Noise detection method and device, electronic equipment and storage medium

Abstract

The invention relates to a noise detection method and device, electronic equipment and a storage medium, and relates to the technical field of noise detection, and the method comprises the steps: obtaining a plurality of first images collected by an image sensor; according to the plurality of first images, determining noise data corresponding to the positions of the same pixel points in the plurality of first images; performing clustering processing on the plurality of pieces of noise data to obtain a plurality of target clustering sets; and carrying out random telegraph signal RTS noise detection on the image sensor according to the plurality of target cluster sets. Therefore, each first image does not need to be analyzed and processed, only the noise data corresponding to each pixel position needs to be determined, and then the RTS noise performance of the image sensor is evaluated according to the noise data, so that the calculation amount and the calculation complexity are greatly reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of noise detection, and in particular to a noise detection method, device, electronic device and storage medium. Background Art

[0002] With the rapid development of integrated circuits, the size of semiconductor devices is getting smaller and smaller, making the gate oxide layer of the metal oxide (MOS) tube in the image sensor prone to defects. This defect often manifests as a low-frequency noise from the circuit, namely RTS (Random Telegraph Signal) noise. The form of RTS noise is similar to that of a telegraph signal, and it is a noise that greatly affects the dark field quality of the image. Since RTS noise not only has ups and downs on the time axis, but also is not evenly distributed in the pixel space of the image sensor, it is often difficult to detect it through a fixed model. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a noise detection method, device, electronic device and storage medium.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for detecting noise is provided, the method comprising:

[0005] Acquire a plurality of first images captured by an image sensor;

[0006] Determine, according to the multiple first images, noise data corresponding to the same pixel position in the multiple first images;

[0007] Performing clustering processing on the plurality of noise data to obtain a plurality of target cluster sets;

[0008] According to the plurality of target cluster sets, random telegraph signal RTS noise detection is performed on the image sensor.

[0009] Optionally, clustering the plurality of noise data to obtain a plurality of target cluster sets comprises:

[0010] Get the preset number of clusters;

[0011] According to the preset number of clusters, a plurality of the noise data are clustered by a preset clustering algorithm to obtain the plurality of target cluster sets.

[0012] Optionally, the preset number of clusters includes a plurality of clusters, and the clustering process is performed on the plurality of noise data by a preset clustering algorithm according to the preset number of clusters to obtain the plurality of target cluster sets including:

[0013] For each of the preset number of clusters, clustering is performed on the plurality of noise data using the preset clustering algorithm according to the preset number of clusters to obtain the preset number of candidate cluster sets;

[0014] Determining a target number of clusters from among the plurality of preset numbers of clusters according to candidate cluster sets corresponding to the plurality of preset numbers of clusters;

[0015] The candidate cluster sets corresponding to the number of target clusters are used as the multiple target cluster sets.

[0016] Optionally, determining the target number of clusters from the plurality of preset numbers of clusters according to the candidate cluster sets corresponding to the plurality of preset numbers of clusters comprises:

[0017] For each of the preset cluster numbers, the confidence of the noise data in each candidate cluster set corresponding to the preset cluster number in the current candidate cluster set is determined in sequence, and the target confidence corresponding to the preset cluster number is determined according to the multiple confidences; the confidence is used to characterize the credibility of the cluster result currently corresponding to the noise data;

[0018] According to the multiple target confidences, the target cluster number is determined from the multiple preset cluster numbers.

[0019] Optionally, for each of the preset number of clusters, sequentially determining the confidence of the noise data in each candidate cluster set corresponding to the preset number of clusters in the current candidate cluster set includes:

[0020] For each of the noise data, determine a first similarity between the noise data and a candidate cluster set where the noise data currently resides, and determine a second similarity between the noise data and other candidate cluster sets; the other candidate cluster sets are other candidate cluster sets among the preset number of candidate cluster sets except the candidate cluster set where the noise data currently resides;

[0021] The confidence level of the noise data in the current candidate cluster set is determined according to the first similarity and the second similarity.

[0022] Optionally, determining the confidence of the noise data in the current candidate cluster set according to the first similarity and the second similarity includes:

[0023] determining a similarity difference between the first similarity and the second similarity;

[0024] When the similarity difference is greater than or equal to a preset similarity threshold, determining a silhouette coefficient of the noise data in the current candidate clustering set, and using the silhouette coefficient as the confidence; or,

[0025] When the similarity difference is less than the preset similarity threshold, a specified threshold is used as the confidence.

[0026] Optionally, determining the target cluster number from a plurality of preset cluster numbers according to the plurality of target confidences comprises:

[0027] The preset cluster number corresponding to the maximum value among the multiple target confidences is used as the target cluster number.

[0028] Optionally, performing random telegraph signal RTS noise detection on the image sensor according to the multiple target cluster sets includes:

[0029] Obtaining the confidence corresponding to the noise data in each of the target cluster sets;

[0030] The number of pixels in the image sensor having the RTS noise is determined according to the noise data and the confidence level.

[0031] Optionally, the noise data includes a noise value, and determining the number of pixels in the image sensor having the RTS noise according to the noise data and the confidence level includes:

[0032] Determine a noise average value corresponding to a plurality of the noise values;

[0033] Determine the number of noises whose confidence is greater than or equal to a preset confidence threshold and whose noise value is greater than or equal to a noise average value;

[0034] The noise quantity is used as the number of pixels in the image sensor having the RTS noise.

[0035] Optionally, determining, according to the multiple first images, the noise data corresponding to the same pixel position in the multiple first images includes:

[0036] Obtaining a pixel value corresponding to a same pixel position in each of the first images;

[0037] For each pixel point position, determine a pixel statistical parameter corresponding to the pixel point position according to the plurality of pixel values;

[0038] The noise data corresponding to the pixel position is determined according to the pixel statistical parameter.

[0039] Optionally, the pixel statistical parameters include a pixel average value and a pixel variance value; and determining the noise data corresponding to the pixel point position according to the pixel statistical parameters includes:

[0040] Noise data corresponding to the pixel position is determined according to the pixel average value and the pixel variance value.

[0041] According to a second aspect of an embodiment of the present disclosure, a noise detection device is provided, the device comprising:

[0042] An acquisition module, configured to acquire a plurality of first images captured by the image sensor;

[0043] A determination module, configured to determine, based on the plurality of first images, noise data corresponding to a same pixel position in the plurality of first images;

[0044] A clustering module is configured to perform clustering processing on the plurality of noise data to obtain a plurality of target cluster sets;

[0045] The detection module is configured to perform random telegraph signal RTS noise detection on the image sensor according to the multiple target cluster sets.

[0046] Optionally, the clustering module is configured to obtain a preset number of clusters; and according to the preset number of clusters, cluster the plurality of noise data using a preset clustering algorithm to obtain the plurality of target cluster sets.

[0047] Optionally, the preset number of clusters includes multiple, and the clustering module is configured to, for each of the preset number of clusters, perform clustering processing on the multiple noise data according to the preset number of clusters by the preset clustering algorithm to obtain the preset number of cluster candidate sets; determine the target number of clusters from the multiple preset numbers of clusters according to the candidate cluster sets corresponding to the multiple preset numbers of clusters; and use the candidate cluster sets corresponding to the target number of clusters as the multiple target cluster sets.

[0048] Optionally, the clustering module is configured to determine, for each of the preset cluster numbers, the confidence of the noise data in each candidate cluster set corresponding to the preset cluster number in the current candidate cluster set, and determine the target confidence corresponding to the preset cluster number based on multiple confidences; the confidence is used to characterize the credibility of the clustering result currently corresponding to the noise data; and determine the target cluster number from multiple preset cluster numbers based on multiple target confidences.

[0049] Optionally, the clustering module is configured to determine, for each of the noise data, a first similarity between the noise data and a candidate cluster set in which the noise data currently resides, and to determine a second similarity between the noise data and remaining candidate cluster sets; the remaining candidate cluster sets are the remaining candidate cluster sets in the preset number of candidate cluster sets except the candidate cluster set in which the noise data currently resides; and determine the confidence of the noise data in the current candidate cluster set based on the first similarity and the second similarity.

[0050] Optionally, the clustering module is configured to determine a similarity difference between the first similarity and the second similarity; when the similarity difference is greater than or equal to a preset similarity threshold, determine a silhouette coefficient of the noise data in the current candidate clustering set, and use the silhouette coefficient as the confidence; or, when the similarity difference is less than the preset similarity threshold, use a specified threshold as the confidence.

[0051] Optionally, the clustering module is configured to use a preset cluster number corresponding to a maximum value among the multiple target confidences as the target cluster number.

[0052] Optionally, the detection module is configured to obtain the confidence level corresponding to the noise data in each of the target cluster sets; and determine the number of pixels in the image sensor having the RTS noise based on the noise data and the confidence level.

[0053] Optionally, the noise data includes noise values, and the detection module is configured to determine a noise average value corresponding to a plurality of the noise values; determine the number of noises whose confidence is greater than or equal to a preset confidence threshold and whose noise value is greater than or equal to the noise average value; and use the noise number as the number of pixels in the image sensor where the RTS noise exists.

[0054] Optionally, the determination module is configured to obtain the pixel value corresponding to the same pixel position in the multiple first images in each of the first images; for each pixel position, determine the pixel statistical parameters corresponding to the pixel position based on the multiple pixel values; and determine the noise data corresponding to the pixel position based on the pixel statistical parameters.

[0055] Optionally, the pixel statistical parameters include a pixel average value and a pixel variance value; the determination module is configured to determine the noise data corresponding to the pixel point position according to the pixel average value and the pixel variance value.

[0056] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to implement the steps of the noise detection method provided in the first aspect of the present disclosure when calling the executable instructions stored on the memory.

[0057] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the noise detection method provided in the first aspect of the present disclosure are implemented.

[0058] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0059] First, multiple first images collected by an image sensor are obtained. After that, noise data corresponding to the same pixel position in the multiple first images is determined based on the multiple first images. Then, the multiple noise data are clustered to obtain multiple target cluster sets. Finally, random telegraph signal RTS noise detection is performed on the image sensor based on the multiple target cluster sets. Multiple first images collected by the image sensor are obtained, and noise data corresponding to pixels at the same position in the first image is determined based on the multiple first images. In this way, there is no need to analyze and process each first image, only the noise data corresponding to each pixel position needs to be determined, and then the RTS noise performance of the image sensor is evaluated based on the noise data, which greatly reduces the amount of calculation and the complexity of calculation. At the same time, by clustering the multiple noise data, the noise data with high similarity is used as a cluster set to obtain multiple target cluster sets. In this way, similar noise data are clustered in each target cluster set, and data with RTS noise in multiple noise data can be separated, and RTS noise detection is performed on the image sensor based on the target cluster set, effectively ensuring the accuracy of RTS noise detection.

[0060] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0062] Figure 1 The figure is a flow chart of a noise detection method according to an exemplary embodiment.

[0063] Figure 2 is a flow chart of another noise detection method according to an exemplary embodiment.

[0064] Figure 3 is a floating diagram of pixel values ​​of a pixel channel according to an exemplary embodiment.

[0065] Figure 4 is a flow chart of another noise detection method according to an exemplary embodiment.

[0066] Figure 5 is a flow chart of another noise detection method according to an exemplary embodiment.

[0067] Figure 6 It is a block diagram of a noise detection device according to an exemplary embodiment.

[0068] Figure 7 It is a block diagram of an electronic device according to an exemplary embodiment.

[0069] Figure 8 is a block diagram of another electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0070] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0071] The terms "first", "second", etc. in the specification and claims of this application and the above drawings are used to distinguish similar objects and do not have to be understood as a specific order or sequence. In addition, in the description with reference to the drawings, the same symbols in different drawings represent the same elements.

[0072] In the description of the present disclosure, unless otherwise specified, "multiple" means two or more than two, and other quantifiers are similar thereto; "at least one item", "one item or multiple items" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one item a can represent any number of a; for another example, one item or multiple items among a, b and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple; "and / or" is a kind of description of the association relationship of associated objects, indicating that there can be three kinds of relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " indicates that the associated objects before and after are in an "or" relationship.

[0073] Before introducing the noise detection method, device, electronic device and storage medium provided by the present disclosure, the application scenarios involved in each embodiment of the present disclosure are first introduced. With the rapid development of integrated circuits, the size of semiconductor devices is getting smaller and smaller, which makes the gate oxide layer of the metal oxide tube in the image sensor prone to defects. This defect often manifests itself as a low-frequency noise from the circuit, that is, RTS noise. The morphology of RTS noise is similar to that of a telegraph signal, and it is a type of noise that greatly affects the dark field quality of the image. Since RTS noise not only has ups and downs on the time axis, but is also not evenly distributed in the pixel space of the image sensor, it is often difficult to detect it through a fixed model.

[0074] In the related art, the RTS noise model is usually obtained by statistically analyzing the gradient information of batch images collected by the image sensor. However, this method is based on the statistical calculation of batch signals and requires a large amount of data support. Both the computational complexity and the amount of data required for calculation are very huge, and it takes longer to establish the RTS noise model.

[0075] In order to solve the above technical problems, the present invention provides a noise detection method, device, electronic device and storage medium, which obtains multiple first images collected by an image sensor, and determines the noise data corresponding to the pixel points at the same position in the first image according to the multiple first images. In this way, there is no need to analyze and process each first image, only the noise data corresponding to each pixel position needs to be determined, and then the RTS noise performance of the image sensor is evaluated according to the noise data, which greatly reduces the amount of calculation and the complexity of calculation. At the same time, by clustering the multiple noise data, the noise data with high similarity is used as a cluster set to obtain multiple target cluster sets. In this way, each target cluster set gathers similar noise data, and the data with RTS noise in the multiple noise data can be separated, and the image sensor is subjected to RTS noise detection according to the target cluster set, which effectively ensures the accuracy of RTS noise detection.

[0076] The specific implementation modes of the present invention are described in detail below with reference to the accompanying drawings.

[0077] Figure 1 is a flow chart of a noise detection method according to an exemplary embodiment. Figure 1 As shown, the method comprises the following steps:

[0078] In step S101 , a plurality of first images captured by an image sensor are acquired.

[0079] Since RTS noise is low-frequency noise, in order to avoid interference caused by medium and high frequency noise that affects the accuracy of detection, in some embodiments, when the image sensor collects the first image, the image sensor may be masked to obtain multiple first images in a dark field environment. For example, the image sensor may be attached to a black / gray chart for shooting to obtain multiple first images.

[0080] In step S102, noise data corresponding to the same pixel position in the multiple first images is determined according to the multiple first images.

[0081] In this step, considering that most of the image sensors currently used in terminal devices have a large number of pixel units, in order to further reduce the amount of calculation, the multiple first images obtained can be cropped to reduce the number of pixels that need to calculate noise data and obtain multiple second images. For example, a 100×100 image can be cropped based on the center position of the first image. That is, at this time, only the noise data corresponding to 10,000 pixel positions need to be obtained, which greatly reduces the amount of calculation and improves the calculation efficiency compared to the number of pixels contained in the first image. Accordingly, according to the multiple first images, determining the noise data corresponding to the same pixel point position in the multiple first images includes: according to the multiple second images, determining the noise data corresponding to the same pixel point position in the multiple second images.

[0082] According to the above description, it can be known that usually the first image is acquired when the image sensor is subjected to black masking. However, due to the fact that it may not be possible to completely black mask in the actual operation process, this will cause some other noise to be introduced into the acquired first image. In some embodiments, the multiple first images can be filtered to filter out other noise introduced due to incomplete black masking, so as to obtain multiple third images. Accordingly, according to the multiple first images, determining the noise data corresponding to the same pixel position in the multiple first images includes: according to the multiple third images, determining the noise data corresponding to the same pixel position in the multiple third images.

[0083] According to the characteristics of RTS noise, RTS noise is low-frequency noise, and the waveform in the time domain generally fluctuates up and down. Based on this feature, the noise data corresponding to each pixel position can be determined based on the pixel values ​​of multiple first images. In this step, the pixel value corresponding to the same pixel position of each first image can be obtained first. For example, if there are 100 first images in total, then for the pixel at the same position, 100 pixel values ​​corresponding to the pixel of the 100 first images can be obtained. Furthermore, the noise data corresponding to each pixel position can be determined based on the pixel value corresponding to the pixel position.

[0084] In addition, it can be understood that, for an image, each pixel generally corresponds to multiple pixel channels, for example, RGB channels, and the performance of RTS noise in each pixel channel may also be different. Therefore, in order to more accurately evaluate the noise performance of RTS in the image sensor. In some embodiments, multiple first images can also be split into channels, and the pixel values ​​corresponding to the multiple pixel channels included in the multiple first images are obtained for each pixel position for the first images after channel splitting. For example, if each pixel includes 4 pixel channels and includes 100 first images, then for the pixel at the same position, 4×100 pixel values ​​can be obtained through 100 first images. Then, the noise data corresponding to the pixel position can be determined based on the pixel values ​​of the pixel channels included in each pixel obtained. At this time, the noise data corresponding to each pixel position corresponds to 4 noise values, and the 4 noise values ​​are the noise values ​​corresponding to the 4 pixel channels. And the noise value corresponding to each pixel channel can be determined based on the pixel value corresponding to the pixel channel in the 100 first images.

[0085] It should be noted that the above-mentioned steps of cropping, filtering and channel splitting can be implemented separately or in combination, and the present disclosure does not specifically limit this. For example, multiple first images can be cropped first to obtain multiple second images. Then, each second image is filtered to obtain a third image. After that, each third image is channel split, and then the noise data corresponding to the same pixel position in the multiple third images is determined based on the third images after channel splitting.

[0086] In step S103, clustering is performed on the plurality of noise data to obtain a plurality of target cluster sets.

[0087] In this step, the acquired multiple noise data can be clustered to group the noise data with high similarity as one cluster, thereby obtaining multiple target cluster sets. In this way, the RTS noise can be separated from the remaining data from the multiple noise data.

[0088] For example, the number of cluster sets that need to be clustered can be set first, that is, the preset number of clusters is obtained. After that, according to the preset number of clusters, multiple noise data can be clustered by a preset clustering algorithm to obtain a target cluster set. Among them, the preset number of clusters can be one or more. In the case where the preset number of clusters includes multiple, the multiple noise data can be clustered separately for each preset number of clusters. In this way, each preset number of clusters corresponds to multiple cluster sets. Further, according to the multiple cluster sets corresponding to each preset number of clusters, the optimal target number of clusters can be determined from the multiple preset numbers of clusters, and the target number of cluster sets can be used as multiple target cluster sets. In other words, each preset number of clusters corresponds to a clustering result, and the optimal clustering result can be selected from the multiple clustering results to ensure that the current clustering result is the most reasonable and accurate, and multiple noise data with high similarity can be accurately used as a target cluster set.

[0089] In step S104, random telegraph signal RTS noise detection is performed on the image sensor according to the multiple target cluster sets.

[0090] In this step, in order to evaluate the RTS noise performance in the image sensor, the number of pixels with RTS noise in the image sensor can be determined based on multiple target cluster sets, and then the performance of RTS noise in the image sensor can be evaluated based on the ratio of the number of pixels with RTS noise to the total number of pixels. If the ratio is greater than or equal to the preset ratio, it can be determined that the RTS noise in the image sensor has a greater impact and the final imaging quality of the image sensor is poor. On the contrary, if the ratio is less than the preset ratio, it can be determined that the RTS noise in the image sensor has a smaller impact and the final imaging quality of the image sensor is better.

[0091] As can be seen from the foregoing, in order to reduce the amount of calculation, the first image can also be cropped, and the number of pixels contained in the cropped image is less than the number of pixels contained in the first image. Therefore, if the first image is cropped, when determining the ratio, the ratio of the number of pixels with RTS noise determined above to the total number of pixels in the cropped second image can be used as a basis for evaluation.

[0092] By adopting the above method, multiple first images collected by the image sensor are obtained, and the noise data corresponding to the pixel points at the same position in the first image is determined according to the multiple first images. In this way, there is no need to analyze and process each first image, and only the noise data corresponding to each pixel position needs to be determined, and then the RTS noise performance of the image sensor is evaluated according to the noise data, which greatly reduces the amount of calculation and the complexity of calculation. At the same time, by clustering the multiple noise data, the noise data with high similarity is used as a cluster set to obtain multiple target cluster sets. In this way, each target cluster set gathers similar noise data, and the data with RTS noise in the multiple noise data can be separated, and the image sensor is subjected to RTS noise detection according to the target cluster set, which effectively ensures the accuracy of RTS noise detection.

[0093] Figure 2 is a flow chart of another noise detection method according to an exemplary embodiment. Figure 2 As shown, in the above step S102, determining the noise data corresponding to the same pixel position in the multiple first images according to the multiple first images may include the following steps:

[0094] In step S1021, the pixel value corresponding to the same pixel position in each of the first images is obtained.

[0095] In step S1022, for each pixel point position, a pixel statistical parameter corresponding to the pixel point position is determined according to a plurality of pixel values.

[0096] The pixel statistics may include a pixel average value and a pixel variance value. The pixel average value may be an average value of multiple pixel values ​​corresponding to the pixel point position, and the pixel variance value may be a variance value of multiple pixel values ​​corresponding to the pixel point position.

[0097] Considering that the pixel values ​​corresponding to each pixel position in the multiple first images may be disturbed and fluctuate, and the fluctuation is not caused by RTS noise, in order to eliminate such interference, in this step, the pixel average value of each pixel position in the multiple first images can be determined, so that the abnormal points that fluctuate can be filtered out.

[0098] For example, if Figure 3As shown in the figure, the fluctuation of pixel values ​​of four pixel channels in 100 first images is shown, the horizontal axis is the sequence number of the first image, a total of 100, and the vertical axis is the pixel value. Taking the pixel value fluctuation diagram of the first row and first column as an example, it can be seen that the points with pixel values ​​close to 90 and the points with pixel values ​​close to 85 are the abnormal points that are interfered with, which cannot accurately reflect the fluctuation of RTS noise. After averaging the pixel values, the interference of these abnormal points can be reduced.

[0099] In step S1023, noise data corresponding to the pixel is determined according to the pixel statistical parameter.

[0100] For example, the pixel statistical parameter includes a pixel average value and a pixel variance value. Accordingly, determining the noise data corresponding to the pixel position according to the pixel statistical parameter includes: determining the noise data corresponding to the pixel position according to the pixel average value and the pixel variance value.

[0101] According to the pixel average value and the pixel variance value, the noise data corresponding to the pixel position can be determined, for example, by the following formula:

[0102]

[0103] Figure 4 is a flow chart of another noise detection method according to an exemplary embodiment. Figure 4 As shown, in the above step S103, clustering the multiple noise data to obtain multiple target cluster sets may include the following steps:

[0104] In step S1031, a preset number of clusters is obtained.

[0105] The preset number of clusters is used to indicate the number of cluster sets to be clustered, and can generally be preset based on actual experience. For example, for RTS noise, it generally fluctuates between two or three levels, so the preset number of clusters can be set to one or more of 2, 3, 4, and 5.

[0106] In step S1032, clustering is performed on the plurality of noise data according to the preset number of clusters by using a preset clustering algorithm to obtain the plurality of target cluster sets.

[0107] Wherein, the preset clustering algorithm can be an unsupervised clustering algorithm, for example, it can be a K-means clustering algorithm. For the K-means clustering algorithm, the preset number of clusters is the K value in the algorithm. In the process of clustering multiple noise data by the preset clustering algorithm, K noise data can be randomly selected as the initial cluster center, and then the distance between each noise data and each cluster center is calculated, and each noise data is assigned to the cluster center closest to it. The cluster center and the noise data assigned to them represent a cluster set. Each time a noise data is assigned, the cluster center of the cluster will be recalculated according to the existing noise data in the cluster. This process will be repeated until a certain termination condition is met. The termination condition can be that no (or the minimum number) of noise data is reassigned to a different cluster set, no (or the minimum number) of cluster centers change again, and the error square sum is locally minimized, so that K cluster sets can be obtained.

[0108] In a possible implementation, a preset number of clusters can be directly set, and according to the preset cluster data, a plurality of the noise data are clustered by a preset clustering algorithm to obtain the plurality of target cluster sets. For example, if the preset number of clusters is 2, two target cluster sets can be obtained by clustering the plurality of the noise data by a preset clustering algorithm.

[0109] In another possible implementation, the preset number of clusters includes multiple numbers, and in this case, it is necessary to select an optimal target number of clusters from the multiple preset numbers of clusters. Specifically, the following steps are included:

[0110] Step 1) for each of the preset number of clusters, clustering is performed on the plurality of noise data using the preset clustering algorithm according to the preset number of clusters to obtain the preset number of candidate cluster sets.

[0111] In this way, each preset number of clusters will obtain a corresponding clustering result, and then the best clustering result can be selected from the multiple clustering results as the clustering result corresponding to the multiple noise data.

[0112] Step 2) According to the candidate cluster sets corresponding to the plurality of preset cluster numbers, a target cluster number is determined from the plurality of preset cluster numbers.

[0113] In this step, each preset number of clusters corresponds to a clustering result, and each clustering result contains the preset number of candidate clustering sets. In this step, the target confidence corresponding to each clustering result can be determined respectively, and then the optimal target number of clusters can be determined from the multiple preset numbers of clusters according to the target confidence. In layman's terms, it is to determine the most appropriate number of clusters into which multiple noise data should be divided.

[0114] Specifically, determining the target number of clusters may include the following steps:

[0115] Step 21) For each of the preset cluster numbers, the confidence of the noise data in each candidate cluster set corresponding to the preset cluster number in the current candidate cluster set is determined in turn, and the target confidence corresponding to the preset cluster number is determined based on the multiple confidences.

[0116] The confidence level is used to characterize the credibility of the clustering result currently corresponding to the noise data. The confidence level can be, for example, a value between 0 and 1. The closer to 1, the more credible the clustering result currently corresponding to the noise data is, that is, the more reasonable the classification of multiple noise data is. The greater the similarity within each cluster set and the greater the difference between cluster sets, the better the clustering effect.

[0117] In this step, for each noise data in each candidate cluster set, a first similarity between the noise data and the candidate cluster set where the noise data currently resides can be determined, and a second similarity between the noise data and the remaining candidate cluster sets can be determined.

[0118] The remaining candidate cluster sets are the remaining candidate cluster sets among the preset number of candidate cluster sets except the candidate cluster set where the noise data currently resides.

[0119] For example, the similarity may be a spatial distance, which may be, for example, a Euclidean distance. That is, the first similarity is the spatial distance between the noise data and the candidate cluster set where the noise data currently resides, and the second similarity is the spatial distance between the noise data and the remaining candidate cluster sets.

[0120] Then, the confidence of the noise data in the current candidate cluster set may be determined according to the first similarity and the second similarity.

[0121] In some embodiments, the similarity difference between the first similarity and the second similarity can be determined. Specifically, the difference between the second spatial distance and the first spatial distance can be used as the similarity difference. Afterwards, when the similarity difference is greater than or equal to the preset similarity threshold, the silhouette coefficient of the noise data in the current candidate clustering set is determined, and the silhouette coefficient is used as the confidence. If the similarity difference is greater than or equal to the preset similarity threshold, it can be indicated that the spatial distance between the noise data cluster and the current candidate clustering set is relatively close, and the silhouette coefficient of the noise data in the current candidate clustering set can be further determined at this time. The silhouette coefficient can be used to measure whether the clustering of the noise data is reasonable. If the silhouette coefficient is close to 1, it means that the clustering of the noise data is reasonable. If the silhouette coefficient is close to -1, it means that the clustering of the noise data is unreasonable and should be classified into other clustering sets. If the silhouette coefficient is close to 0, it means that the noise data is on the boundary of two clustering sets. The specific calculation method of the silhouette coefficient can refer to the method in the relevant technology, which will not be repeated here.

[0122] Alternatively, when the similarity difference is less than the preset similarity threshold, the specified threshold is used as the confidence. When the similarity difference is less than the preset similarity threshold, it indicates that the spatial distance between the noise data and the current candidate cluster set is far. The specified threshold may be, for example, 0, indicating that the current clustering of the noise data is unreasonable and the similarity with the cluster set is low.

[0123] It should be noted that, when the pixel position of the first image includes multiple pixel channels, the similarity difference corresponding to each pixel channel can be determined, and the maximum value of the similarity difference is used as the similarity difference corresponding to the pixel position.

[0124] Furthermore, after obtaining the confidence corresponding to each noise data, in a possible implementation, the maximum value among the multiple confidences can be used as the target confidence corresponding to the preset number of clusters. In another possible implementation, the average value of the multiple confidences can also be used as the target confidence corresponding to the preset number of clusters. In this way, it can be determined whether the clustering result corresponding to the preset clustering data is reasonable by the target confidence. The larger the target confidence, the more reasonable the clustering result.

[0125] Step 22) Determine the target cluster number from the plurality of preset cluster numbers according to the plurality of target confidences.

[0126] It can be understood that the greater the target confidence is, the more reasonable the clustering result is, so the preset number of clusters corresponding to the maximum value of multiple target confidences can be used as the target number of clusters.

[0127] Step 3) The candidate cluster sets corresponding to the target cluster number are used as the multiple target cluster sets.

[0128] Figure 5 is a flow chart of another noise detection method according to an exemplary embodiment. Figure 5 As shown, in the above step S104, performing random telegraph signal RTS noise detection on the image sensor according to the multiple target cluster sets may include the following steps:

[0129] In step S1041, the confidence level corresponding to the noise data in each target cluster set is obtained.

[0130] The confidence level may be obtained from the implementation process of the above step S1032.

[0131] In step S1042, the number of pixels in the image sensor having the RTS noise is determined according to the noise data and the confidence level.

[0132] For example, the noise data may include a noise value, and first, a noise average value corresponding to a plurality of the noise values ​​may be determined. Afterwards, the number of noises whose confidence is greater than or equal to a preset confidence threshold and whose noise value is greater than or equal to the noise average value may be determined. Finally, the number of noises is used as the number of pixels in the image sensor where the RTS noise exists. If the confidence corresponding to the noise data is greater than or equal to the preset confidence threshold, it indicates that the clustering result of the noise data is credible, and further, if the noise value is greater than or equal to the noise average value, it indicates that the noise data is RTS noise. The number of noise data that meets the above conditions may be used as the number of pixels in the image sensor where the RTS noise exists.

[0133] In addition, in order to further analyze the fluctuation of the RTS noise in the image sensor, the maximum value of the noise data corresponding to the pixel points with the RTS noise in the image sensor may be used as the maximum fluctuation value of the RTS noise in the image sensor.

[0134] It should be noted that, when the pixel point of the image sensor includes multiple pixel channels, the maximum value of the noise values ​​corresponding to the pixel channel of the pixel point where the RTS noise exists in the image sensor can be used as the maximum floating value of the RTS noise of the pixel channel in the image sensor for each pixel channel.

[0135] For example, Figure 3As shown, it includes floating graphs corresponding to 4 pixel channels, and each floating graph represents the change of the pixel value of the pixel channel in the 100 first images. Each floating graph is marked with the maximum floating value of the RTS noise corresponding to the pixel channel above. The maximum floating value RTS max of the RTS noise corresponding to the pixel channel in the floating graph of the first row and the first column is RTS max=4.8724, the maximum floating value RTS max of the RTS noise corresponding to the pixel channel in the floating graph of the first row and the second column is RTS max=10.1718, the maximum floating value RTS max of the RTS noise corresponding to the pixel channel in the floating graph of the second row and the first column is RTS max=5.402, and the maximum floating value RTS max of the RTS noise corresponding to the pixel channel in the floating graph of the second row and the second column is RTS max=10.0077.

[0136] In this way, the maximum floating value of the RTS noise in the image sensor can be used to evaluate the degree of influence of the pixel point in the image sensor that is most seriously affected by the RTS noise, and the imaging quality of the image sensor can be evaluated in turn.

[0137] By adopting the above method, multiple first images collected by the image sensor are obtained, and the noise data corresponding to the pixel points at the same position in the first image is determined according to the multiple first images. In this way, there is no need to analyze and process each first image, and only the noise data corresponding to each pixel position needs to be determined, and then the RTS noise performance of the image sensor is evaluated according to the noise data, which greatly reduces the amount of calculation and the complexity of calculation. At the same time, by clustering the multiple noise data, the noise data with high similarity is used as a cluster set to obtain multiple target cluster sets. In this way, each target cluster set gathers similar noise data, and the data with RTS noise in the multiple noise data can be separated, and the image sensor is subjected to RTS noise detection according to the target cluster set, which effectively ensures the accuracy of RTS noise detection.

[0138] Figure 6 FIG. 1 is a block diagram of a noise detection device according to an exemplary embodiment. Figure 6 As shown, the device 200 includes:

[0139] An acquisition module 201 is configured to acquire a plurality of first images captured by an image sensor;

[0140] A determination module 202 is configured to determine, based on the multiple first images, noise data corresponding to the same pixel position in the multiple first images;

[0141] The clustering module 203 is configured to perform clustering processing on the plurality of noise data to obtain a plurality of target cluster sets;

[0142] The detection module 204 is configured to perform random telegraph signal RTS noise detection on the image sensor according to the multiple target cluster sets.

[0143] Optionally, the clustering module 203 is configured to obtain a preset number of clusters; and according to the preset number of clusters, perform clustering processing on the plurality of noise data by using a preset clustering algorithm to obtain the plurality of target cluster sets.

[0144] Optionally, the preset number of clusters includes multiple, and the clustering module 203 is configured to, for each of the preset number of clusters, perform clustering processing on the multiple noise data according to the preset number of clusters by using the preset clustering algorithm to obtain the preset number of candidate cluster sets; determine the target number of clusters from the multiple preset numbers of clusters according to the candidate cluster sets corresponding to the multiple preset numbers of clusters; and use the candidate cluster set corresponding to the target number of clusters as the multiple target cluster sets.

[0145] Optionally, the clustering module 203 is configured to determine, for each preset number of clusters, the confidence of the noise data in each candidate cluster set corresponding to the preset number of clusters in the current candidate cluster set, and determine a target confidence corresponding to the preset number of clusters based on multiple confidences; the confidence is used to characterize the degree of credibility of the clustering result currently corresponding to the noise data; and based on multiple target confidences, determine the target number of clusters from multiple preset numbers of clusters.

[0146] Optionally, the clustering module 203 is configured to determine, for each noise data, a first similarity between the noise data and a candidate cluster set in which the noise data currently resides, and to determine a second similarity between the noise data and remaining candidate cluster sets; the remaining candidate cluster sets are remaining candidate cluster sets in the preset number of candidate cluster sets except the candidate cluster set in which the noise data currently resides; and determine the confidence of the noise data in the current candidate cluster set based on the first similarity and the second similarity.

[0147] Optionally, the clustering module 203 is configured to determine a similarity difference between the first similarity and the second similarity; when the similarity difference is greater than or equal to a preset similarity threshold, determine a silhouette coefficient of the noise data in the current candidate clustering set, and use the silhouette coefficient as the confidence; or, when the similarity difference is less than the preset similarity threshold, use a specified threshold as the confidence.

[0148] Optionally, the clustering module 203 is configured to use a preset cluster number corresponding to a maximum value among a plurality of target confidences as the target cluster number.

[0149] Optionally, the detection module 204 is configured to obtain the confidence level corresponding to the noise data in each target cluster set; and determine the number of pixels in the image sensor having the RTS noise according to the noise data and the confidence level.

[0150] Optionally, the noise data includes noise values, and the detection module 204 is configured to determine a noise average value corresponding to a plurality of the noise values; determine the number of noises whose confidence is greater than or equal to a preset confidence threshold and whose noise value is greater than or equal to the noise average value; and use the number of noises as the number of pixels in the image sensor that have the RTS noise.

[0151] Optionally, the determination module 202 is configured to obtain the pixel value corresponding to the same pixel position in the multiple first images in each of the first images; for each pixel position, determine the pixel statistical parameters corresponding to the pixel position based on the multiple pixel values; and determine the noise data corresponding to the pixel position based on the pixel statistical parameters.

[0152] Optionally, the pixel statistical parameter includes a pixel average value and a pixel variance value; the determination module 202 is configured to determine the noise data corresponding to the pixel point position according to the pixel average value and the pixel variance value.

[0153] By adopting the above-mentioned device, multiple first images collected by the image sensor are obtained, and the noise data corresponding to the pixel points at the same position in the first image is determined based on the multiple first images. In this way, there is no need to analyze and process each first image, and only the noise data corresponding to each pixel position needs to be determined, and then the RTS noise performance of the image sensor is evaluated based on the noise data, which greatly reduces the amount of calculation and the complexity of calculation. At the same time, by clustering the multiple noise data, the noise data with high similarity is used as a cluster set to obtain multiple target cluster sets. In this way, each target cluster set gathers similar noise data, and the data with RTS noise in the multiple noise data can be separated, and the image sensor is subjected to RTS noise detection based on the target cluster set, which effectively ensures the accuracy of RTS noise detection.

[0154] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0155] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, and when the program instructions are executed by a processor, the steps of the noise detection method provided by the present disclosure are implemented.

[0156] Figure 7is a block diagram of an electronic device 300 according to an exemplary embodiment. For example, the electronic device 300 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0157] Reference Figure 7 , the electronic device 300 may include one or more of the following components: a first processing component 302 , a first memory 304 , a first power component 306 , a multimedia component 308 , an audio component 310 , a first input / output interface 312 , a sensor component 314 , and a communication component 316 .

[0158] The first processing component 302 generally controls the overall operation of the electronic device 300, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The first processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above-mentioned noise detection method. In addition, the first processing component 302 may include one or more modules to facilitate the interaction between the first processing component 302 and other components. For example, the first processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 308 and the first processing component 302.

[0159] The first memory 304 is configured to store various types of data to support operations on the electronic device 300. Examples of such data include instructions for any application or method operating on the electronic device 300, contact data, phone book data, messages, pictures, videos, etc. The first memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0160] The first power source component 306 provides power to various components of the electronic device 300. The first power source component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 300.

[0161] The multimedia component 308 includes a screen that provides an output interface between the electronic device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the electronic device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0162] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC), and when the electronic device 300 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the first memory 304 or sent via the communication component 316. In some embodiments, the audio component 310 also includes a speaker for outputting audio signals.

[0163] The first input / output interface 312 provides an interface between the first processing component 302 and a peripheral interface module, which may be a keyboard, a click wheel, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0164] The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for the electronic device 300. For example, the sensor assembly 314 can detect the open / closed state of the electronic device 300, the relative positioning of components, such as the display and keypad of the electronic device 300, and the sensor assembly 314 can also detect the position change of the electronic device 300 or a component of the electronic device 300, the presence or absence of user contact with the electronic device 300, the orientation or acceleration / deceleration of the electronic device 300, and the temperature change of the electronic device 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0165] The communication component 316 is configured to facilitate wired or wireless communication between the electronic device 300 and other devices. The electronic device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0166] In an exemplary embodiment, the electronic device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above-mentioned noise detection method.

[0167] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a first memory 304 including instructions, and the instructions can be executed by a first processor 320 of the electronic device 300 to complete the above noise detection method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0168] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-mentioned noise detection method when executed by the programmable device.

[0169] Figure 8 FIG. 4 is a block diagram of another electronic device 400 according to an exemplary embodiment. For example, the electronic device 400 may be provided as a server. Figure 8 The electronic device 400 includes a second processing component 422, which further includes one or more processors, and a memory resource represented by a second memory 432 for storing instructions that can be executed by the second processing component 422, such as an application. The application stored in the second memory 432 may include one or more modules, each corresponding to a set of instructions. In addition, the second processing component 422 is configured to execute instructions to perform the above-mentioned noise detection method.

[0170] The electronic device 400 may further include a second power supply component 426 configured to perform power management of the electronic device 400, a wired or wireless network interface 450 configured to connect the electronic device 400 to a network, and a second input / output interface 458. The electronic device 400 may operate based on an operating system stored in the memory 432, such as Windows Server 2008. TM , Mac OS X TM , Unix TM , Linux TM , FreeBSD TM or similar.

[0171] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0172] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A noise detection method, characterized in that: The method comprises: Acquire a plurality of first images captured by an image sensor; Determine, according to the multiple first images, noise data corresponding to the same pixel position in the multiple first images; Performing clustering processing on the plurality of noise data to obtain a plurality of target cluster sets; According to the multiple target cluster sets, random telegraph signal RTS noise detection is performed on the image sensor.

2. The method according to claim 1, characterized in that The clustering process is performed on the plurality of noise data to obtain a plurality of target cluster sets, including: Get the preset number of clusters; According to the preset number of clusters, a plurality of the noise data are clustered by a preset clustering algorithm to obtain the plurality of target cluster sets.

3. The method according to claim 2, characterized in that The preset number of clusters includes a plurality of clusters, and the plurality of noise data are clustered by a preset clustering algorithm according to the preset number of clusters to obtain the plurality of target cluster sets including: For each of the preset number of clusters, clustering is performed on the plurality of noise data using the preset clustering algorithm according to the preset number of clusters to obtain the preset number of candidate cluster sets; Determining a target number of clusters from among the plurality of preset numbers of clusters according to candidate cluster sets corresponding to the plurality of preset numbers of clusters; The candidate cluster sets corresponding to the number of target clusters are used as the multiple target cluster sets.

4. The method according to claim 3, characterized in that The determining the target cluster number from the plurality of preset cluster numbers according to the candidate cluster sets corresponding to the plurality of preset cluster numbers comprises: For each of the preset cluster numbers, the confidence of the noise data in each candidate cluster set corresponding to the preset cluster number in the current candidate cluster set is determined in sequence, and the target confidence corresponding to the preset cluster number is determined according to the multiple confidences; the confidence is used to characterize the credibility of the cluster result currently corresponding to the noise data; According to the multiple target confidences, the target cluster number is determined from the multiple preset cluster numbers.

5. The method according to claim 4, characterized in that The step of sequentially determining, for each of the preset number of clusters, the confidence of the noise data in each candidate cluster set corresponding to the preset number of clusters in the current candidate cluster set includes: For each of the noise data, determine a first similarity between the noise data and a candidate cluster set where the noise data currently resides, and determine a second similarity between the noise data and other candidate cluster sets; the other candidate cluster sets are other candidate cluster sets among the preset number of candidate cluster sets except the candidate cluster set where the noise data currently resides; The confidence level of the noise data in the current candidate cluster set is determined according to the first similarity and the second similarity.

6. The method according to claim 5, characterized in that Determining the confidence of the noise data in the current candidate cluster set according to the first similarity and the second similarity includes: determining a similarity difference between the first similarity and the second similarity; When the similarity difference is greater than or equal to a preset similarity threshold, determining a silhouette coefficient of the noise data in the current candidate clustering set, and using the silhouette coefficient as the confidence; or, When the similarity difference is less than the preset similarity threshold, a specified threshold is used as the confidence.

7. The method according to claim 4, characterized in that Determining the target cluster number from a plurality of preset cluster numbers according to the plurality of target confidences comprises: The preset cluster number corresponding to the maximum value among the multiple target confidences is used as the target cluster number.

8. The method according to claim 1, characterized in that The performing random telegraph signal RTS noise detection on the image sensor according to the multiple target cluster sets comprises: Obtaining the confidence corresponding to the noise data in each of the target cluster sets; The number of pixels in the image sensor having the RTS noise is determined according to the noise data and the confidence level.

9. The method according to claim 8, characterized in that The noise data includes a noise value, and determining the number of pixels in the image sensor having the RTS noise according to the noise data and the confidence level includes: Determine a noise average value corresponding to a plurality of the noise values; Determine the number of noises whose confidence is greater than or equal to a preset confidence threshold and whose noise value is greater than or equal to a noise average value; The noise quantity is used as the number of pixels in the image sensor having the RTS noise.

10. The method according to any one of claims 1 to 9, characterized in that The determining, according to the multiple first images, noise data corresponding to the same pixel position in the multiple first images comprises: Obtaining a pixel value corresponding to a same pixel position in each of the first images; For each pixel point position, determine a pixel statistical parameter corresponding to the pixel point position according to the plurality of pixel values; The noise data corresponding to the pixel position is determined according to the pixel statistical parameter.

11. The method according to claim 10, characterized in that The pixel statistical parameters include a pixel average value and a pixel variance value; and determining the noise data corresponding to the pixel point position according to the pixel statistical parameters includes: Noise data corresponding to the pixel position is determined according to the pixel average value and the pixel variance value.

12. A noise detection device, characterized in that: The device comprises: An acquisition module, configured to acquire a plurality of first images captured by the image sensor; A determination module, configured to determine, based on the plurality of first images, noise data corresponding to a same pixel position in the plurality of first images; A clustering module is configured to perform clustering processing on the plurality of noise data to obtain a plurality of target cluster sets; The detection module is configured to perform random telegraph signal RTS noise detection on the image sensor according to the multiple target cluster sets.

13. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the steps of the method described in any one of claims 1 to 11 when calling the executable instructions stored in the memory.

14. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the steps of the method described in any one of claims 1 to 11 are implemented.

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