Noise extraction method, removal method, radiation dose rate detection method, device and medium in gamma radiation environment

Abstract

The disclosure provides a noise extraction method, a noise removal method, a radiation dose rate detection method, a device and a medium in a gamma radiation environment, the method comprising: stacking two identical image sensors to obtain a stacked structure comprising an upper image sensor and a lower image sensor, and configuring a lens for the upper image sensor; collecting bright-field images under different gamma radiation dose rate environments through the upper image sensor with the lens to obtain a bright-field image corresponding to each gamma radiation dose rate; collecting dark-field images under different gamma radiation dose rate environments through the lower image sensor to obtain a dark-field image corresponding to each gamma radiation dose rate; processing each dark-field image respectively to determine the number of noise points corresponding to each gamma radiation dose rate, and determining the number of noise points as the value of the gamma radiation dose rate. The method can realize noise extraction, removal and radiation dose rate detection without affecting the normal image collection function of the image sensor.

Description

Technical Field

[0001] This disclosure relates to the field of video surveillance technology in gamma radiation environments, and in particular to a method for noise extraction, removal, radiation dose rate detection, equipment, and medium in a gamma radiation environment. Background Technology

[0002] With the rapid development of the nuclear industry, the supporting equipment for nuclear operation and maintenance and nuclear emergency response is also constantly being upgraded. Among these upgrades, miniaturization, lightweighting, and intelligentization have become the development directions. In this process, video surveillance equipment with image acquisition capabilities plays an important role. They can acquire, transmit, and store image information from nuclear industrial sites in real time, assisting in operation and maintenance and emergency response work.

[0003] However, current video surveillance systems have limitations; video acquisition devices cannot simultaneously perform image acquisition and radiation dose rate detection. Radiation dose rate is a crucial indicator of radiation levels and is essential for safety monitoring in the nuclear industry environment. Therefore, developing a video surveillance system capable of simultaneously acquiring images and detecting radiation dose rate is urgently needed. Summary of the Invention

[0004] In view of this, the present disclosure provides a method for noise extraction, removal, radiation dose rate detection, equipment, and medium under gamma radiation environment to solve the above-mentioned technical problems.

[0005] This disclosure provides a method for noise extraction from an image sensor under gamma radiation conditions, comprising: stacking two identical image sensors to obtain a stacked structure consisting of an upper image sensor and a lower image sensor, and configuring a lens for the upper image sensor; acquiring a bright field image under gamma radiation conditions through the upper image sensor with the lens, and acquiring a dark field image under gamma radiation conditions through the lower image sensor; processing the dark field image to extract noise points generated on the image sensor under gamma radiation conditions, wherein the noise points are used to provide noise distribution information for the bright field image.

[0006] According to embodiments of this disclosure, acquiring a bright field image under gamma radiation environment using an upper image sensor with a lens includes: acquiring a first RAW image under gamma radiation environment using the upper image sensor, and converting the first RAW image into a grayscale image to obtain a bright field image; acquiring a dark field image under gamma radiation environment using a lower image sensor includes: acquiring a second RAW image under gamma radiation environment using the lower image sensor, and converting the second RAW image into a grayscale image to obtain a dark field image.

[0007] According to embodiments of this disclosure, processing a dark field image to extract noise points generated on an image sensor under gamma radiation includes: traversing the grayscale values ​​of each row of pixels in the dark field image using a mean-based constant false alarm rate (CFAR) detection algorithm, detecting pixels in each row whose grayscale values ​​are higher than a threshold, and obtaining peak points, wherein the threshold is determined based on the expected false alarm probability; and identifying the peak points as noise points.

[0008] According to embodiments of this disclosure, a mean-based constant false alarm rate (CFAR) detection algorithm is used to traverse the grayscale values ​​of each row of pixels in a dark field image and detect pixels in each row whose grayscale values ​​are higher than a threshold. This includes: setting a traversal window size and traversing each row of pixels through the traversal window; for each pixel contained in the current traversal window, calculating the ratio between the grayscale value of each pixel and the average grayscale value of all pixels in the current traversal window to obtain the standardized grayscale value of each pixel; and determining the pixel as a peak point in response to the standardized grayscale value of a pixel being greater than the threshold.

[0009] According to embodiments of this disclosure, the method further includes: reducing the integration time of the image sensor so that the bright field image and the dark field image retain noise points with gray values ​​greater than a gray value threshold.

[0010] According to embodiments of this disclosure, the upper image sensor and the lower image sensor are aligned using mechanical holes.

[0011] Another aspect of this disclosure provides a method for noise removal from an image sensor under gamma radiation, comprising:

[0012] The above-mentioned image sensor noise extraction method under gamma radiation environment is used to extract noise points generated on the image sensor under gamma radiation environment; based on the mapping relationship between pixels between the lower image sensor and the upper image sensor, noise removal is performed on the bright field image according to the noise points.

[0013] This disclosure also provides a method for detecting radiation dose rate under gamma radiation conditions, comprising: stacking two identical image sensors to obtain a stacked structure consisting of an upper image sensor and a lower image sensor, and configuring a lens for the upper image sensor; acquiring bright field images under different gamma radiation dose rate conditions through the upper image sensor with the lens to obtain a bright field image corresponding to each gamma radiation dose rate; acquiring dark field images under different gamma radiation dose rate conditions through the lower image sensor to obtain a dark field image corresponding to each gamma radiation dose rate; processing each dark field image to determine the number of noise points corresponding to each gamma radiation dose rate, and determining the number of noise points as the value of the gamma radiation dose rate, wherein the noise points determined based on each dark field image are used to provide noise distribution information for bright field images acquired at the same time.

[0014] This disclosure also provides an electronic device, including: one or more processors and an image sensor; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the above-described method.

[0015] In another aspect, this disclosure provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the above-described method.

[0016] The noise extraction method, removal method, radiation dose rate detection method, equipment, and medium provided in the embodiments of this disclosure under gamma radiation environment have at least the following beneficial effects:

[0017] By setting up an upper and lower image sensor stack, the upper image sensor is used to acquire bright field images normally, while the lower image sensor is used to simultaneously acquire dark field images for noise extraction and radiation dose rate detection. By detecting radiation noise and the pixel mapping relationship between the two image sensors, the noise distribution information is mapped from the lower to the upper part of the stacked structure to remove noise from the image acquired by the bright field image sensor. Thus, noise extraction, removal, and radiation dose rate detection are completed without affecting the normal image acquisition function of the image sensor. Attached Figure Description

[0018] The relationships between the various features of this disclosure are further illustrated below with reference to the accompanying drawings. The drawings are exemplary; some features are not shown to scale, and some drawings may omit conventional features in the field of this disclosure that are not essential to it, or additional features that are not essential to this disclosure may be shown. The combination of features shown in the drawings is not intended to limit this disclosure. Furthermore, throughout this specification, the same reference numerals refer to the same things. Specific descriptions of the drawings are as follows:

[0019] Figure 1 A flowchart illustrating a method for noise extraction from an image sensor under gamma radiation according to an embodiment of the present disclosure is shown.

[0020] Figure 2 A schematic diagram of a stacked structure of two image sensors according to an embodiment of the present disclosure is shown.

[0021] Figure 3 A flowchart illustrating an image sensor noise removal method under gamma radiation according to an embodiment of the present disclosure is shown schematically.

[0022] Figure 4 A flowchart illustrating a method for detecting radiation dose rate in a gamma radiation environment according to an embodiment of the present disclosure is shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0024] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] In this disclosure, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0026] In the description of this disclosure, it should be understood that the terms "longitudinal", "length", "circumferential", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the subsystem or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure.

[0027] Throughout the accompanying drawings, identical elements are represented by the same or similar reference numerals. Conventional structures or constructions have been omitted where they may cause confusion in understanding this disclosure. Furthermore, the shapes, dimensions, and positional relationships of the components in the drawings do not reflect actual size, scale, or actual positional relationships. Additionally, any reference numerals placed between parentheses in the claims should not be construed as limiting the claims.

[0028] Similarly, to simplify this disclosure and aid in understanding one or more of the various aspects of the disclosure, in the above description of exemplary embodiments of the present disclosure, various features of the present disclosure are sometimes grouped together in a single embodiment, figure, or description thereof. The use of terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refers to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present disclosure. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0029] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this disclosure, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0030] Figure 1 A flowchart illustrating a method for noise extraction from an image sensor under gamma radiation according to an embodiment of the present disclosure is shown.

[0031] like Figure 1 As shown, the image sensor noise extraction method under γ radiation environment may include, for example, operations S110 to S130.

[0032] In operation S110, two identical image sensors are stacked to obtain a stacked structure consisting of an upper image sensor and a lower image sensor, and a lens is configured for the upper image sensor.

[0033] Figure 2 A schematic diagram of a stacked structure of two image sensors according to an embodiment of the present disclosure is shown.

[0034] like Figure 2 As shown, the two identical image sensors can be understood as two image sensors of the same model, with the upper one being the bright field image sensor and the lower one being the dark field image sensor. The upper and lower image sensors can be precisely aligned using mechanical holes, and both sensors simultaneously acquire images using an image processing chip.

[0035] During operation of S120, bright field images under gamma radiation environment are acquired by the upper image sensor with lens, and dark field images under gamma radiation environment are acquired by the lower image sensor.

[0036] In embodiments of this disclosure, acquiring a bright field image under gamma radiation environment using an upper image sensor with a lens may include: acquiring a first RAW image under gamma radiation environment using the upper image sensor, converting the first RAW image into a grayscale image, and obtaining a bright field image.

[0037] Acquiring dark-field images under gamma radiation conditions using a lower image sensor includes: acquiring a second RAW image under gamma radiation conditions using a lower image sensor, converting the second RAW image into a grayscale image, and obtaining the dark-field image.

[0038] In operation S130, the dark field image is processed to extract noise points generated on the image sensor under gamma radiation.

[0039] In the embodiments of this disclosure, noise points extracted from dark-field images are used to provide noise distribution information for bright-field images. A mean-based constant false alarm rate (CA-CFAR) detection algorithm can be used to traverse the grayscale values ​​of each row of pixels in the dark-field image, detect pixels in each row whose grayscale values ​​are higher than a threshold, obtain peak points, and identify these peak points as noise points.

[0040] The CA-CFAR detection algorithm process is as follows:

[0041] First, set the size of the traversal window, and then traverse the pixels of each row through the traversal window.

[0042] You can use a fixed-size rectangular window, such as 3x3 or 5x5, to traverse the window, or use an adaptive window depending on the specific application.

[0043] Then, for each pixel contained in the current traversal window, the ratio between the gray value of each pixel and the average gray value of all pixels in the current traversal window is calculated to obtain the standardized gray value of each pixel.

[0044] For example, suppose the traversal window currently contains N pixels, calculate the sum of the grayscale values ​​of all pixels in the traversal window:

[0045]

[0046] Where S represents the sum of the gray values ​​of all pixels, and P(i) is the gray value of the i-th pixel.

[0047] Calculate the average gray value of the pixels in the traversal window:

[0048] MB = S / N

[0049] Where MB is the average gray value of the pixels in the traversal window.

[0050] For the current pixel T, calculate its normalized gray value:

[0051] NG = P(T) / MB

[0052] Where P(T) is the gray value of the current pixel T, and NG is the normalized gray value of the current pixel T.

[0053] Finally, if the standardized gray value of a pixel is greater than a threshold, the pixel is determined to be a peak point.

[0054] A threshold Thr can be set based on the expected false alarm probability. If NG is greater than Thr, it indicates that the current pixel T is a peak point; otherwise, it indicates that the current pixel T is a background pixel.

[0055] Based on the above embodiments, the noise extraction method further includes: reducing the integration time of the image sensor to retain noise points with gray values ​​greater than a gray value threshold in both bright and dark field images. In other words, a lower integration time value is set for the image sensor to detect noise points with larger gray values ​​by comparing noise points in the upper and lower image sensors. Subsequently, the characteristic relationships of these noise points can be used to map from the lower image sensor to the upper image sensor to remove noise from the acquired real image.

[0056] Based on the same inventive concept, embodiments of this disclosure also provide a method for noise removal from an image sensor under gamma radiation.

[0057] Figure 3 A flowchart illustrating an image sensor noise removal method under gamma radiation according to an embodiment of the present disclosure is shown schematically.

[0058] like Figure 3 As shown, the image sensor noise removal method under γ radiation environment may include, for example, operations S310 to S320.

[0059] In operation S310, noise points generated on the image sensor under gamma radiation are extracted.

[0060] In embodiments of this disclosure, the following methods may be employed: Figure 1 The image sensor noise extraction method shown extracts noise points generated on the image sensor under gamma radiation. For specific extraction details, please refer to [link to relevant documentation]. Figure 1 The embodiments shown are not described in detail here.

[0061] In operation S320, noise removal is performed on the bright field image based on the mapping relationship between pixels between the lower image sensor and the upper image sensor, according to the noise points.

[0062] For example, the positions of noise points in the bright field image acquired by the upper image sensor and the positions of noise points in the dark field image acquired by the lower image sensor can correspond. After obtaining the noise points based on the dark field image, the noise distribution information of the bright field image can be obtained based on this mapping relationship, and then the noise in the bright field image can be removed.

[0063] Based on the same inventive concept, embodiments of this disclosure also provide a method for detecting radiation dose rate under gamma radiation environment.

[0064] Figure 4 A flowchart illustrating a method for detecting radiation dose rate in a gamma radiation environment according to an embodiment of the present disclosure is shown.

[0065] like Figure 4 As shown, the radiation dose rate detection method under gamma radiation environment may include, for example, operations S410 to S440.

[0066] In operation S410, two identical image sensors are stacked to obtain a stacked structure consisting of an upper image sensor and a lower image sensor, and a lens is configured for the upper image sensor.

[0067] During operation of S420, bright field images under different γ radiation dose rates are acquired by an upper image sensor with a lens, resulting in a bright field image corresponding to each γ radiation dose rate.

[0068] During operation of S430, dark field images under different γ radiation dose rates are acquired through the lower image sensor to obtain a dark field image corresponding to each γ radiation dose rate.

[0069] In operation S440, each dark field image is processed to determine the number of noise points corresponding to each γ radiation dose rate, and the number of noise points is determined as the value of the γ radiation dose rate.

[0070] The noise points determined based on each dark field image are used to provide noise distribution information for bright field images acquired at the same time.

[0071] For example, two image sensors are placed in an environment with a gamma radiation dose rate of 3 rad(Si) / s, and raw images are simultaneously acquired under the control of a host computer. The acquired 1920*1080 resolution dark field raw images are converted into grayscale images. The CA-CFAR window is set to a size of 5*5, and the threshold is set to 1.5 times the average value of the window for noise points. All pixels of the 1920*1080 resolution image are traversed to obtain the number of noise points in that frame.

[0072] Two image sensors were placed at dose rates ranging from 3 rad(Si) / s to 50 rad(Si) / s, and the above operation was repeated to obtain a curve showing the relationship between dose rate and the number of radiation noise points. Based on this curve, the value of each gamma radiation dose rate can be intuitively obtained.

[0073] It should be noted that some details in this method are not fully covered. Figure 1 The embodiments shown are the same, and will not be described in detail again.

[0074] According to embodiments of this disclosure, the above method sets up an upper image sensor and a lower image sensor in a stack. The upper image sensor is used to normally acquire bright field images, while the lower image sensor is used to simultaneously acquire dark field images for noise extraction and radiation dose rate detection. By detecting radiation noise and the pixel mapping relationship between the two image sensors, the noise distribution information is mapped from the lower part of the stacked structure to the upper part to remove noise from the image acquired by the bright field image sensor. Thus, noise extraction, removal, and radiation dose rate detection are completed without affecting the normal image acquisition function of the image sensor.

[0075] The specific embodiments described above further illustrate the purpose, technical solutions, and beneficial effects of this disclosure. It should be understood that the above are merely preferred embodiments and the technical principles applied. Those skilled in the art will understand that this disclosure is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this disclosure. Therefore, although this disclosure has been described in detail through the above embodiments, this disclosure is not limited to the above embodiments. More other equivalent embodiments may be included without departing from the concept of this disclosure, all of which fall within the scope of protection of this disclosure.

Claims

1. A method for noise extraction from an image sensor under gamma radiation, comprising: Two identical image sensors are stacked together and aligned with the upper and lower image sensors through mechanical holes to obtain a stacked structure consisting of the upper and lower image sensors, and a lens is configured for the upper image sensor. Bright field images under gamma radiation are acquired by the upper image sensor with a lens, and dark field images under gamma radiation are acquired by the lower image sensor. The dark field image is processed to extract noise points generated on the image sensor under gamma radiation, wherein the noise points are used to provide noise distribution information for the bright field image.

2. The method for extracting noise from an image sensor under gamma radiation environment according to claim 1, wherein: The acquisition of bright-field images under gamma radiation environment by the upper image sensor with a lens includes: The upper image sensor acquires a first RAW image under γ-radiation environment, and converts the first RAW image into a grayscale image to obtain the bright field image. The acquisition of dark-field images under gamma radiation environment via the lower image sensor includes: The lower image sensor acquires a second RAW image under gamma radiation conditions, and the second RAW image is converted into a grayscale image to obtain the dark field image.

3. The method for extracting noise from an image sensor under gamma radiation environment according to claim 2, wherein, The process of processing the dark field image to extract noise points generated on the image sensor under gamma radiation includes: The mean-based constant false alarm detection algorithm iterates through the gray values ​​of each row of pixels in the dark field image, detects the pixels in each row whose gray values ​​are higher than a threshold, and obtains the peak points. The threshold is determined based on the expected false alarm probability. The peak point is identified as a noise point.

4. The method for extracting noise from an image sensor under gamma radiation environment according to claim 3, wherein, The step of using a mean-based constant false alarm rate (CFAR) detection algorithm to iterate through the grayscale values ​​of each row of pixels in the dark field image and detect pixels in each row whose grayscale values ​​are higher than a threshold includes: Set the size of the traversal window, and traverse the pixels of each row through the traversal window; For each pixel contained in the current traversal window, calculate the ratio between the gray value of each pixel and the average gray value of all pixels in the current traversal window to obtain the standardized gray value of each pixel. In response to the standardized grayscale value of the pixel being greater than the threshold, the pixel is determined to be the peak point.

5. The method for extracting noise from an image sensor under gamma radiation environment according to claim 1 or 2, further comprising: Reduce the integration time of the image sensor so that the bright field image and the dark field image retain noise points with gray values ​​greater than the gray value threshold.

6. A method for noise removal from an image sensor under gamma radiation, comprising: The method described in any one of claims 1-5 is used to extract noise points generated on the image sensor under gamma radiation. Based on the pixel mapping relationship between the lower image sensor and the upper image sensor, noise removal is performed on the bright field image according to the noise points.

7. A method for detecting radiation dose rate under gamma radiation environment, comprising: Two identical image sensors are stacked together and aligned with the upper and lower image sensors through mechanical holes to obtain a stacked structure consisting of the upper and lower image sensors, and a lens is configured for the upper image sensor. Bright field images under different γ radiation dose rates are acquired by the upper image sensor with a lens, and a bright field image corresponding to each γ radiation dose rate is obtained. The lower image sensor acquires dark field images under different γ radiation dose rates to obtain a dark field image corresponding to each γ radiation dose rate. Each dark field image is processed to determine the number of noise points corresponding to each gamma radiation dose rate. The number of noise points is then determined as the value of the gamma radiation dose rate. The noise points determined based on each dark field image are used to provide noise distribution information for the bright field images acquired at the same time.

8. An electronic device, comprising: One or more processors and an image sensor; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 7.

9. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 7.

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