Image processing method, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides an image processing method, electronic equipment, a storage medium and a program product, interference of external ambient light is counteracted by intelligently adjusting the flicker frequency of an internal light source of an image acquisition device, and multiple monitoring images corresponding to organ parts such as the face, the palm and the tongue are acquired under the optimized illumination condition. And the image quality is improved through merging and averaging processing, so that a high-quality health monitoring image set is effectively generated.

Description

Technical Field

[0001] The present application relates to the technical field of medical devices, and in particular, to an image processing method, an electronic device, a storage medium, and a program product. Background Art

[0002] As the basis for traditional Chinese medicine diagnosis and treatment to analyze the condition, the accuracy of tongue, palm, and face images is directly related to the judgment and treatment of diseases. However, tongue, palm, and face image acquisition devices generally face the problem of being unable to adjust the light source according to the changes in the actual use environment. In outdoor environments with variable natural light intensity and in places with different indoor lighting conditions, image acquisition can only rely on a fixed light source brightness, resulting in uneven image quality collected by the acquisition device under different lighting conditions. For example, when the external light is too strong, the fixed light source brightness often cannot effectively illuminate the tongue surface, resulting in overly dark images with serious loss of details; when the external light is too weak, overexposure is likely to occur, making the images appear white and the key information blurred. The unstable interference of ambient light not only greatly reduces the clarity and contrast of the images but may also mislead the doctor's visual judgment. Therefore, there is an urgent need for a method that can flexibly adjust the internal lighting conditions of the tongue, palm, and face image acquisition device according to the actual lighting conditions of the external environment to ensure that high-quality images can be collected by the acquisition device in different lighting environments, thereby improving the accuracy and reliability of tongue, palm, and face diagnosis. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide an image processing method, an electronic device, a storage medium, and a program product to achieve the technical effect of improving the quality of image acquisition.

[0004] In the first aspect of the embodiments of the present application, an image processing method is provided. The method is applied to an image acquisition device that acquires a set of health monitoring images, and the set of health monitoring images includes target images of organ parts; the organ parts include one or more of the face, palm, and tongue; the image acquisition device includes an internal light source; the method includes:

[0005] Obtain the ambient light parameters of the external environment where the image acquisition device is located; the ambient light parameters include the first flicker frequency of the ambient light;

[0006] Adjust the second flicker frequency of the internal light source to be the same as and opposite in phase to the first flicker frequency;

[0007] Control the image acquisition device to respectively acquire multiple monitoring images corresponding to each of the organ parts under the adjusted internal light source;

[0008] For multiple monitoring images of each of the organ parts, the multiple monitoring images are combined and averaged to obtain a target image of the organ part, and a health monitoring image set including the target images of each of the organ parts is obtained.

[0009] In the above implementation process, by adjusting the flashing frequency of the internal light source of the image acquisition device to cancel out external ambient light interference, multiple monitoring images are collected and processed to obtain a high-quality health monitoring image set.

[0010] Further, the ambient light parameters further include the light direction and intensity of the ambient light; the internal light source includes multiple ones; the multiple internal light sources are respectively located in different orientations in the image acquisition device; the method further includes:

[0011] Based on the light direction, a target internal light source with an orientation opposite to the light direction is determined;

[0012] Based on the intensity, the target brightness of the target internal light source is determined;

[0013] Adjust the brightness of the target internal light source according to the target brightness.

[0014] In the above implementation process, by comprehensively considering the light direction and intensity of the external ambient light, the internal light sources with opposite orientations in the image acquisition device are selected and adjusted to an appropriate brightness, so as to effectively reduce ambient light interference and improve the acquisition quality of the health monitoring image set.

[0015] Further, the ambient light parameters further include the color temperature and illuminance of the ambient light; the internal light source includes a red light source, a yellow light source, and a blue light source; the method further includes:

[0016] If the color temperature and the illuminance are in a first preset interval, adjust the brightness of the blue light source to be higher than the brightness of the red light source and the brightness of the yellow light source;

[0017] If the color temperature and the illuminance are in a second preset interval, adjust the brightness of the yellow light source to be higher than the brightness of the red light source and the brightness of the blue light source; the second preset interval is higher than the first preset interval;

[0018] If the color temperature and the illuminance are in a third preset interval, adjust the brightness of the red light source to be higher than the brightness of the blue light source and the brightness of the yellow light source; the third preset interval is higher than the second preset interval.

[0019] In the above implementation process, according to the color temperature and illuminance of the ambient light, the brightness of the red, yellow, and blue light sources in the image acquisition device is intelligently adjusted to adapt to different lighting conditions and optimize the color balance and clarity of the health monitoring image set.

[0020] Further, perform denoising processing on the merged and averaged monitoring image to obtain a denoised image;

[0021] Increase the brightness of the shadow area in the denoised image to obtain an image with optimized brightness;

[0022] Perform contrast enhancement processing on the image with optimized brightness to obtain an image with enhanced contrast;

[0023] Blur the interfering objects in the image with enhanced contrast.

[0024] In the above implementation process, perform a series of processing such as merging and averaging, denoising, brightness optimization, contrast enhancement, and blurring of interfering objects on the monitoring image, significantly improving the quality of the health monitoring image and ensuring the clarity and accuracy of the image.

[0025] Further, the performing denoising processing on the merged and averaged monitoring image to obtain a denoised image includes:

[0026] Determine the brightness difference and color difference between each pixel point in the image and its adjacent pixel points;

[0027] If the brightness difference and the color difference exceed a preset threshold, determine the pixel point as a noise point;

[0028] Determine the target brightness of the noise point according to the brightness of the adjacent pixel points of the noise point;

[0029] Adjust the brightness of the noise point to the target brightness to obtain the denoised image.

[0030] In the above implementation process, by comparing the brightness and color differences between pixel points in the image and their adjacent pixel points, intelligently identify and adjust the brightness of noise points, thereby effectively removing the noise in the monitoring image.

[0031] Further, the blurring the interfering objects in the monitoring image with enhanced contrast includes:

[0032] Input the monitoring image with enhanced contrast into a trained region classification model to determine the region where the interfering objects are located;

[0033] Reduce the clarity and contrast of the region.

[0034] In the above implementation process, use the region classification model to identify the interfering object regions in the image and specifically reduce their clarity and contrast, thereby blurring the regions where the interfering objects are located and effectively reducing the impact of the interfering objects on the analysis of the health monitoring image.

[0035] Further, the image acquisition device is integrated with a light filtering device, and the light filtering device is used to filter out interfering light sources in the external environment.

[0036] In the above implementation process, by integrating the light filtering device to filter out interfering light sources in the external environment, the interference of complex ambient light from the outside on image acquisition is reduced, and the quality of the health monitoring image set is improved.

[0037] The second aspect of the embodiments of the present application provides an electronic device, and the electronic device includes:

[0038] A processor;

[0039] A memory for storing instructions executable by the processor;

[0040] Wherein, when the processor calls the executable instructions, any of the methods described in the first aspect is implemented.

[0041] The third aspect of the embodiments of the present application provides a computer-readable storage medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the steps of any of the methods described in the first aspect are implemented.

[0042] The fourth aspect of the embodiments of the present application provides a computer program product, and the computer program product includes a computer program, and when the computer program is executed by a processor, any of the methods described in the first aspect is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a schematic flowchart of an image processing method provided by an embodiment of the present application;

[0045] Figure 2 It is a schematic flowchart of another image processing method provided by an embodiment of the present application;

[0046] Figure 3 It is a schematic external structure diagram of an image acquisition device provided by an embodiment of the present application;

[0047] Figure 4 It is a schematic internal structure diagram of an image acquisition device provided by an embodiment of the present application;

[0048] Figure 5 It is a block diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0049] The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

[0050] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second", etc. are only used for differential description and cannot be understood as indicating or implying relative importance.

[0051] In the related art, the internal lighting conditions of an image acquisition device cannot be automatically adjusted according to the external ambient light, which results in uneven quality of the acquired health monitoring image set and affects doctors' diagnosis.

[0052] In view of any of the above problems, the embodiments of the present application provide an image processing method. Refer to Figure 1 , Figure 1 which is a schematic flowchart of an image processing method provided by an embodiment of the present application.

[0053] In this embodiment, the method is applied to an image acquisition device for acquiring a health monitoring image set, and the health monitoring image set includes target images of organ parts; the organ parts include one or more of the face, palm, and tongue; the image acquisition device includes an internal light source; the method includes:

[0054] Step S10: Obtain the ambient light parameters of the external environment where the image acquisition device is located; the ambient light parameters include the first flicker frequency of the ambient light;

[0055] It should be noted that the image acquisition device may be an acquisition instrument for acquiring images of a patient's face, tongue (including the tongue surface and the tongue bottom), and palm, for example, a traditional Chinese medicine four-diagnosis instrument.

[0056] The internal light source of the image acquisition device may be an LED lamp, for example, a red, yellow, and blue tri-color LED lamp. These lamps can be used alone or in combination to adjust the internal lighting conditions of the image acquisition device.

[0057] The flicker frequency refers to the number of times the light source changes from bright to dark per unit time.

[0058] It can be understood that by obtaining the ambient light parameters, the internal light source of the image acquisition device can be intelligently adjusted to be coordinated with the ambient light, so as to achieve the effect of eliminating the interference of the ambient light, making the acquired monitoring images real and accurate, and providing effective support for traditional Chinese medicine diagnosis.

[0059] Step S20: Adjust the second flashing frequency of the internal light source to be the same as and opposite in phase to the first flashing frequency;

[0060] It should be noted that the phase refers to the position of a specific point in the flashing waveform relative to the reference point. In alternating current, the phase difference can be used to describe the relative position relationship between two waveforms. When two waveforms with the same frequency and opposite phases meet, they will cancel each other out to form a stable output. This principle also applies in the optical field, that is, when two light sources with the same flashing frequency and opposite phases are superimposed, their flashing effects will cancel each other out.

[0061] Specifically, a dedicated ambient light sensor is used to detect the flashing frequency of the external ambient light. After detecting the flashing frequency of the external ambient light, it is necessary to calculate the phase difference that the internal light source needs to adjust. Calculating the phase difference usually involves analyzing the flashing waveform to determine its phase relationship with the reference waveform. According to the calculated phase difference, adjust the flashing frequency and phase of the internal light source so that it is the same as and opposite in phase to the flashing frequency of the external ambient light, thereby eliminating or reducing the impact of the external ambient light flashing on image acquisition, improving the image quality, and making the image clearer and more stable.

[0062] Step S30: Control the image acquisition device to respectively acquire multiple monitoring images corresponding to each of the organ parts under the adjusted internal light source;

[0063] It should be noted that when acquiring the monitoring images, due to the changes in the external ambient light, the performance limitations of the image acquisition device, and the complexity of the organ parts themselves, a single acquisition may not be able to obtain sufficiently clear and accurate images. Therefore, by acquiring multiple times and comprehensively processing these images, random errors and uncertainties can be reduced, and the image quality can be improved. Especially for the case where the ambient light has flashing interference, even if the internal light source has compensated to a certain extent with opposite bright and dark changes, it may not be able to eliminate all the impacts brought by the ambient light flashing. Therefore, it is necessary to acquire multiple images of the same part for processing to eliminate all the impacts brought by the ambient light flashing. Optionally, if the ambient light does not flash, there is no need to acquire multiple monitoring images of the same organ part, and only one image needs to be acquired for each organ part respectively.

[0064] As an example, under the adjusted internal light source, each organ part is acquired multiple times. Each time of acquisition, the position or angle of the image acquisition device can be slightly adjusted to obtain images under different perspectives or lighting conditions.

[0065] Step S40: For multiple monitoring images of each of the organ parts, perform merging and averaging processing on the multiple monitoring images to obtain a target image of the organ part, and obtain the health monitoring image set including the target images of each of the organ parts.

[0066] It should be noted that the purpose of merging and averaging multiple images is to reduce the noise and random errors in the images through statistical averaging, and improve the clarity and accuracy of the images.

[0067] Exemplarily, before performing the merging and averaging processing, it is first necessary to align the multiple monitoring images. Since there may be slight position or angle differences during acquisition, it is necessary to register the images so that they are aligned in the same coordinate system. Merge the aligned multiple monitoring images to form a superimposed image set, which can be completed by adding the image pixel values or performing other forms of combination. Perform averaging processing on the merged image set, that is, calculate the average value at each pixel position. This step can eliminate or reduce the random noise and interference in the image, and improve the clarity and contrast of the image. After the merging and averaging processing, a target image corresponding to each organ part is obtained, and each organ part corresponds to a target image, and the health monitoring image set is composed of one or more target images.

[0068] In a specific implementation, for the case of ambient light flickering, the host controls the three-color LED to flicker at the same frequency but with opposite phases according to the flicker frequency measured by the ambient light sensor (a sensor dedicated to collecting ambient light parameters), and adjusts the brightness ratio according to the illuminance change. Exemplarily, if the ambient light flicker frequency is 50 Hz, the host precisely controls the flicker rhythm and brightness of the LED through the PWM signal. In the image processing stage, the image processing algorithm performs superimposing processing on multiple continuously acquired frames of images, and adopts the image averaging algorithm to average the light and dark changes caused by the flicker, so as to eliminate the stripes and stabilize the image quality, so that the collected tongue surface and facial images can truly reflect the actual situation of the patient, and provide a reliable basis for doctors' diagnosis.

[0069] In this embodiment, by adjusting the internal light source flicker frequency to offset the ambient light interference and performing merging and averaging processing on multiple monitoring images, the image quality is significantly improved, providing effective support for traditional Chinese medicine diagnosis.

[0070] Based on any of the above embodiments, the ambient light parameters further include the light direction and intensity of the ambient light; the internal light source includes multiple; the multiple internal light sources are respectively located in different orientations in the image acquisition device; the method further includes steps S50-S70 as Figure 2 shown:

[0071] Step S50: Based on the light direction, determine a target internal light source whose orientation is opposite to the light direction.

[0072] It should be noted that the light direction describes the direction of the ambient light relative to the image acquisition device. The light of the ambient light is not always comprehensive and uniform, but is affected by various factors. For example, when the image acquisition device is located outdoors, the image acquisition device is affected by natural light sources such as the sun. The light direction of the sun changes with time and geographical location. At sunrise and sunset, the light direction of the sun is oblique, while at noon, the light direction of the sun is almost vertical. This change in light direction will have different effects on image acquisition, such as producing different shadow and reflection patterns. When the image acquisition device is located indoors, it may also be affected by artificial light sources such as lights and fluorescent lamps, and their light directions are also variable. If the artificial light source irradiates the left side of the image acquisition device, it may cause shadows on the right side of the acquired image, or in the case of excessive light intensity of the artificial light source, it may cause overexposure on the left side of the acquired image. Therefore, it is necessary to adjust the internal light source according to the light direction and intensity of the ambient light. Optionally, if the light direction and intensity of the ambient light are both at or close to the ideal level, then there is no need to adjust the internal light source.

[0073] Intensity describes the brightness level of the ambient light. Adjust the brightness of the internal light source according to the intensity of the ambient light to ensure that the internal light source can be balanced with the ambient light and reduce the problem of too large or too small contrast. Optionally, a dedicated ambient light sensor is used to monitor the light intensity of the environment around the imager in real time. According to the data (including light direction and intensity, etc.) fed back by the ambient light sensor, the light source control system will automatically adjust the brightness of the internal light source to ensure that clear, uniform, and tongue surface images in the best exposure state can be obtained under different lighting conditions.

[0074] The internal light sources are integrated at different orientations inside the image acquisition device. As an example, internal light sources are integrated at the four orientations of the top, bottom, left, and right of the image acquisition device. Among them, the internal light source can be either an incandescent lamp or a red, yellow, and blue three-color LED lamp.

[0075] The advantages of the distributed design of the internal light source are reflected in the following aspects:

[0076] Improve lighting uniformity: Multiple internal light sources can provide lighting for the organ part to be photographed from different angles, thereby effectively reducing the shadow area and improving the lighting uniformity of the entire image, which helps to capture details of the face, palm, and tongue.

[0077] Enhanced adaptability: Since the direction and intensity of ambient light may change at any time, multiple internal light sources can be flexibly adjusted to better adapt to these changes. They can work together to ensure high-quality images under any lighting conditions.

[0078] Reduced reflection and glare: By distributing the internal light sources in different orientations, the problems of reflection and glare caused by a single light source can be reduced. This helps to maintain the clarity and contrast of the image and reduce image distortion caused by light problems.

[0079] Specifically, a sensor integrated inside the image acquisition device is used to detect the incident angle of light, determine the light direction, and based on the light direction, determine the internal light source opposite to this direction, which is the target internal light source.

[0080] Step S60: Based on the intensity, determine the target brightness of the target internal light source;

[0081] It is understandable that the brightness of the internal light source is adjusted according to the different light intensities. For example, when the external light is strong, the brightness of the internal light source is appropriately reduced to avoid overexposure of the captured image; when the external light is weak, the brightness of the internal light source is appropriately increased to provide sufficient illumination.

[0082] Step S70: Adjust the brightness of the target internal light source according to the target brightness.

[0083] It should be understood that based on the principle of light cancellation in physics, that is, when two beams of light meet with the same intensity but in opposite directions, they will cancel each other out, thereby reducing the interference of light on image acquisition. Therefore, it is necessary to adjust the brightness of the internal light source opposite to the direction of the ambient light to cancel the interference caused by the external ambient light in terms of direction and intensity.

[0084] In a specific implementation, when the ambient light sensor used to collect ambient light parameters detects strong side light illumination, the host, according to the light direction and intensity information, instructs the control board to adjust the brightness distribution of the red, yellow, and blue LEDs through PWM instructions. Exemplarily, if the side light comes from the left, the host instructs the control board to increase the brightness of the red, yellow, and blue LEDs on the right, such as increasing the brightness of the red LED by 20%, the yellow LED by 15%, and the blue LED by 15%, and at the same time appropriately reducing the brightness of the three-color LEDs on the left. In the image processing stage, the image processing algorithm optimizes the brightness and contrast of the captured image. The algorithm first analyzes the brightness distribution characteristics of the shadow area in the image, and then by increasing the brightness value of the pixels in the shadow area and reasonably adjusting the contrast of the entire image, makes the tongue and facial features in the shadow area visually coordinated with the features in the non-shadow area, effectively avoiding feature blurring or loss caused by shadows, and ensuring that doctors can clearly and accurately observe the image details.

[0085] In this embodiment, by determining an internal light source opposite to the direction of the ambient light and adjusting its brightness based on the intensity of the ambient light, the interference of the external ambient light on image acquisition is effectively cancelled out. This strategy of dynamically adjusting the internal light source not only improves the flexibility and adaptability of image acquisition, but also significantly enhances the clarity and contrast of the images.

[0086] Based on any of the above embodiments, the ambient light parameters further include the color temperature and illuminance of the ambient light; the internal light source includes a red light source, a yellow light source, and a blue light source; the method further includes:

[0087] If the color temperature and the illuminance are within a first preset range, adjust the brightness of the blue light source to be higher than the brightness of the red light source and the brightness of the yellow light source;

[0088] If the color temperature and the illuminance are within a second preset range, adjust the brightness of the yellow light source to be higher than the brightness of the red light source and the brightness of the blue light source; the second preset range is higher than the first preset range;

[0089] If the color temperature and the illuminance are within a third preset range, adjust the brightness of the red light source to be higher than the brightness of the blue light source and the brightness of the yellow light source; the third preset range is higher than the second preset range.

[0090] It should be understood that the characteristics of red, yellow, and blue LEDs:

[0091] (1) The light emitted by the red LED has a relatively long wavelength. In a high-color-temperature environment, it can add a stable warm tone to the image. For tongue diagnosis, red light can enhance the presentation of the characteristics related to qi and blood in the tongue color. For example, under the illumination of red light, the color of a light red tongue can better reflect the balanced state of qi and blood in the human body; a red tongue can more significantly show the heat excess of qi and blood in the body. In facial images, red light helps to highlight the rosiness of the complexion, which is in line with the traditional Chinese medicine theory that the complexion reflects qi and blood.

[0092] (2) The wavelength of the light emitted by the yellow LED is moderate, which is an important element for constructing a natural color balance. In a medium-color-temperature environment, yellow light works in coordination with red and blue light to simulate the spectral characteristics similar to natural sunlight. This is extremely important for showing the fine texture of the tongue surface, such as the texture of the tongue coating and the moisture of the tongue body, as well as the detailed features of the face, such as the texture of the skin and the state of pores. It can avoid the loss of image details or misjudgment caused by light color deviation.

[0093] (3) The short-wavelength light generated by blue LEDs has obvious advantages in low-color-temperature environments. It can enhance the cool-toned components of the light, keeping the image clear and bright in strong light environments. For the tongue surface, blue light can highlight subtle features such as shallow cracks and tiny spots on the tongue surface, and these subtle features may be related to the early signs of certain diseases. In facial images, blue light helps to present the three-dimensional effect and subtle color changes of the face, such as slight freckles on the face or uneven skin tone.

[0094] It should be noted that color temperature is a physical quantity that measures the color of a light source. Different color temperatures will bring different color sensations to the human eye, such as cool tones and warm tones. During the image acquisition process, changes in color temperature will affect the color balance and authenticity of the image.

[0095] Illuminance is a unit that reflects the intensity of light, referring to the luminous flux received per unit area. Illuminance is used to measure the strength of light or the lighting level. Changes in illuminance will directly affect the brightness and contrast of the image.

[0096] Red light source, yellow light source, blue light source: These three-color light sources form the basis of the internal light source. By adjusting the brightness ratio of them, different color temperature effects can be simulated, so as to match the ambient light and improve the color restoration degree and visual effect of the image. In addition to adjusting the red, yellow, and blue light sources to the white balance state according to the color temperature and illuminance as described in this embodiment, and collecting one or more monitoring images for each organ part in this state (optionally, when the ambient light is stable and non-flickering, only one monitoring image is collected for each organ part, and when the ambient light is flickering, multiple monitoring images are collected for each organ part), according to actual application requirements, the red, yellow, and blue light sources can also be regulated, and one or more monitoring images are respectively collected for each organ part under the illumination conditions of red light, yellow light, and blue light, so as to provide doctors with richer diagnostic information and assist doctors in more accurately analyzing the actual conditions of the organ parts. For example, for the face, one or more monitoring images are collected in the white balance state, and at the same time, at least one monitoring image can be collected respectively under the illumination conditions of red light, yellow light, and blue light to obtain multi-spectral image data.

[0097] The first preset interval: When the color temperature and illuminance of the ambient light fall within this interval, it means that the environment presents a warm tone and the brightness is appropriate. To balance this warm-toned environment, the proportion of the blue light source in the combination of red, yellow, and blue light sources needs to be increased. The introduction of the blue light source can effectively reconcile the illumination and add a cool-toned effect, thus balancing the light color.

[0098] The second preset interval corresponds to an environment with a neutral color tone and moderate brightness. In this environment, in order to create a neutral white lighting atmosphere, the brightness of the yellow light source needs to be adjusted higher than that of the red and blue light sources. This helps to clearly show the color and thickness changes of the tongue coating on the tongue surface, as well as the normal or abnormal states of the facial skin color, such as sallow complexion, flushing, etc., providing accurate diagnostic image information for doctors.

[0099] The third preset interval represents an environment with a cold color tone and moderate brightness. In order to balance this cold color tone and avoid color distortion, the brightness of the red light source needs to be adjusted higher than that of the yellow and blue light sources to ensure the accuracy of tongue image and facial color judgment. Especially under the cold white indoor lighting with a high color temperature, this adjustment can ensure the true presentation of the white coating on the tongue surface, avoid the phenomenon of misjudgment caused by an overly white image, and accurately reflect the actual state of the facial skin color.

[0100] Exemplarily, the first preset interval can be that the ambient light color temperature is close to 5000K and the illuminance is stable, with the specific illuminance within the range of 3000 ± 10% LX; the second preset interval can be that the ambient light color temperature is close to 6000K and the illuminance is 3000 LX; the third preset interval can be that the ambient light color temperature is close to 7000K and the illuminance is normal, with the specific illuminance also within the range of 3000 ± 10% LX. Of course, the embodiments of the present application are not limited to this, and the first preset interval, the second preset interval, and the third preset interval can be set according to actual needs.

[0101] In this embodiment, by monitoring the color temperature and illuminance of the ambient light and adjusting the brightness of the internal light sources (red, yellow, blue) according to the different preset intervals (the first preset interval, the second preset interval, the third preset interval) it is in, the balance of the light color is achieved, so as to ensure that the image color is normal and accurate under different ambient light conditions and avoid color distortion.

[0102] Based on any of the above embodiments, the method further includes:

[0103] Performing denoising processing on the monitored image after merging and averaging to obtain a denoised image;

[0104] It should be noted that in a relatively dark environment, although there is adaptive dimming of the three-color LED, noise may still be generated in the collected image. Therefore, denoising processing is performed on the monitored image.

[0105] Optionally, various denoising algorithms, such as mean filtering, median filtering, Gaussian filtering, non-local means denoising, BM3D denoising, etc., are used to perform denoising on the monitored image after merging and averaging.

[0106] Increasing the brightness of the shadow area in the denoised image to obtain an image with optimized brightness;

[0107] Specifically, techniques such as image segmentation, threshold processing, or deep learning are used to identify the shadow regions in the image. Then, the identified shadow regions are subjected to brightness increase processing, and methods such as linear stretching, histogram equalization, or adaptive brightness adjustment can be used to complete the brightness increase processing.

[0108] Perform contrast enhancement processing on the image with optimized brightness to obtain an image with enhanced contrast;

[0109] It can be understood that enhancing the contrast of the image can make the details in the image clearer and improve the visual effect of the image.

[0110] Optionally, methods such as linear contrast stretching, piecewise linear contrast stretching, histogram equalization, or contrast-limited adaptive histogram equalization (CLAHE) are used to perform contrast enhancement processing on the image with optimized brightness.

[0111] In a specific implementation, the contrast is enhanced by stretching the brightness histogram of the image. First, comprehensively count the brightness distribution of all pixels in the image to construct an accurate brightness histogram. Then, the pixel brightness values in the darker regions of the histogram are reduced as a whole, and the pixel brightness values in the brighter regions are increased as a whole, thereby effectively expanding the brightness range of the image. For example, for the relatively light cracks on the tongue surface, they may be difficult to detect in the original image due to low contrast. After contrast enhancement processing, the pixel brightness in the crack region is reduced, and the pixel brightness in the surrounding normal tongue surface region is relatively increased, making the cracks clearly visible in the image; for the slight blushing on the face, similarly, by enhancing the brightness difference between the blushing region and the surrounding normal skin color region, it can be keenly captured by the doctor. This contrast enhancement mechanism is based on the visual sensitivity difference of the human eye to different brightness regions. By expanding the brightness difference, the originally subtle features become more prominent, assisting the doctor in discovering potential pathological features and improving the accuracy of diagnosis.

[0112] Blur the interfering objects in the image with enhanced contrast.

[0113] It should be noted that when there are interfering objects such as hair or ornaments on the tongue surface or face of the patient in the image, or the background has complex patterns, it is necessary to blur or mask the interfering objects.

[0114] In this embodiment, through steps such as denoising processing, brightness optimization, contrast enhancement, and blurring of interfering objects, the monitoring image is comprehensively optimized, improving the quality and visual effect of the image.

[0115] Based on any of the above embodiments, the step of performing denoising processing on the merged and averaged monitoring image to obtain a denoised image includes:

[0116] Determine the brightness difference and color difference between each pixel point in the image and its adjacent pixel points;

[0117] If the brightness difference and the color difference exceed a preset threshold, determine the pixel point as a noise point;

[0118] Determine the target brightness of the noise point according to the brightness of the adjacent pixel points of the noise point;

[0119] Adjust the brightness of the noise point to the target brightness to obtain the denoised image.

[0120] It should be noted that the preset threshold is a standard value used to distinguish normal pixel points from potential noise points, which can be determined according to experience or experimental data, and this embodiment does not limit it.

[0121] Specifically, an intelligent denoising method based on pixel statistical analysis is used for image denoising. First, the pixels in the image are grouped and statistically analyzed, and the differences in multiple dimensions such as brightness and color between each pixel point and its adjacent pixel points are deeply analyzed. For example, for a pixel point, if its brightness difference from multiple surrounding adjacent pixel points exceeds the preset threshold and its color distribution does not conform to the overall feature pattern of the image, it is determined as a noise point. Then, according to the type and distribution of the noise points, methods such as mean filtering or median filtering are used for processing. Mean filtering replaces the brightness value of the noise pixel with the average value of the brightness of its surrounding pixels, and median filtering replaces the brightness value of the noise pixel with the median value of the brightness of its surrounding pixels. Through any one of these methods, the noise points in the image are effectively removed, making the image more pure and clear, and reducing the interference of noise points on the judgment of tongue surface and facial features. For example, in a tongue surface image collected in a low-light environment, there may be some isolated bright or dark noise points, which may be misrecognized as lesion points on the tongue surface. After the noise removal process, the true texture and color features of the tongue surface are accurately presented, avoiding misdiagnosis. Among them, the overall feature pattern refers to the overall trend or pattern presented by the image in dimensions such as brightness and color. These patterns can be obtained through statistical analysis of the image and are used to judge whether a pixel point conforms to the features of its adjacent pixel points and the entire image. The overall feature pattern is an abstract description based on the statistical characteristics of the image. If the color or brightness of a certain pixel point is significantly different from the features of its surrounding pixel points and the entire image, then it may be a noise point.

[0122] In this embodiment, the monitored image after merging and averaging is denoised. By comparing the brightness and color differences between each pixel point and its adjacent pixel points, the pixel points that exceed the preset threshold are identified as noise points, and the brightness of the noise points is adjusted according to the brightness of their adjacent pixel points, thereby effectively removing the noise points and obtaining a clearer and purer denoised image.

[0123] Based on any of the above embodiments, blurring the interfering objects in the monitored image after contrast enhancement includes:

[0124] Inputting the monitored image after contrast enhancement into a trained region classification model to determine the region where the interfering objects are located;

[0125] Reducing the clarity and contrast of the region.

[0126] It should be noted that for the region where the interfering objects are located, blurring processing and / or shielding processing can be performed.

[0127] Specifically, the image segmentation technology in machine learning is used to accurately identify the target region (i.e., the region where the interfering objects are located). This process relies on a deeply trained image segmentation model (i.e., the region classification model), which has mastered the typical features and region definitions of the tongue, face, palm, and potential interfering objects through the preprocessing and learning of a large number of tongue-face-palm images. During the model training stage, it analyzes and extracts key information in the image, including but not limited to color, texture, and shape features, so as to construct an in-depth understanding and cognitive framework of the tongue-face-palm image and its interfering objects. After training, the image segmentation model can automatically identify and distinguish the specific regions of the tongue surface, face, palm, and interfering objects in the actual application scenario. For the identified interfering object region, a blurring processing or shielding processing strategy is adopted. For example, when the face is blocked by hair, the image segmentation model can accurately define the hair region and blur it, reducing the clarity and contrast of the hair region to reduce its visual interference and highlighting the key features such as the skin color and complexion of the face. Similarly, for interfering objects such as jewelry, the model can accurately identify the region where it is located based on the shape and position information of the jewelry, and shield or replace the region with colors and textures similar to the surrounding background, thereby effectively reducing the impact of the interfering objects on the judgment of tongue images and complexions and significantly improving the usability and diagnostic value of the image. For example, when processing a tongue-face-palm image of a patient wearing a necklace, the image segmentation model can quickly and accurately identify the necklace region and obscure or blur it, enabling the doctor to focus on observing the features of the tongue surface and face and avoiding the reflection or shape of the necklace from misleading the diagnosis.

[0128] In this embodiment, by inputting the monitored image after contrast enhancement into the trained region classification model, the interfering object region is accurately located, and its clarity and contrast are reduced, thereby effectively weakening the impact of the interfering objects on the image presentation.

[0129] Based on any of the above embodiments, the image acquisition device is integrated with a light filtering device, and the light filtering device is used to filter out the interfering light sources in the external environment.

[0130] Optionally, the light filtering device refers to an optical filter.

[0131] An optical filter is an optical component that can selectively transmit or reflect light of specific wavelengths. Optical filters are generally made of transparent materials (such as glass, plastic, etc.) and have specific filter film layers coated on their surfaces or inside. They can reduce the interference of stray light, improve image clarity and color accuracy, optimize the shooting effect of the camera in complex lighting environments, and achieve precise control of light. In an image acquisition device, the optical filter can be integrated in front of the lens or the sensor (i.e., the ambient light sensor that acquires ambient light parameters) to filter out the interfering light sources in the ambient light. These interfering light sources may include unnecessary reflected light, glare, stray light, etc., which will have a negative impact on the quality of image acquisition. By introducing an optical filter, the interference of these interfering light sources can be effectively reduced, and the clarity and color accuracy of image acquisition can be improved. Of course, the filtering device is not limited to a single type of optical filter. The filtering device can also be other types of optical components, such as band-pass filters, cut-off filters, polarization filters, etc. These components are different in function and characteristics, but the common point is that they can all precisely control and filter light.

[0132] As Figure 3 shown, Figure 3 is a schematic diagram of the external structure of an image acquisition device provided by an embodiment of the present application. The patient needs to put their head into the acquisition window with a gray border so that the image acquisition device can acquire a set of health monitoring images. As Figure 4 shown, Figure 4 is a schematic diagram of the internal structure of an image acquisition device provided by an embodiment of the present application. Combining Figure 3 with Figure 4It can be seen that the image acquisition device constructs an intelligent image acquisition system, and its core components include an LED light source (specifically, it can be an LED light source of red, yellow, and blue colors), an ambient light sensor (for collecting the flicker frequency, light direction, intensity, color temperature, and illuminance of ambient light), a host, and a control board. On the top and the left and right sides of the chassis, three groups of ambient light sensors are respectively installed to achieve a full-range detection of external light sources. When the device starts to work, it will trigger the three groups of ambient light sensors to work. The three groups of ambient light sensors collect the parameters of external light sources in real time and send the collected values back to the host. The host calculates the average value based on the values collected by the three groups of sensors. According to the average value, the host generates corresponding instructions through PWM (pulse width modulation) technology and sends them to the control board. The control board precisely adjusts the current of the LED light source according to the instructions (optionally, precisely adjusts the currents of the red, yellow, and blue LEDs), thereby realizing real-time dimming of the light source to adapt to different environmental conditions and ensuring that high-quality tongue surface, facial, and palm images can be obtained. The ambient light sensor adopts a high-sensitivity photosensitive element array, which enables it to have the ability to perceive ambient light in all directions. It can not only accurately measure the color temperature and illuminance values of ambient light, but also keenly capture key parameters such as the direction change and flicker frequency of light. For example, when strong light shines from the side, the sensor can accurately calculate the incident direction and angle change information of the light through the light intensity difference received by different photosensitive elements. For the flickering situation of ambient light, such as in a stroboscopic light environment, the sensor uses high-speed sampling technology and frequency analysis algorithms to accurately measure the stroboscopic frequency. Its working principle is mainly based on the photoelectric effect of photosensitive elements, which converts light signals into electrical signals and performs signal processing and analysis through internal circuits, thereby obtaining various parameter information of ambient light. These information provide reliable data support for the device to make precise adjustments according to the ambient light conditions, effectively improving the adaptability and working efficiency of the device under different ambient light conditions. When the light sensing module of the ambient light sensor detects that the light brightness parameter of the acquisition window exceeds the set value, it will feedback the data to the host, and the host will automatically reduce the brightness of the LED light source to ensure that the brightness is within a reasonable range. On the contrary, when the light sensing module detects that the light brightness parameter of the acquisition window is lower than the set value, the host will automatically increase the brightness of the LED light source (optionally, adjust the LED light source within the range of 3000 LX ± 10% of the illuminance, reduce the brightness of the LED light if it exceeds; increase the brightness if it is lower). In addition, the image acquisition device shown in the embodiments of the present application provides a variety of preset working modes, including "automatic light sensing mode", "constant light mode", "low light mode", etc., and users can select the appropriate working mode according to the actual environment. Among them, the "automatic light sensing mode" means that the device will intelligently adjust the working mode of the internal light source according to the ambient light; the "constant light mode" means that the device will not adjust the internal light source according to the ambient light; the "low light mode" is mainly applicable to outdoor environments and means that the device will maintain a low brightness working mode.Users can choose the appropriate working mode according to the actual environmental conditions. At the same time, the camera of the image acquisition device can adjust the following shooting parameters: ① Aperture size: The maximum aperture can reach F2.2. This design allows the camera to capture sufficient light in a low-light environment. At the same time, by adjusting the aperture size, users can easily control the depth of field effect, whether they want a blurred background effect or a clear foreground and background effect. ② Exposure: The camera supports fine exposure adjustment. Users can manually adjust the exposure according to the actual shooting scene and light conditions to obtain the best light and dark balance and picture details. Whether it is a bright outdoor environment or a dark indoor environment, it can ensure that the picture is rich in layers and clear in details. ③ Sensitivity: The camera provides flexible sensitivity settings. Users can reasonably select the sensitivity value according to the light conditions during shooting, so as to effectively suppress the generation of noise while ensuring the clarity of the picture. In a low-light environment, increasing the sensitivity can capture more light; while in sufficient light, reducing the sensitivity can keep the picture pure.

[0133] Here is an example of "Automatic Exposure Mode":

[0134] (1) In an environment with low illumination and warm color temperature: When the ambient light sensor detects that the ambient light color temperature is about 3000K and the illumination is less than 10% of 3000LX (i.e. 300LX), the host instructs the control board to set the brightness of the red, yellow and blue LEDs through PWM instructions according to the algorithm. The blue LED brightness is adjusted to 60%, the yellow LED brightness is 30%, and the red LED brightness is 10%. This combination of light makes the image appear cool and can clearly illuminate the tongue and face in a low-light environment. In the image processing stage, the algorithm first removes noise from the monitoring image. Since more noise may be generated in a low-light environment, the noise can be removed by pixel statistical analysis and mean filtering methods. Then, the contrast of the image is enhanced by stretching the brightness histogram, making the tongue color such as light red tongue more vivid, and the key features such as the texture and color of the tongue coating and the complexion of the face can be clearly presented, thereby assisting doctors in making accurate diagnoses. In such an environment, the patient's tongue surface may appear dull due to insufficient light, but after image processing, the white or yellow tongue coating can be clearly distinguished from the tongue body, and facial features such as insufficient qi and blood or yellow complexion can also be accurately observed by the doctor.

[0135] (2) In an environment with high illuminance and a relatively cold color temperature: When the ambient light sensor detects that the ambient light color temperature is approximately 7000K and the illuminance is higher than 10% of 3000 LX (i.e., 3300 LX), the brightness of the red, yellow, and blue LEDs will be correspondingly adjusted by the host through PWM instructions to the control board. At this time, the brightness of the blue LED drops to 5%, the brightness of the yellow LED is adjusted to 15%, and the brightness of the red LED is increased to 80%. The adjusted light can adapt to the outdoor strong light environment and avoid image overexposure. In the image processing stage, a method based on pixel difference analysis is used to identify and remove the specular noise generated by strong light. Then, the contrast of the tongue surface and facial features is enhanced. For the fine cracks on the tongue surface and the fine wrinkles on the face, their visibility is improved by adjusting the brightness range, enabling the doctor to accurately observe the details of the tongue image and facial complexion, ensuring that the doctor can make an accurate diagnosis. For example, in outdoor sunlight, there may be specular reflection on the patient's tongue surface. After noise removal and contrast enhancement processing, the true color and texture of the tongue surface, such as the color depth of the crimson tongue, the crack condition of the tongue surface, as well as the wrinkles and color changes on the face can be clearly observed, providing an accurate basis for the doctor to judge the patient's physical condition.

[0136] (3) In complex and changing lighting environments: When the ambient light sensor detects complex lighting conditions, the ambient light color temperature is unstable due to the mixture of multiple colors, there is a certain amount of flickering, and the illumination is uneven, the brightness of the red, yellow, and blue LEDs will be dynamically adjusted under the intelligent control of the control panel according to the main color temperature components and illumination of the ambient light, and flexibly switch from neutral white to cold white, warm white and other different light atmospheres. For example, if the ambient light contains more yellow light components and flickers, the brightness of the red LED may be adjusted to 30%, the yellow LED to 40%, and the blue LED to 30%, and the LED flickering frequency is matched with the ambient light flickering frequency to offset the effect of stripes. In the image processing stage, the image processing algorithm will first use machine learning image classification technology to quickly locate the tongue surface and facial area, and then perform color correction on the area to remove the interference of other colors in the ambient light. By deeply analyzing the distribution and deviation of colors in the image, the algorithm will use digital image processing technology to adjust the proportion of color channels to restore the true tongue color and facial color. For example, if the ambient light is yellowish, causing the tongue color to be yellowish, the algorithm will reduce the proportion of the yellow channel in the image and increase the proportion of the red and blue channels to restore the tongue color to normal. The doctor can accurately judge the patient's physical condition and avoid misdiagnosis caused by ambient light interference. In complex lighting environments such as shopping malls, the patient's tongue color may be affected by the surrounding yellow light and appear yellowish, but after color correction, the doctor can accurately judge from the collected images whether the patient's tongue color is normal, such as whether there are abnormal conditions such as yellow fur, and can also accurately observe facial color changes, such as facial flushing and other features closely related to health. It can be seen that the image acquisition device has built a highly adaptive and accurate image acquisition system. The unique optical properties of the three-color LEDs work together under different color temperatures and illumination environments. Red light focuses on highlighting the characteristics of Qi and blood at low color temperatures, yellow light is committed to ensuring color balance and detail presentation at medium color temperatures, and blue light focuses on maintaining clarity and capturing lesion features under strong light at high color temperatures. The flexible adjustment of the red, yellow and blue brightness combinations can accurately simulate various ambient light effects and effectively avoid image deviations caused by changes in light conditions, providing a solid technical support for TCM tongue and face diagnosis at the source of image acquisition with high stability, high accuracy and wide adaptability to various complex environments, greatly improving the reliability and effectiveness of TCM diagnosis.

[0137] In this embodiment, by building a filter device into the image acquisition device, interfering light sources in the external environment can be effectively filtered out, reducing the impact of reflections and shadows on image quality, thereby improving the quality and accuracy of image acquisition.

[0138] Based on the method described in any of the above embodiments, the present application also provides Figure 5 A schematic diagram of the structure of an electronic device is shown in FIG. Figure 5, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the method described in any of the above embodiments.

[0139] Based on the method described in any of the above embodiments, the present application also provides a computer storage medium storing a computer program, which when executed by a processor can be used to execute the method described in any of the above embodiments.

[0140] Based on the method described in any of the above embodiments, the present application also provides a computer program product, which includes one or more computer programs or instructions. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. When the computer program is executed by a processor, it implements the method described in any of the above embodiments.

[0141] In several embodiments provided by the present application, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the drawings show the possible architectures, functions, and operations of the apparatus, method, and computer program product according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0142] In addition, in each embodiment of the present application, the various functional modules may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0143] When the above-mentioned functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0144] The above are only the embodiments of this application and are not used to limit the protection scope of this application. For those skilled in the art, this application can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this application shall be included in the protection scope of this application. It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0145] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by this application and should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0146] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitations, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

Claims

1. An image processing method, characterized in that: The method is applied to an image acquisition device for acquiring a health monitoring image set, wherein the health monitoring image set includes target images of organ parts; the organ parts include one or more of face, palm, and tongue; the image acquisition device includes an internal light source; the method includes: Acquire ambient light parameters of the external environment where the image acquisition device is located; the ambient light parameters include a first flicker frequency of the ambient light; adjusting a second flickering frequency of the internal light source to be the same frequency as the first flickering frequency and opposite in phase; Controlling the image acquisition device to respectively acquire a plurality of monitoring images corresponding to each of the organ parts under the adjusted internal light source; For the multiple monitoring images of each organ part, the multiple monitoring images are merged and averaged to obtain the target image of the organ part, and the health monitoring image set including the target image of each organ part is obtained.

2. The method according to claim 1, characterized in that The ambient light parameters also include the direction and intensity of the ambient light; the internal light source includes multiple light sources; the multiple internal light sources are respectively located at different positions in the image acquisition device; The method further comprises: Based on the light direction, determining a target internal light source whose orientation is opposite to the light direction; determining a target brightness of the target internal light source based on the intensity; The brightness of the light source inside the target is adjusted according to the target brightness.

3. The method according to claim 1 or 2, characterized in that The ambient light parameters also include the color temperature and illumination of the ambient light; the internal light source includes a red light source, a yellow light source, and a blue light source; the method also includes: If the color temperature and the illumination are within a first preset range, adjusting the brightness of the blue light source to be higher than the brightness of the red light source and the brightness of the yellow light source; If the color temperature and the illumination are in a second preset range, adjusting the brightness of the yellow light source to be higher than the brightness of the red light source and the brightness of the blue light source; the second preset range is higher than the first preset range; If the color temperature and the illumination are in a third preset range, the brightness of the red light source is adjusted to be higher than the brightness of the blue light source and the brightness of the yellow light source; the third preset range is higher than the second preset range.

4. The method according to claim 1, characterized in that The method further comprises: De-noising the combined and averaged monitoring images to obtain a de-noised image; Increasing the brightness of the shadow area in the denoised image to obtain a brightness-optimized image; Performing contrast enhancement processing on the brightness optimized image to obtain a contrast enhanced image; Blurring distracting objects in the contrast enhanced image.

5. The method according to claim 4, characterized in that The denoising process is performed on the combined and averaged monitoring image to obtain a denoised image, including: Determine the brightness and color differences between each pixel in the image and its adjacent pixels; If the brightness difference and the color difference exceed a preset threshold, the pixel is determined as a noise point; Determining a target brightness of the noise point according to the brightness of adjacent pixels of the noise point; The brightness of the noise point is adjusted to the target brightness to obtain the denoised image.

6. The method according to claim 4, characterized in that The blurring and contrast-enhanced monitoring of interference objects in the image includes: Inputting the contrast-enhanced monitoring image into a trained region classification model to determine the region where the interference object is located; Reduces the clarity and contrast of the area.

7. The method according to claim 1, characterized in that The image acquisition device is integrated with a filter device, and the filter device is used to filter out interfering light sources in the external environment.

8. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing processor-executable instructions; Wherein, when the processor calls the executable instruction, the method described in any one of claims 1-7 is implemented.

9. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the steps of any method described in claims 1-7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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