

[0014] Figure 4 This is a schematic diagram of model naming provided in an embodiment of the present invention.

[0015] Figure 5 This is a schematic diagram of the appearance of the vein imaging device provided in an embodiment of the present invention.

[0016] Figure 6 This is an exploded view of the internal structure of the vein imaging device provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0019] Example

[0020] Please see Figure 1 This is a system structure block diagram of the vein imaging device provided in the embodiments of this application. The vein imaging device (100) adopts a hardware and software co-design to achieve high signal-to-noise ratio imaging and low-delay motion compensation.

[0021] In this embodiment, the vein imaging device (100) includes a near-infrared light source module (101), a first polarizer (102), an image sensor (103), a second polarizer (104), an inertial measurement unit (IMU) (105), a processing unit (106), and a projection module (107).

[0022] Specifically, the optical imaging and projection section employs a coaxial optical path design. For example... Figure 1 As shown, the system may include a beam splitter prism to achieve conjugation of the imaging optical axis and the projection optical axis, ensuring that the image acquisition field of view and the projection field of view are precisely coincident.

[0023] The near-infrared light source module (101) is used to emit near-infrared light of a specific wavelength, which can penetrate skin tissue and be effectively absorbed by deoxyhemoglobin in the blood. In a preferred embodiment, the near-infrared light source module (101) may employ 12 high-power light-emitting diodes (LEDs) in a ring array, the center wavelength of the emitted near-infrared light being in the range of 800 nm to 950 nm, for example, preferably 850 nm ± 10 nm.

[0024] The first polarizer (102) is disposed in the optical path of the near-infrared light source module (101) to polarize the emitted near-infrared light. The image sensor (103), such as a complementary metal-oxide-semiconductor (CMOS) sensor, is used to collect near-infrared light reflected by skin tissue to generate a digital image frame. In this embodiment, the image sensor (103) can be an industrial-grade Sony IMX291 sensor, which has the characteristics of high sensitivity and low noise. A second polarizer (104) is disposed in the optical path of the image sensor (103) to cooperate with the imaging lens.

[0025] Crucially, the polarization direction of the second polarizer (104) is orthogonal to the polarization direction of the first polarizer (102). Preferably, both the first polarizer (102) and the second polarizer (104) are linear polarizers. For example, the transmission direction of the first polarizer (102) (polarizer) can be set to 0°, while the transmission direction of the second polarizer (104) (analyzer) can be set to 90°. The working principle is that when polarized light emitted from the light source shines on the skin surface, the specular reflection light directly generated by the epidermis or hair still maintains its original polarization state, and is therefore blocked by the second polarizer (104) which is orthogonal to it. The light that penetrates into the subcutaneous tissue changes its polarization state (i.e., depolarizes) after multiple scatterings by blood vessels and surrounding tissues, and some of the depolarized light can reach the image sensor (103) through the second polarizer (104). This orthogonal polarization extinction design effectively filters out most surface noise at the physical level, thereby significantly improving the contrast of diffuse reflected light carrying effective vascular information and providing high-quality raw data for subsequent image processing.

[0026] The inertial measurement unit (IMU) (105) is used to acquire motion posture data of the vein imaging device (100) in real time. To accurately capture minute vibrations and rapid movements of the device, the IMU (105) is preferably a six-axis inertial measurement unit, such as the MPU6050 sensor, which can simultaneously provide three-axis angular velocity data. and triaxial acceleration data The IMU (105) is rigidly fixed inside the device, such as on the backplate of the imaging module, to ensure that its measurement data accurately reflects the motion state of the entire device. Its sampling frequency can be set to a high value, such as 1 kHz, to ensure the real-time performance of the motion data.

[0027] The processing unit (106) is the core of the entire system and is electrically connected to the image sensor (103) and the IMU (105). To achieve low-latency processing for tasks such as image processing, motion data fusion, and projection control, the processing unit (106) is preferably a field-programmable gate array (FPGA), such as the Xilinx Spartan-7 series chip. FPGAs have parallel processing capabilities and can be used to build dedicated image signal processing (ISP) pipelines and algorithm acceleration cores.

[0028] The projection module (107) is electrically connected to the processing unit (106) for projecting the processed vascular image back onto the skin surface in real time. To ensure projection quality and response speed, the projection module (107) can be a digital light processing (DLP) projection module, such as an optomechanical system based on the TI DLP2010 digital micromirror device (DMD). The FPGA can directly drive the DLP module, minimizing data transmission latency.

[0029] In addition, the system may include a microcontroller unit (MCU) (108), such as an STM32H7 series chip. This MCU (108) is electrically connected to a processing unit (106), primarily responsible for handling low-speed peripheral interactions and system management tasks, such as responding to key input signals (for switching projection colors, sizes, brightness, working modes, etc.), executing power management strategies (such as entering or exiting sleep mode), and overlaying OSD menus. This heterogeneous architecture of FPGA+MCU achieves a reasonable division of tasks, ensuring high performance of the core image processing link. The system also includes a power supply module for powering each module, and a DDR memory module for caching image frame data.

[0030] Please combine Figure 2 and Figure 3 The control method of the vein imaging device based on the above hardware structure will be described in detail below. Figure 2 Here is the overall flowchart of the method. Figure 3 This is a schematic diagram of motion compensation data processing.

[0031] The method includes the following steps:

[0032] Step S201: Near-infrared light polarized by the first polarizer (102) is emitted by the near-infrared light source module (101), and the light signal reflected by the skin tissue and filtered by the orthogonal second polarizer (104) is collected by the image sensor (103) to generate an image frame.

[0033] Step S202: At the same time, the motion posture data of the vein imaging device (100) is acquired in real time through the IMU (105), namely the high-frequency sampled angular velocity and linear acceleration data.

[0034] Step S203: The processing unit (106) executes a series of image processing algorithms to extract vein features based on the acquired image frames. This process can be efficiently completed in the ISP pipeline within the FPGA. Preprocessing of the original image, such as Gaussian filtering smoothing and contrast-limited adaptive histogram equalization (CLAHE), is performed to enhance local contrast. In a specific implementation, the tile grid size of the CLAHE algorithm can be set to... The cropping limit can be set to 2.0. To achieve enhanced functions such as "hair removal," a morphological top-hat transformation combined with Hessian matrix feature analysis can be used. The calculation logic of the top-hat transformation can be expressed by the following formula:

[0035]

[0036] in, This indicates the image after preprocessing and enhancement. This refers to a structural element, such as a disk structural element with a radius of 3 pixels. This represents the morphological opening operation. This operation effectively removes slowly varying brightness unevenness and fine hair noise from the background. A local adaptive thresholding method, such as the NiBlack method, is used to binarize the processed image to segment out clear vein features. The threshold calculation logic of the NiBlack method can be expressed as:

[0037]

[0038] in, It is a pixel. The segmentation threshold, and These are the mean and standard deviation of the gray level in the neighborhood of that point, respectively. It is an adjustable coefficient, which can be set to -0.2 in this embodiment.

[0039] Step S204: The processing unit (106) calculates the predicted displacement of the vein imaging device based on the motion posture data output by the IMU (105) and the preset system delay. The system delay refers to the time difference between the moment the image sensor completes the exposure of a frame and the moment the projection module begins to project the processing result corresponding to that frame. This delay... This is an inherent property of the system and can be pre-calibrated experimentally, for example, approximately 35 milliseconds. To accurately predict the device's performance... For the displacement within a time period, the processing unit (106) is configured to employ a Kalman filter algorithm. For example... Figure 3As shown, the algorithm uses the angular velocity and linear acceleration data output by the IMU(105) as the measurement input, and the displacement and rotation angle (attitude) of the vein imaging device in three-dimensional space as the state vector. Through the state prediction step of the Kalman filter, the state vector is iteratively calculated. The predicted value of the state vector at the end of the time period is used to obtain the predicted displacement containing translation and rotation components. Furthermore, to optimize filtering performance, the state transition matrix of the Kalman filter algorithm... Based on system latency The kinematic model of the equipment is pre-defined. Process noise covariance matrix. and measurement noise covariance matrix It is based on offline calibration and optimization using a large amount of empirical motion data from handheld device operation to adapt to typical user handshake models.

[0040] Step S205: The processing unit (106) performs a geometric transformation on the vein features extracted in step S203, which is equal in magnitude but opposite in direction to the predicted displacement calculated in step S204, to generate a compensated projection image. Specifically, this geometric transformation is preferably an affine transformation. The processing unit (106) calculates the translation component contained in the predicted displacement. , and rotational components First, construct an affine transformation matrix. Then, apply this matrix to each pixel in the vein feature image to calculate its position in the new coordinate system. For any pixel coordinate in the vein feature... Its new coordinates after affine transformation It can be obtained through the following matrix operations:

[0041]

[0042] In this embodiment, the geometric transformation step essentially establishes a 'spatio-temporal remapping' interlocking mechanism. The displacement (Δx, Δy, Δθ) predicted by the processing unit using Kalman filtering is not merely a geometric translation; it serves as a temporal bridge connecting the 'past' (image acquisition time t0) and the 'future' (photon projection time t0 + Tdelay). By constructing an affine transformation matrix, the system forcibly maps the pixel coordinate system of the 'old image frame' stored in memory to the 'new physical coordinate system' that the device will arrive in 35ms. This mechanism ensures that although the content of the projected image originates from the 'past,' its spatial location is strictly anchored to the 'future.' The IMU's motion data here becomes an absolute prerequisite for activating the spatial validity of vein images—without this predictive transformation, any high-resolution vascular image will lose its clinical value in dynamic scenes due to misalignment.

[0043] Step S206: The compensated projection image is projected onto the skin tissue surface through the projection module (107). Thanks to the FPGA-implemented hardware acceleration link from image acquisition, ISP, vein extraction, motion compensation to DLP driving, the end-to-end latency of the entire process can be strictly controlled within 35 milliseconds, ensuring excellent real-time performance and smoothness.

[0044] like Figure 4 As shown, this illustrates the naming convention for the product model DS-VF20 in this embodiment, where DS is the preset manufacturer identifier, VF represents the product series (Vein Finder), and the following numbers represent the product generation and upgrade serial number. Figure 5 As shown, this is a schematic diagram of the overall appearance of the vein imaging device in this embodiment, presenting a handheld device form that is easy to operate with one hand. Figure 6 As shown, it is an exploded schematic diagram of the internal structure of the vein imaging device in this embodiment, showing its internal structure composed of multiple precision components.

[0045] The system described in this invention constructs the following collaborative mechanism through deep interlocking of optics, algorithms, and inertial sensing; none of these mechanisms can be omitted:

[0046] (1) The time trade-off mechanism between optics and computing power: There is an implicit causal interlock between the orthogonal polarization module (hardware) and the motion compensation algorithm (software). The orthogonal polarization design directly filters out more than 90% of the surface specular reflection noise at the physical optical path level. This physical preprocessing greatly reduces the computational load of the back-end ISP pipeline. It is precisely because the optical part "saves" a lot of noise reduction computing power and time that the FPGA can reserve the precious 35ms time window for the Kalman filter and geometric transformation module. If the polarization module is removed, in order to achieve the same signal-to-noise ratio, the ISP processing time will increase significantly and exceed the 35ms threshold, causing the prediction error of the Kalman filter to diverge exponentially and the motion compensation to fail.

[0047] (2) Inertial-Vision Closed-Loop Locking Mechanism: The system establishes a "dynamic-to-static" display logic. Traditional projectors are passive displays, while this system is an active tracking system. The angular velocity ω provided by the IMU directly determines the "dynamic field of view boundary" of the projection module. When a severe jitter is detected, the system uses the predicted Δθ rotation component to distort the image in the opposite direction to counteract the physical rotation of the device, thereby producing a visual persistence effect of "the image is locked on the skin" on the observer's retina. This effect cannot be achieved by a single image sensor or a single IMU; it must be achieved by the two working together in sub-millisecond time synchronization.

[0048] In summary, this application, through the organic combination of orthogonal polarization optical design and motion compensation algorithm based on IMU prediction, and by utilizing an FPGA hardware platform for efficient implementation, successfully solves the problems of projection delay, misalignment, and surface noise interference in dynamic usage scenarios of handheld vein imaging devices, significantly improving the positioning accuracy, imaging quality, and user experience of the device.

[0049] It should be noted that the various functions described in the embodiments of this application, such as twelve projection color switching, three adjustable sizes, inversion mode, six adjustable projection brightness, and nine working modes (default, enhancement, hair removal, auxiliary lines, etc.), can all be implemented by the MCU (108) responding to button signals and communicating and coordinating with the FPGA (106) as the main processing unit. For example, in the "enhanced mode", the FPGA can load a blood vessel enhancement algorithm based on Hessian matrix feature analysis; in the "auxiliary line mode", the FPGA can superimpose crosshairs or concentric circles on the final projected image. These are all specific application extensions of the technical solution of this application.

[0050] 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A vein imaging device, characterized in that, include: Near-infrared light source module, used to emit near-infrared light; A first polarizer is disposed in the optical path of the near-infrared light source module; An image sensor is used to acquire the near-infrared light reflected from skin tissue to generate an image frame; A second polarizer is disposed in the optical path of the image sensor, and the polarization direction of the second polarizer is orthogonal to the polarization direction of the first polarizer; An inertial measurement unit is used to acquire the motion posture data of the vein imaging device in real time; The processing unit is electrically connected to the image sensor and the inertial measurement unit; And a projection module, electrically connected to the processing unit, for projecting the compensated projection image onto the surface of the skin tissue; The processing unit is configured as follows: Based on the image frames acquired by the image sensor, venous vessel features are extracted; The predicted displacement of the vein imaging device is calculated based on the motion posture data of the inertial measurement unit and the preset system delay. Perform a geometric transformation on the extracted vein features that is opposite to the predicted displacement to generate a compensated projected image; The processing unit is configured to use a Kalman filter algorithm to calculate the predicted displacement. Specifically, the processing unit is configured to: use the angular velocity and linear acceleration data output by the inertial measurement unit as input to the Kalman filter algorithm, use the displacement and rotation angle of the vein imaging device in three-dimensional space as a state vector, and iteratively calculate the predicted value of the state vector at the end of the system delay time period through the state prediction step of the Kalman filter algorithm, thereby obtaining the predicted displacement.

2. The vein imaging device according to claim 1, characterized in that, The state transition matrix of the Kalman filter algorithm Based on the system latency The kinematic model of the vein imaging device is pre-set; the process noise covariance matrix of the Kalman filtering algorithm is... and measurement noise covariance matrix It is calibrated and optimized based on experience motion data for handheld device operation.

3. The vein imaging device according to claim 1, characterized in that, The geometric transformation is an affine transformation; the processing unit is configured to process the translation component contained in the predicted displacement. , and rotational components Construct an affine transformation matrix and apply it to each pixel in the vein feature.

4. The vein imaging device according to claim 3, characterized in that, For any pixel coordinate in the vein feature Its new coordinates after the affine transformation Obtained through the following matrix operations: 。 5. The vein imaging device according to claim 1, characterized in that, The processing unit is a field-programmable gate array (FPGA).

6. The vein imaging device according to claim 5, characterized in that, The field-programmable gate array is also configured to implement an image signal processing (ISP) pipeline and an algorithm for extracting the vein features, and is configured to directly drive the projection module to ensure that the end-to-end delay of the vein imager from acquiring the image frame to projecting the compensated projected image is less than 35 milliseconds.

7. The vein imaging device according to claim 1, characterized in that, The inertial measurement unit is a six-axis inertial measurement unit, used to provide three-axis angular velocity data and three-axis acceleration data as the motion attitude data.

8. The vein imaging device according to claim 1, characterized in that, Both the first polarizer and the second polarizer are linear polarizers.

9. The vein imaging device according to claim 1, characterized in that, The projection module is a digital light processing (DLP) projection module.

10. The vein imaging device according to claim 1, characterized in that, The near-infrared light source module includes multiple light-emitting diodes (LEDs), and the center wavelength of the near-infrared light emitted by the LEDs is in the range of 800 nanometers to 950 nanometers.

11. The vein imaging device according to claim 1, characterized in that, It also includes a microcontroller unit (MCU) electrically connected to the processing unit for responding to key input signals and performing power management.

12. The vein imaging device according to claim 1, characterized in that, The system delay is a pre-calibrated time difference between the moment when the image sensor completes the exposure of a frame of image and the moment when the projection module begins to project the processing result corresponding to that frame of image.

13. A control method for a vein imaging device, characterized in that, Includes the following steps: Near-infrared light, polarized by a first polarizer, is emitted through a near-infrared light source module; The near-infrared light reflected from skin tissue and filtered by a second polarizer is acquired by an image sensor to generate an image frame, wherein the polarization direction of the second polarizer is orthogonal to the polarization direction of the first polarizer; The motion posture data of the vein imaging device is acquired in real time through the inertial measurement unit; Extract vein features based on the image frames; The predicted displacement of the vein imaging device is calculated based on the motion posture data and the preset system delay. Perform a geometric transformation on the extracted vein features that is opposite to the predicted displacement to generate a compensated projected image; as well as The compensated projection image is projected onto the surface of the skin tissue using a projection module; The step of calculating the predicted displacement employs a Kalman filter algorithm. Specifically, this step includes: using the angular velocity and linear acceleration data in the motion attitude data as input to the Kalman filter algorithm, using the displacement and rotation angle of the vein imaging device in three-dimensional space as a state vector, and iteratively calculating the predicted value of the state vector at the end of the system delay time period through the state prediction step of the Kalman filter algorithm, thereby obtaining the predicted displacement.

14. The method according to claim 13, characterized in that, The state transition matrix of the Kalman filter algorithm Based on the system latency The kinematic model of the vein imaging device is pre-set; the process noise covariance matrix of the Kalman filtering algorithm is... and measurement noise covariance matrix It is calibrated and optimized based on experience motion data for handheld device operation.

15. The method according to claim 13, characterized in that, The step of performing the geometric transformation specifically involves performing an affine transformation; this step includes: based on the translation component contained in the predicted displacement. , and rotational components Construct an affine transformation matrix and apply it to each pixel in the vein feature.

16. The method according to claim 15, characterized in that, For any pixel coordinate in the vein feature Its new coordinates after the affine transformation Obtained through the following matrix operations: 。 17. The method according to claim 13, characterized in that, The steps of extracting venous features, calculating predicted displacement, and performing geometric transformations are all executed by a field-programmable gate array (FPGA).

18. The method according to claim 17, characterized in that, The method further includes, prior to the step of extracting venous features, performing an image signal processing (ISP) pipeline by the field-programmable gate array; and the projection step is performed by the projection module directly driven by the field-programmable gate array to ensure that the end-to-end delay from the step of generating the image frame to the completion of the projection step is less than 35 milliseconds.

19. The method according to claim 13, characterized in that, The step of acquiring motion attitude data is performed by a six-axis inertial measurement unit to provide three-axis angular velocity data and three-axis acceleration data.

20. The method according to claim 13, characterized in that, Both the first polarizer and the second polarizer are linear polarizers.

21. The method according to claim 13, characterized in that, The projection process is performed by a digital light processing (DLP) projection module.

22. The method according to claim 13, characterized in that, The center wavelength of the emitted near-infrared light is in the range of 800 nanometers to 950 nanometers.

23. The method according to claim 13, characterized in that, The method also includes steps of responding to key input signals and performing power management via a microcontroller unit (MCU).

24. The method according to claim 13, characterized in that, The system delay is a pre-defined time difference between the moment when the exposure of a frame of an image is completed and the moment when the processing result corresponding to that frame of an image begins to be projected.

Images