Method and device for enhancing imaging of low-reflectivity sample

Abstract

The invention discloses a low-reflectivity sample imaging enhancement method and device, and the method comprises the steps: obtaining a plurality of channel components of a low-reflectivity sample, the plurality of channel components comprise a first color channel component, a second color channel component and a third color channel component, and the low-reflectivity sample comprises a plurality of target objects; determining a first channel component related to the color of the target object; determining a first weight of the first color channel component, a second weight of the second color channel component and a third weight of the third color channel component based on the first channel component, the weight corresponding to the first channel component being greater than a standard weight corresponding to the first channel component; and weighting the first color channel component, the second color channel component and the third color channel component based on the first weight, the second weight and the third weight to obtain a grey-scale map corresponding to the low-reflectivity sample. According to the invention, the image features of the low-reflectivity sample can be enhanced.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing, and in particular to a method and device for enhancing imaging of low-reflectivity samples. Background Art

[0002] In the field of modern science and technology, image analysis of samples is of great significance. For example, in the field of materials science, especially when studying new materials, researchers usually need to analyze the microstructure of material samples through images to understand the relevant parameters of the materials.

[0003] In existing image analysis methods, a standard formula is generally used to convert multiple channel components of a sample into a grayscale image, and then the grayscale image is analyzed to obtain relevant parameters of the sample. The standard formula is generally determined based on the sensitivity of the human eye to different colors.

[0004] However, when the above image analysis method is applied to a low reflectivity sample, due to the low reflectivity of the low reflectivity sample, the reflected light is weak, resulting in a low contrast in the grayscale image obtained by converting multiple channel components. In other words, when the above image analysis method is applied to a low reflectivity sample, the features of the sample pattern in the grayscale image are not obvious, which in turn makes it difficult to effectively extract the image features of the sample pattern in the grayscale image, affecting further image analysis. Summary of the invention

[0005] The present disclosure is proposed in view of the above-mentioned situation, and its purpose is to provide a method and device for low reflectivity sample imaging enhancement that can enhance the image characteristics of low reflectivity samples.

[0006] To this end, a first aspect of the present disclosure provides a method for enhancing imaging of low-reflectivity samples, comprising: acquiring multiple channel components of the low-reflectivity sample, the multiple channel components including a first color channel component, a second color channel component and a third color channel component, and the low-reflectivity sample includes multiple targets; determining a first channel component related to the color of the target; determining a first weight of the first color channel component, a second weight of the second color channel component and a third weight of the third color channel component based on the first channel component, wherein, among the first color channel component, the second color channel component and the third color channel component, the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component; and performing weighted processing on the first color channel component, the second color channel component and the third color channel component based on the first weight, the second weight and the third weight to obtain a grayscale image corresponding to the low-reflectivity sample.

[0007] In this case, the first channel component is determined based on the color of the target object, the color of the target object can be analyzed, and the main color presented by the target object can be determined, and the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component. Compared with the standard formula with fixed weights, the weight can be adaptively adjusted based on the color of the target object in the process of obtaining the grayscale image, which can increase the effective signal in the grayscale image, thereby improving the contrast of the grayscale image, thereby enhancing the grayscale features of the target object pattern in the grayscale image (that is, it can enhance the image features of the low reflectivity sample), which can be beneficial to improve the accuracy of subsequent image analysis using the grayscale image. In addition, since the sum of the weights of multiple channel components is determined when converting to a grayscale image, the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component, which can make the sum of the weights corresponding to the other channel components other than the first channel component less than the sum of the standard weights corresponding to the other channel components, thereby reducing the noise signal in the grayscale image, improving the signal-to-noise ratio of the grayscale image, and further improving the contrast of the grayscale image.

[0008] In addition, in the method for enhancing imaging of low-reflectivity samples involved in the first aspect of the present disclosure, optionally, a second channel component related to the color of the noise is determined, and the first weight, the second weight, and the third weight are determined based on the first channel component and the second channel component, wherein, among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the second channel component is less than the weight corresponding to the first channel component. In this case, the weight of the channel component corresponding to the noise signal can be controlled within a range less than the weight of the channel component corresponding to the valid signal, thereby further improving the signal-to-noise ratio of the grayscale image.

[0009] In addition, in the method for enhancing imaging of low reflectivity samples involved in the first aspect of the present disclosure, optionally, among the first color channel component, the second color channel component and the third color channel component, the weight corresponding to the first channel component is the largest. In this case, when the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component, further maximizing the weight corresponding to the first channel component can further increase the effective signal of the grayscale image.

[0010] In addition, in the method for enhancing low reflectivity sample imaging involved in the first aspect of the present disclosure, optionally, the first color channel component corresponds to the red component of the target object, the second color channel component corresponds to the green component of the target object, and the third color channel component corresponds to the blue component of the target object. In this case, the characteristics of the image can be represented by the RGB color model. In addition, the RGB color model can intuitively display the color characteristics of the target object itself through different channel components, which can facilitate the subsequent quick determination of the channel components related to the color of the target object.

[0011] In addition, in the method for enhancing low reflectivity sample imaging according to the first aspect of the present disclosure, optionally, the formula related to the standard weight is a standard formula, and the third weight is 0.2 to 1. Thus, the contrast of the grayscale image can be improved.

[0012] In addition, in the method for enhancing low reflectivity sample imaging according to the first aspect of the present disclosure, optionally, the third weight is greater than the first weight, and the third weight is greater than the second weight. In this case, the signal of the target object whose color is closest to the color corresponding to the third color channel component in the grayscale image can be further increased.

[0013] In addition, in the method for enhancing low reflectivity sample imaging according to the first aspect of the present disclosure, optionally, when acquiring multiple channel components of the low reflectivity sample, the contrast of the grayscale image is improved by at least one of adjusting the brightness of the light source, adjusting the gain of the acquisition device used to acquire the multiple channel components, and adjusting the exposure time of the acquisition device. Thus, the contrast of the obtained grayscale image can be further improved.

[0014] In addition, in the method for enhancing low-reflectivity sample imaging according to the first aspect of the present disclosure, optionally, the grayscale image is used to determine the parameters of the low-reflectivity sample, and the parameters of the low-reflectivity sample include at least one of the total area of ​​the target, the number of the target, the width of the target, the height of the target, and the density of the target. In this case, the grayscale image is used to determine the parameters of the low-reflectivity sample, and after enhancing the image features of the low-reflectivity sample, the accuracy of the parameters can be improved.

[0015] In addition, in the method for enhancing the imaging of low-reflectivity samples involved in the first aspect of the present disclosure, optionally, it also includes extracting features from the grayscale image to obtain an intensity map, and the intensity map is used to highlight the pattern of the target object. In this case, the pattern of the target object can be further highlighted by the intensity map, and the image features of the low-reflectivity sample can be further enhanced.

[0016] A second aspect of the present disclosure provides a device for enhancing imaging of low-reflectivity samples, comprising an acquisition module, a weight module and a result module; the acquisition module is configured to acquire multiple channel components of the low-reflectivity sample, the multiple channel components comprising a first color channel component, a second color channel component and a third color channel component, and the low-reflectivity sample comprises multiple targets; the weight module is configured to determine a first channel component related to the color of the target, and the weight module is further configured to determine a first weight of the first color channel component, a second weight of the second color channel component and a third weight of the third color channel component based on the first channel component, wherein, among the first color channel component, the second color channel component and the third color channel component, the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component; the result module is configured to perform weighted processing on the first color channel component, the second color channel component and the third color channel component based on the first weight, the second weight and the third weight to obtain a grayscale image corresponding to the low-reflectivity sample.

[0017] In the second aspect of the present disclosure, the first channel component is determined based on the color of the target object, the color of the target object can be analyzed, and the main color presented by the target object can be determined, and the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component. Compared with the standard formula with fixed weights, the weight can be adaptively adjusted based on the color of the target object in the process of obtaining the grayscale image, which can increase the effective signal in the grayscale image, thereby improving the contrast of the grayscale image, thereby enhancing the grayscale features of the target object pattern in the grayscale image (that is, it can enhance the image features of the low reflectivity sample), which can be beneficial to improve the accuracy of subsequent image analysis using the grayscale image. In addition, since the sum of the weights of multiple channel components is determined when converting to a grayscale image, the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component, the sum of the weights corresponding to the other channel components except the first channel component can be made less than the sum of the standard weights corresponding to the other channel components, thereby reducing the noise signal in the grayscale image, improving the signal-to-noise ratio of the grayscale image, and further improving the contrast of the grayscale image.

[0018] According to the present disclosure, a method and device for enhancing imaging of low-reflectivity samples are provided, which can enhance the image characteristics of low-reflectivity samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present disclosure will now be explained in further detail, by way of example only, with reference to the accompanying drawings.

[0020] Figure 1 It is a schematic diagram showing the application scenario of the enhancement method involved in the example of the present disclosure.

[0021] Figure 2 is a flow chart showing an enhancement method involved in examples of the present disclosure.

[0022] Figure 3 is a block diagram showing the structure of an enhancement device involved in the example of the present disclosure.

[0023] Figure 4 is a flow chart showing a measurement method involved in the examples of the present disclosure.

[0024] Figure 5 Detailed description of the invention is given in detail below.

[0025] Fig. 6A 1 is a grayscale image obtained under general conditions using the existing method involved in the example of the present disclosure.

[0026] Figure 6B 2 is a grayscale image obtained under general conditions using the enhancement method involved in the example of the present disclosure.

[0027] Figure 7 is a grayscale image showing the improvement of the brightness of the light source according to the example of the present disclosure without using an enhancement method. DETAILED DESCRIPTION

[0028] Hereinafter, the preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same symbols are assigned to the same components, and repeated descriptions are omitted. In addition, the accompanying drawings are only schematic diagrams, and the ratio of the dimensions of the components or the shapes of the components may be different from the actual ones.

[0029] It should be noted that the terms "including" and "having" and any variations thereof in the present disclosure, such as a process, method, system, product or device that includes or has a series of steps or units, are not necessarily limited to those steps or units clearly listed, but may include or have other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] The present disclosure provides a method for enhancing the imaging of a low-reflectivity sample (hereinafter referred to as the enhancement method or method). The method for enhancing the imaging of a low-reflectivity sample provided by the present disclosure can improve the contrast of the grayscale image of the low-reflectivity sample, thereby enhancing the grayscale characteristics of the low-reflectivity sample (that is, enhancing the image characteristics of the low-reflectivity sample), which can help improve the accuracy of subsequent image processing using the grayscale image.

[0031] The low reflectivity sample involved in the present disclosure refers to a sample with a low reflectivity to incident light. In some examples, the reflectivity of the low reflectivity sample can be less than 30%. In some examples, the low reflectivity sample can include low-reflection glass, anti-reflection film, display screen assembly, and automobile front windshield, etc.

[0032] The multiple channel components involved in the present disclosure may be channel components in a color map. In some examples, the channel component may refer to the numerical value corresponding to the channel in the color map. In some examples, the channel component may be represented in the form of a vector, an array, a matrix or an image. In some examples, the color map is an image represented using a color model. In some examples, the color model may include an RGB (red, green and blue) model. In the RGB model, the channel components of the three colors of red (R), green (G) and blue (B) constitute an RGB image. That is, the color map may be an RGB image. In some examples, the multiple channel components may include a first color channel component, a second color channel component and a third color channel component. In other examples, the color model may include a CMY (Cyan Magenta Yellow, cyan, magenta, yellow) model, an HSV (hue, saturation, brightness) model and an HSL (hue, saturation, brightness) model, etc.

[0033] The target object involved in the present disclosure can be any object in a low-reflectivity sample. In some examples, the target object can be a microscopic structure in a low-reflectivity sample.

[0034] In some examples, a pattern of a target object having characteristics of the target object may be displayed in a grayscale image or an intensity image (described later). The pattern of the target object may also be referred to as a target object pattern. In some examples, the pattern of the target object may be any geometric figure. For example, the pattern of the target object may show the edges or boundaries of the target object.

[0035] The effective signal involved in the present disclosure may refer to a signal corresponding to the pattern of the target object. The noise signal involved in the present disclosure may refer to a signal that interferes with obtaining the effective signal.

[0036] The present disclosure also provides a low reflectivity sample imaging enhancement device, which can be used to perform the enhancement method involved in the present disclosure. The low reflectivity sample imaging enhancement device involved in the present disclosure can be simply referred to as an enhancement device or device.

[0037] Hereinafter, the enhancement method involved in the present disclosure will be described in detail with reference to the accompanying drawings.

[0038] Figure 1 It is a schematic diagram showing the application scenario of the enhancement method involved in the example of the present disclosure.

[0039] In some examples, the enhancement method disclosed herein can be used to enhance the imaging of a low reflectivity sample 10. In some examples, see Figure 1 , a collection device 20 may be used to acquire multiple channel components of the low reflectivity sample 10. In some examples, the collection device 20 may be a confocal microscope.

[0040] In some examples, the computing device 30 may be used to process multiple channel components of the low reflectivity sample 10 for imaging enhancement. Specifically, the enhancement method of the present disclosure may be used to obtain a grayscale image corresponding to the low reflectivity sample 10 based on multiple channel components of the low reflectivity sample 10. In some examples, the grayscale image may be further processed.

[0041] In some examples, the low reflectivity sample 10 may include a plurality of targets. In some examples, the target pattern may be highlighted in the grayscale image by enhancing the image features of the low reflectivity sample 10. Thus, it is possible to facilitate the subsequent determination of the parameters of the low reflectivity sample 10 using the grayscale image. In some examples, enhancing the image features of the low reflectivity sample 10 may refer to enhancing the image features in the grayscale image related to the targets in the low reflectivity sample 10. In some examples, enhancing the image features of the low reflectivity sample 10 may refer to increasing the brightness or contrast of the image features in the grayscale image.

[0042] In some examples, an image feature may refer to specific information in an image used to identify, describe, or explain an object. In some examples, when an image feature is obtained from a grayscale image, the image feature obtained from the grayscale image may also be referred to as a grayscale feature. In some examples, a grayscale feature may be used to characterize a local characteristic or a global characteristic of a grayscale image.

[0043] It should be noted that Figure 1 This is just a schematic diagram, and the application scenarios of the present disclosure are not limited thereto. In some examples, imaging enhancement can be performed on samples of any reflectivity.

[0044] Figure 2 is a flow chart showing an enhancement method involved in examples of the present disclosure.

[0045] For some examples, see Figure 2, the enhancement method may include acquiring multiple channel components of the low reflectivity sample 10 (step S100), determining a first channel component related to the color of the target object (step S200), determining multiple weights corresponding to the multiple channel components based on the first channel component (step S300), and weighting the multiple channel components based on the multiple weights to obtain a grayscale image corresponding to the low reflectivity sample 10 (step S400). In this case, determining the first channel component based on the color of the target object can analyze the color of the target object and determine the main color presented by the target object, and determining multiple weights corresponding to the multiple channel components based on the first channel component can enhance the grayscale features of the target object pattern in the grayscale image (that is, it can enhance the image features of the low reflectivity sample 10).

[0046] For some examples, see Figure 2 In step S100, a plurality of channel components of the low reflectivity sample 10 may be obtained. In some examples, the plurality of channel components may include a first color channel component, a second color channel component, and a third color channel component.

[0047] In some examples, the multiple channel components may be channel components in a color image. That is, the color image may include multiple channel components. As described above, the color image may be an RGB image. In some examples, the first color channel component may correspond to the red component of the target object. The second color channel component may correspond to the green component of the target object. The third color channel component may correspond to the blue component of the target object. In this case, the features of the image can be represented by the RGB color model. In addition, the RGB color model can intuitively display the color characteristics of the target object itself through different channel components, which can facilitate the subsequent quick determination of the channel components related to the color of the target object.

[0048] It should be noted that in some examples, the correspondence between multiple channel components and multiple color channels in the color map can be arbitrary. For example, the correspondence between the first color channel component, the second color channel component, and the third color channel component and the three color channels of red, green, and blue can be arbitrary.

[0049] In some examples, multiple channel components may also match other color models besides the RGB color model. For example, the first color channel component, the second color channel component, and the third color channel component may also match the CMY model, the HSV model, or the HSL model. For example, the first color channel component may correspond to the cyan component of the target object. The second color channel component may correspond to the magenta component of the target object. The third color channel component may correspond to the yellow component of the target object. Thus, the method of the present disclosure can be applied to different color models.

[0050] For the convenience of description, the following description is given by taking the first color channel component corresponding to the red component of the target object, the second color channel component corresponding to the green component of the target object, and the third color channel component corresponding to the blue component of the target object as an example. It should be noted that this does not limit the present disclosure, and unless there is a contradiction, the relevant description is also applicable to other color models.

[0051] As described above, the low reflectivity sample 10 may include multiple targets. In some examples, when the target is a microstructure, the acquisition device 20 for acquiring multiple channel components may be a microscope. In some examples, the microscope may include a confocal microscope. Thus, a clearer image of the target can be obtained, which is convenient for subsequent image processing.

[0052] In some examples, when acquiring multiple channel components of the low reflectivity sample 10, the contrast of the grayscale image can be improved by adjusting at least one of the brightness of the light source, adjusting the gain of the acquisition device 20 used to acquire the multiple channel components, and adjusting the exposure time of the acquisition device 20. Thus, the contrast of the obtained grayscale image can be further improved. In addition, under low light conditions, increasing the brightness of the light source can reduce noise and blur; increasing the gain, that is, increasing the degree of signal amplification, can improve the contrast of the grayscale image without increasing the brightness of the light source; increasing the exposure time of the acquisition device 20 can allow more light to enter the acquisition device 20, thereby improving the contrast of the grayscale image.

[0053] In some examples, when acquiring multiple channel components of the low reflectivity sample 10, the brightness of the light source and the exposure time of the acquisition device 20 may be adjusted first, and then the gain of the acquisition device 20 used to acquire the multiple channel components may be adjusted. Generally, the brightness of the light source and the exposure time may be adjusted when acquiring the multiple channel components, and the gain of the acquisition device 20 may be adjusted after the multiple channel components are acquired.

[0054] In some examples, in step S200, a first channel component related to the color of the target object may be determined. In some examples, the color of the target object may refer to the color of the target object in the human eye. In this case, determining the first channel component based on the color of the target object can analyze the color of the target object and determine the main color presented by the target object.

[0055] In some examples, the first channel component related to the color of the target object may refer to the channel component that is closest to the color of the target object among the multiple channel components. In some examples, a color distance function may be used to calculate the color distance between the multiple colors corresponding to the multiple channel components and the color of the target object to select the channel component that is closest to the color of the target object. The color distance may refer to the gap between two colors. In this case, when the weights of each channel component are subsequently determined based on the first channel component and weighted processing is performed, it can be helpful to highlight the target pattern in the grayscale image, thereby enhancing the image features of the low reflectivity sample 10.

[0056] In some examples, the first channel component may be determined manually. For example, a pixel point at a position may correspond to the values ​​of three channel components. In some examples, the channel component with the largest pixel average value may be manually selected as the first channel component. The pixel average value may refer to the average value of the values ​​corresponding to all pixels in the channel component. In some examples, the first channel component may be preset.

[0057] In some examples, the first channel component can be automatically determined. For example, the channel component with the largest pixel average value can be automatically selected from the first color channel component, the second color channel component, and the third color channel component as the first channel component. In some examples, a color distance function can be used to calculate the color distance between multiple colors corresponding to multiple channel components and the color of the target object to automatically determine the first channel component. In this case, the first channel component can be automatically determined, thereby improving the efficiency of obtaining a grayscale image based on three channel components.

[0058] In some examples, if the color of the target object is blue, the first channel component may be the third color channel component. In this case, when the color of the target object is blue, by using the third color channel component as the first channel component, the weight of the third color channel component can be increased, thereby increasing the signal of the target object with blue color in the grayscale image, further improving the contrast of the grayscale image, and facilitating the highlighting of the target object pattern in the grayscale image.

[0059] In some examples, in step S300, multiple weights corresponding to the multiple channel components may be determined based on the first channel component. In some examples, when the multiple channel components include a first color channel component, a second color channel component, and a third color channel component, the multiple weights may include a first weight, a second weight, and a third weight, wherein the first weight may be a weight of the first color channel component, the second weight may be a weight of the second color channel component, and the third weight may be a weight of the third color channel component.

[0060] In some examples, a first weight of a first color channel component, a second weight of a second color channel component, and a third weight of a third color channel component may be determined based on the first channel component. In some examples, among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the first channel component may be greater than the standard weight corresponding to the first channel component (described in detail later). In this case, the first channel component is determined based on the color of the target object, and the weight of the channel component corresponding to the first channel component is greater than the standard weight corresponding to the first channel component. Compared with a standard formula with fixed weights, the weight can be adaptively adjusted based on the color of the target object during the process of acquiring the grayscale image, which can increase the effective signal in the grayscale image, thereby improving the contrast of the grayscale image.

[0061] In addition, the weight corresponding to the channel component may refer to the influence of the pixel value corresponding to the channel component on the pixel value of the grayscale image when multiple channel components are converted into a grayscale image. In some examples, each channel component may correspond to a weight. The standard weight corresponding to the channel component may refer to the weight corresponding to the channel component determined by the standard formula when multiple channel components are converted into a grayscale image using a standard formula. That is, after the standard formula is determined, the standard weight corresponding to the channel component is also determined accordingly. In some examples, each channel component may correspond to a standard weight.

[0062] In some examples, in the standard formula, the standard weight of the channel component related to the color of the target object (described in detail later) is often low. Without changing the predetermined imaging conditions (for example, without increasing the gain of the acquisition device 20), the value of the channel component is generally close to or has been overexposed. When the standard formula is used to convert multiple channel components into a grayscale image, even if the value of the channel component related to the color of the target object has been overexposed, the features belonging to the channel component generally cannot be highlighted in the grayscale image.

[0063] In addition, when the standard formula is used, when the predetermined imaging conditions are changed, the values ​​of the channel components related to the color of the target object generally do not increase much, while the values ​​of other channel components corresponding to the noise generally increase more. Therefore, the signal-to-noise ratio of the grayscale image is often reduced, and the characteristics of the channel components related to the color of the target object generally cannot be highlighted.

[0064] Taking the blue surface of the low reflectivity sample 10 as an example, the value of the third color channel component is often large. In this case, when processing a target object that mainly presents blue, since the standard weight corresponding to the third color channel component in the standard formula is low, even if the third color channel component is brightened by increasing the camera gain, light source brightness or exposure time of the acquisition device 20, it is difficult to effectively increase the brightness of the target object in the grayscale image, so the effects of these adjustment methods are often poor. In addition, these methods may cause other channel components to be overexposed, thereby losing some information. Determining the first channel component based on the color of the target object and adaptively adjusting the weights of each channel component can not only increase the brightness of the target object in the grayscale image without losing information, but also cooperate with adjustment methods such as camera gain, light source brightness and exposure time to further enhance image features.

[0065] In some examples, the standard formula may be determined based on the sensitivity of the human eye to different colors. In some examples, the standard formula may also be a formula with fixed standard weights. For example, the standard formula may be a Luminance formula. The Luminance formula may be:

[0066] gray(x,y)=K 1 R(x,y)+K 2 G(x,y)+K 3 B(x,y),

[0067] Among them, x can represent the coordinate of the pixel on the x-axis, y can represent the coordinate of the pixel on the y-axis, (x,y) can represent the pixel with coordinates (x,y), gray(x,y) can represent the value of the pixel with coordinates (x,y) in the grayscale image, R(x,y) can represent the value of the pixel with coordinates (x,y) in the first color channel component, G(x,y) can represent the value of the pixel with coordinates (x,y) in the second color channel component, B(x,y) can represent the value of the pixel with coordinates (x,y) in the third color channel component, and K 1 It can represent the value of the standard weight corresponding to the first color channel component, K 2 It can represent the value of the standard weight corresponding to the second color channel component, K 3 Can represent the value of the standard weight corresponding to the third color channel component. In some examples, K 1 It can be 0.299, K 2 It can be 0.587, K 3 It can be 0.114.

[0068] As described above, the weight corresponding to the first channel component may be greater than the standard weight corresponding to the first channel component. For example, when the first channel component is the first color channel component, the weight corresponding to the first channel component may be greater than 0.299. When the first channel component is the second color channel component, the weight corresponding to the first channel component may be greater than 0.587. When the first channel component is the third color channel component, the weight corresponding to the first channel component may be greater than 0.114.

[0069] In some examples, the standard weights corresponding to the various channel components may be arbitrary values. For example, the standard weights corresponding to the first color channel component may be 0.1 to 0.9. The standard weights corresponding to the second color channel component may be 0.1 to 0.9. The standard weights corresponding to the third color channel component may be 0.1 to 0.9. In some examples, the standard weights may be 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9, etc. In some examples, the weights corresponding to the first channel component may be 0.1 to 0.9. For example, the weights corresponding to the first channel component may be 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9, etc.

[0070] In some examples, the sum of the standard weights corresponding to the various channel components may be determined. In some examples, the sum of the standard weights corresponding to the various channel components may be a fixed value. For example, the sum of the standard weights may be 1. In some examples, the sum of the standard weights may not be 1. For example, the sum of the standard weights may be 2.

[0071] In some examples, the sum of the weights corresponding to the various channel components may be determined. In some examples, the sum of the weights corresponding to the various channel components may be a fixed value. For example, the sum of the weights may be 1. In some examples, the sum of the weights may not be 1. For example, the sum of the weights may be 2.

[0072] In some examples, among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the first channel component may be the largest. In this case, when the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component, further maximizing the weight corresponding to the first channel component can further increase the effective signal of the grayscale image.

[0073] In some examples, a second channel component related to the color of the noise may be determined, and the first weight, the second weight, and the third weight may be determined based on the first channel component and the second channel component. The color of the noise may refer to the color of the noise in the human eye.

[0074] In some examples, among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the second channel component may be less than the weight corresponding to the first channel component. In this case, the weight of the channel component corresponding to the noise signal can be controlled within a range less than the weight of the channel component corresponding to the valid signal, thereby further improving the signal-to-noise ratio of the grayscale image.

[0075] In some examples, the weight corresponding to the second channel component may be smaller than the standard weight corresponding to the second channel component, thereby reducing the weight of the channel component corresponding to the noise signal.

[0076] In some examples, the method for determining the second channel component may be the same as the method for determining the first channel component. For example, a channel component related to the color of noise may be used as the second channel component.

[0077] In some examples, the color of the target object may be blue. In some examples, when the surface of the low reflectivity sample 10 may be blue, the color of the target object may be blue.

[0078] In some examples, when the surface of the low-reflectivity sample 10 is not blue, the color of the target object may also be blue. Thus, when the surface color of the low-reflectivity sample 10 is not blue, the target object with a blue color can be highlighted.

[0079] In some examples, the color of the target object may be blue, and the first channel component may be the third color channel component. In this case, when the color of the target object is blue, by using the third color channel component as the first channel component, the weight of the third color channel component can be increased, thereby increasing the signal of the target object with blue color in the grayscale image, further improving the contrast of the grayscale image, and facilitating the highlighting of the target object pattern in the grayscale image.

[0080] In some examples, the formula associated with the standard weight may be a standard formula, and the third weight may be 0.2 to 1. Thus, the contrast of the grayscale image can be improved. The specific effect can be seen in the experimental results below.

[0081] As described above, the weight corresponding to the first channel component may be maximized. In some examples, when the first channel component is a third color channel component, the third weight may be maximized. In this case, when the third weight is greater than the standard weight corresponding to the third color channel component, further maximizing the third weight can further increase the signal about the target object in the grayscale image.

[0082] In some examples, the third weight may be greater than the first weight, and the third weight may be greater than the second weight. That is, the third weight may be the largest. In some examples, when the first channel component is the third color channel component, the third weight may be greater than the first weight, and the third weight may be greater than the second weight. In this case, the signal of the target object whose color is closest to the color corresponding to the third color channel component in the grayscale image can be further increased.

[0083] In some examples, when the first channel component is a third color channel component, the ratio of the first weight to the second weight may be equal to the ratio of the standard weight of the first color channel component to the standard weight of the second color channel component. In some examples, the ratio of the first weight to the second weight may be substantially the same as the ratio of the standard weight of the first color channel component to the standard weight of the second color channel component. In some examples, the ratio of the first weight to the second weight may be 2:1 to 3:1. In this case, the first weight and the second weight can be adjusted based on the sensitivity of the human eye to different colors to highlight the target object.

[0084] In some examples, in step S400 , a plurality of channel components may be weighted based on a plurality of weights to obtain a grayscale image corresponding to the low reflectivity sample 10 .

[0085] In some examples, the first color channel component, the second color channel component, and the third color channel component may be weighted based on the first weight, the second weight, and the third weight to obtain a grayscale image corresponding to the low reflectance sample 10. For example, the standard weights corresponding to the various channel components in the standard formula may be replaced with the first weight, the second weight, and the third weight, and the first color channel component, the second color channel component, and the third color channel component may be weighted using the formula with the replaced weights to obtain a grayscale image corresponding to the low reflectance sample 10.

[0086] In some examples, the grayscale image may include texture features of the low reflectivity sample 10. In some examples, the texture features of the low reflectivity sample 10 may include pattern features of the target object. In some examples, the value of a parameter related to the texture feature may be obtained based on the grayscale image.

[0087] In some examples, the grayscale image can be used to determine the parameters of the low reflectivity sample 10. In some examples, the parameters of the low reflectivity sample 10 may include at least one of the total area of ​​the target, the number of targets, the width of the target, the height of the target, and the density of the target. In this case, the grayscale image is used to determine the parameters of the low reflectivity sample 10, and after enhancing the image features of the low reflectivity sample 10, the accuracy of the parameters can be improved. In addition, the grayscale image is used to determine the parameters of the low reflectivity sample 10, and at least one of the quantity characteristics and shape characteristics of the target can be determined, so that it can be judged whether the low reflectivity sample 10 meets the use requirements.

[0088] In some examples, the enhancement method may further include extracting features from the grayscale image to obtain an intensity image. The intensity image may be used to highlight the pattern of the target object. In this case, the pattern of the target object can be further highlighted by the intensity image, and the image features of the low reflectivity sample 10 can be further enhanced.

[0089] In some examples, the intensity map can be used for the parameters of the low reflectivity sample 10. In some examples, the parameters of the low reflectivity sample 10 can also include the location of the target object. In this case, the features associated with the target object in the grayscale image can be extracted through the intensity map, and then the location and number of the target object can be determined according to the intensity map, so that the parameters of the low reflectivity sample 10 can be determined by statistics based on the intensity map.

[0090] In some examples, the method of extracting features from a grayscale image may include at least one of a deep learning method, a histogram equalization, an edge detection method, a wavelet transform, a local binary pattern, and a gray-level co-occurrence matrix.

[0091] In some examples, a pseudo color image can be obtained based on a grayscale image. A pseudo color image can be an image obtained by mapping the value of each pixel of the grayscale image through a certain function algorithm. In a pseudo color image, each gray level is mapped to a specific color in a color space. In some examples, a pseudo color image can be used to highlight the texture features of a low reflectivity sample 10. In some examples, a pseudo color image can be used to represent the value of a parameter related to the texture features. Thus, the visualization effect of the texture features of the low reflectivity sample 10 can be enhanced.

[0092] As described above, the present disclosure further provides an enhancement device 100. The enhancement device 100 involved in the present disclosure can be used to execute the enhancement method described above.

[0093] Figure 3 is a block diagram showing the structure of the enhancement device 100 involved in the example of the present disclosure.

[0094] For some examples, see Figure 3The enhancement device 100 may include an acquisition module 110 , a weight module 120 and a result module 130 .

[0095] In some examples, the acquisition module 110 can be configured to acquire multiple channel components of the low reflectivity sample 10. In some examples, the multiple channel components can include a first color channel component, a second color channel component, and a third color channel component. In some examples, the acquisition module 110 can include an acquisition device 20, such as a microscope. The microscope can include a confocal microscope.

[0096] In some examples, the weight module 120 may be configured to determine a first channel component associated with the color of the target object of the low reflectance sample 10. The weight module 120 may also be configured to determine a first weight of a first color channel component, a second weight of a second color channel component, and a third weight of a third color channel component based on the first channel component, wherein, among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the first channel component may be greater than a standard weight corresponding to the first channel component.

[0097] In some examples, the result module 130 may be configured to perform weighted processing on the first color channel component, the second color channel component, and the third color channel component based on the first weight, the second weight, and the third weight to obtain a grayscale image corresponding to the low reflectance sample 10 .

[0098] The present disclosure also provides a measurement method, which is a measurement method for measuring parameters of a low reflectivity sample 10. The measurement method may include obtaining a grayscale image corresponding to the low reflectivity sample 10 using the enhancement method described above, and further processing the grayscale image.

[0099] Figure 4 is a flow chart showing a measurement method involved in the examples of the present disclosure.

[0100] For some examples, see Figure 4 The measurement method may include acquiring multiple channel components of the low-reflectivity sample 10 (step S600), obtaining a grayscale image corresponding to the low-reflectivity sample 10 based on the multiple channel components (step S700), performing feature extraction on the grayscale image to obtain an intensity image (step S800), and determining parameters of the low-reflectivity sample 10 based on the intensity image (step S900).

[0101] For step S600, reference may be made to the relevant description in step S100. For step S700, reference may be made to the relevant description in steps S200, S300 and S400.

[0102] In some examples, in step S800, feature extraction may be performed on the grayscale image to obtain an intensity image. The intensity image may be used to determine the parameters of the low reflectivity sample 10. For details about the intensity image, please refer to the above description.

[0103] In some examples, in step S900, the parameters of the low reflectivity sample 10 can be determined based on the intensity map. As described above, the parameters of the low reflectivity sample 10 can include at least one of the total area of ​​the target, the number of targets, the width of the target, the height of the target, and the density of the target. In this case, the number characteristics and shape characteristics of the target can be determined, so that it can be determined whether the low reflectivity sample 10 meets the use requirements.

[0104] As described above, the target object may be a microstructure. In some examples, the height of the target object may be calculated based on the width of the target object and the microstructure. In some examples, the width of the target object may be proportional to the height of the target object, and when the width of the target object is obtained, the height of the target object may be calculated.

[0105] In some examples, a pseudo color image can also be obtained based on the grayscale image. The content related to the pseudo color image can refer to the above description. Thus, the visualization effect of the texture features of the low reflectivity sample 10 can be enhanced.

[0106] The present disclosure also provides a measuring device 200, which can be used to execute the measuring method described above.

[0107] Figure 5 2 is a block diagram showing the structure of a measuring device 200 according to an example of the present disclosure.

[0108] For some examples, see Figure 5 , the measuring device 200 may include a channel component acquisition module 210, a grayscale image acquisition module 220, and a processing module 230. In some examples, the channel component acquisition module 210 may be configured to acquire multiple channel components of the low reflectivity sample 10. The grayscale image acquisition module 220 may be configured to obtain a grayscale image corresponding to the low reflectivity sample 10 using the enhancement method described above. The processing module 230 may be configured to further process the grayscale image using the measurement method described above.

[0109] In order to verify the effectiveness of the enhancement method disclosed in the present invention, an experiment was conducted to demonstrate the enhancement method disclosed in the present invention. The following is a detailed description of the experiment.

[0110] In this experiment, the low reflectivity sample 10 is a sample that mainly presents blue and has multiple targets arranged on the surface, and a confocal microscope is used to obtain multiple channel components of the low reflectivity sample 10. In the low reflectivity sample 10, the color of the target is blue, and the third color channel component is used as the first channel component. The existing method used in this experiment refers to a method of converting three channel components into a grayscale image using a standard formula. The general conditions used in this experiment refer to the conditions where the brightness of the light source is not adjusted, the gain of the acquisition device 20 is not adjusted, and the exposure time of the acquisition device 20 is not adjusted.

[0111] Fig. 6A 1 is a grayscale image obtained under general conditions using the existing method involved in the example of the present disclosure. Figure 6B 2 is a grayscale image obtained under general conditions using the enhancement method involved in the example of the present disclosure. Fig. 6A and Figure 6B Different grayscale images are obtained by processing the three channel components of the same low reflectivity sample 10 using different methods.

[0112] See also Fig. 6A and Figure 6B , you can see, Figure 6B The contrast ratio is obviously Fig. 6A high, Figure 6B The pattern features of the target object in the image are more obvious. This shows that the enhancement method involved in the present disclosure can enhance the image features of the low-reflectivity sample 10. Using the enhancement method involved in the present disclosure, the standard deviation of the parameters of the low-reflectivity sample 10 measured is smaller, which shows that the measurement results using the enhancement method are more stable.

[0113] Figure 7 is a grayscale image showing the improvement of the brightness of the light source according to the example of the present disclosure without using an enhancement method.

[0114] See also Figure 6B and Figure 7 It can be seen that even if the brightness of the light source is not increased, the enhancement method involved in the present disclosure can effectively increase the brightness of the grayscale image and enhance the image features. In this case, the impact of image overexposure caused by excessive brightness of the light source can be reduced. In addition, the power used by the light source will affect the service life of the light source. The higher the power used by the light source, the shorter the service life of the light source. By using the enhancement method involved in the present disclosure, it is possible to obtain an image with higher brightness at a relatively low light source brightness, thereby effectively extending the life of the light source.

[0115] In the enhancement method disclosed in the present disclosure, a first channel component related to the color of the target object in the low reflectivity sample 10 is determined, and a first weight of the first color channel component, a second weight of the second color channel component, and a third weight of the third color channel component are determined based on the first channel component, wherein, among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component. In this case, the first channel component is determined based on the color of the target object, the color of the target object can be analyzed, and the main color presented by the target object can be determined, and the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component. Compared with the standard formula with fixed weights, the weight can be adaptively adjusted based on the color of the target object in the process of obtaining the grayscale image, the effective signal in the grayscale image can be increased, and then the contrast of the grayscale image can be improved, so that the grayscale features of the target object pattern in the grayscale image can be enhanced (that is, the image features of the low reflectivity sample 10 can be enhanced), which can be beneficial to improve the accuracy of subsequent image analysis using the grayscale image. In addition, since the sum of the weights of multiple channel components is determined when converting to a grayscale image, the weight corresponding to the first channel component is greater than the standard weight corresponding to the first channel component, the sum of the weights corresponding to the channel components other than the first channel component can be made smaller than the sum of the standard weights corresponding to the other channel components, thereby reducing the noise signal in the grayscale image, improving the signal-to-noise ratio of the grayscale image, and further improving the contrast of the grayscale image.

[0116] Although the present disclosure is specifically described above in conjunction with the accompanying drawings and examples, it is to be understood that the above description does not limit the present disclosure in any form. Those skilled in the art may modify and change the present disclosure as needed without departing from the essential spirit and scope of the present disclosure, and these modifications and changes all fall within the scope of the present disclosure.

Claims

1. A method for enhancing imaging of low reflectivity samples, characterized in that: include: Acquire multiple channel components of the low reflectivity sample, wherein the multiple channel components include a first color channel component, a second color channel component, and a third color channel component, and the low reflectivity sample includes multiple targets; determining a first channel component associated with the color of the target object; Determine, based on the first channel component, a first weight of the first color channel component, a second weight of the second color channel component, and a third weight of the third color channel component, wherein, among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the first channel component is greater than a standard weight corresponding to the first channel component; and The first color channel component, the second color channel component and the third color channel component are weighted based on the first weight, the second weight and the third weight to obtain a grayscale image corresponding to the low reflectance sample.

2. The method for enhancing imaging of low reflectivity samples according to claim 1, characterized in that: Determine a second channel component related to the color of noise, and determine the first weight, the second weight, and the third weight based on the first channel component and the second channel component, wherein, among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the second channel component is less than the weight corresponding to the first channel component.

3. The method for enhancing imaging of low reflectivity samples according to claim 1, characterized in that: Among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the first channel component is the largest.

4. The method for enhancing imaging of low reflectivity samples according to claim 1, characterized in that: The first color channel component corresponds to a red component of the target object, the second color channel component corresponds to a green component of the target object, and the third color channel component corresponds to a blue component of the target object.

5. The method for enhancing imaging of low reflectivity samples according to claim 1, characterized in that: The formula associated with the standard weight is a standard formula, and the third weight is 0.2 to 1.

6. The method for enhancing imaging of low reflectivity samples according to claim 1 or 4, characterized in that: The third weight is greater than the first weight, and the third weight is greater than the second weight.

7. The method for enhancing imaging of low reflectivity samples according to claim 1, characterized in that: When acquiring multiple channel components of the low reflectivity sample, the contrast of the grayscale image is improved by at least one of adjusting the brightness of the light source, adjusting the gain of the acquisition device used to acquire the multiple channel components, and adjusting the exposure time of the acquisition device.

8. The method for enhancing imaging of low reflectivity samples according to claim 1, characterized in that: The grayscale image is used to determine the parameters of the low reflectivity sample, and the parameters of the low reflectivity sample include at least one of the total area of ​​the target objects, the number of the target objects, the width of the target objects, the height of the target objects and the density of the target objects.

9. The method for enhancing imaging of low reflectivity samples according to claim 1 or 8, characterized in that: The method also includes extracting features from the grayscale image to obtain an intensity image, where the intensity image is used to highlight the pattern of the target object.

10. A device for enhancing imaging of low reflectivity samples, characterized in that: Includes acquisition module, weight module and result module; The acquisition module is configured to acquire multiple channel components of the low reflectivity sample, the multiple channel components include a first color channel component, a second color channel component and a third color channel component, and the low reflectivity sample includes multiple targets; The weight module is configured to determine a first channel component related to the color of the target object, and the weight module is further configured to determine a first weight of the first color channel component, a second weight of the second color channel component, and a third weight of the third color channel component based on the first channel component, wherein, among the first color channel component, the second color channel component, and the third color channel component, the weight corresponding to the first channel component is greater than a standard weight corresponding to the first channel component; The result module is configured to perform weighted processing on the first color channel component, the second color channel component and the third color channel component based on the first weight, the second weight and the third weight to obtain a grayscale image corresponding to the low reflectance sample.

Images