Simulation verification method and device for image signal processing based on artificial intelligence

Abstract

The invention discloses a simulation verification method and device for image signal processing based on artificial intelligence, and relates to the field of image signal processing, and the method comprises the steps: transferring an image signal into a preprocessing module before the image signal is transmitted into a simulation verification module; image signals needing to be verified are transmitted into the preprocessing module, target detection and feature extraction are carried out on images, processed image data are classified, and meanwhile the classified image data are sequentially transmitted into the simulation verification module; according to the artificial intelligence-based simulation verification method and device for image signal processing, the performance of a whole computer system under different conditions is detected by testing and verifying the whole computer system in a simulation environment; when design and optimization are carried out in a simulation environment, optimal parameters and configuration are found so as to meet specific requirements and requirements; therefore, the time and the cost can be saved, the efficiency and the performance of simulation verification are improved, and the abnormal part in the image is marked at the fastest speed.

Description

Technical Field

[0001] The present invention relates to image signal processing technology, and particularly to a simulation verification method and device for image signal processing based on artificial intelligence. Background Art

[0002] Image processing refers to the technology of analyzing images by computer to achieve the desired results; also known as image processing. Image processing generally refers to digital image processing. A digital image is a large two-dimensional array obtained by devices such as industrial cameras, video cameras, and scanners. The elements of this array are called pixels, and their values are called gray values. Image processing technology generally includes three parts: image compression, enhancement and restoration, and matching, description and recognition.

[0003] When the existing image signal processing simulation verification method is in use, the input images often have drawbacks such as noise and insufficient contrast due to different acquisition environments, such as the brightness and darkness of illumination and the quality of equipment performance. In addition, due to factors such as distance and focal length, the size and position of objects in the entire image are uncertain. To ensure the consistency of the size, position, and image quality of the objects in the image, the image is preprocessed, and after processing, the image information is simulated and verified. At the same time, during the process of image signal processing simulation verification, abnormal data in the image information cannot be accurately marked in the verification simulation module. Therefore, a simulation verification method and device for image signal processing based on artificial intelligence are developed. Summary of the Invention

[0004] The purpose of the present invention is to provide a simulation verification method and device for image signal processing based on artificial intelligence to solve the above deficiencies in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A simulation verification method for image signal processing based on artificial intelligence, including: before the image signal is transmitted into the simulation verification module, transferring the image signal into the preprocessing module;

[0006] Transmitting the image signal to be verified into the preprocessing module, performing target detection and feature extraction on the image, classifying the processed image data, and sequentially transmitting the classified image data into the simulation verification module;

[0007] Comparing the processed image signal with the standard values set inside the simulation verification module, marking the abnormal parts in the image information according to the comparison result, and at the same time marking the relative optimization and improvement directions in the image information.

[0008] As a further optimization solution of the present invention, the preprocessing module is used to denoise the image to improve the image quality. At the same time, by adjusting the pixel distribution of the image, the contrast of the image becomes more uniform and the details of the image are enhanced.

[0009] As a further optimization solution of the present invention, the preprocessing module performs normalization processing on the pixel values of the image so that the pixel value range of the image is between 0 and 1, and marks the edge information in the image information through an edge detection algorithm and performs segmentation processing on the edge part.

[0010] As a further optimization solution of the present invention, the image information processed by the preprocessing module is input into the simulation verification module, and the corresponding image processing module is run to process the image and record the processed result at the same time.

[0011] As a further optimization solution of the present invention, the simulation verification module analyzes and evaluates the processed image result and stores the processed result at the same time.

[0012] A simulation verification device for image signal processing based on artificial intelligence, at least one processor;

[0013] And a memory communicatively connected to the at least one processor;

[0014] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the simulation verification method for image signal processing based on artificial intelligence as described in any one of the above.

[0015] Compared with the prior art, a simulation verification method and device for image signal processing based on artificial intelligence provided by the present invention perform geometric transformation operations such as rotating and flipping the image to change the direction or perspective of the image so as to be applicable to subsequent operations; at the same time, through background modeling or segmentation technology, the foreground target in the image is separated from the background for subsequent processing or analysis; at the same time, the entire computer system is tested and verified in a simulation environment to detect its performance under different conditions; and when designing and optimizing in a simulation environment, the best parameters and configurations are found to meet specific needs and requirements; thereby, time and costs can be saved, and the efficiency and performance of simulation verification can be improved, and the abnormal parts in the image can be marked out at the fastest speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0017] Figure 1 This is the overall process schematic diagram provided by the embodiment of the present invention. Specific implementation manners

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention; the terms "first", "second", "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0020] Embodiment 1:

[0021] Please refer to Figure 1 , a simulation verification method for image signal processing based on artificial intelligence, including: before the image signal is transmitted into the simulation verification module, transferring the image signal into the preprocessing module;

[0022] Transmitting the image signal to be verified into the preprocessing module, performing target detection and feature extraction on the image, classifying the processed image data, and sequentially transmitting the classified image data into the simulation verification module;

[0023] The standard values set inside the simulation verification module are compared with the processed image signals. According to the comparison results, the abnormal parts in the image information are marked, and at the same time, the corresponding optimization and improvement directions are marked in the image information.

[0024] In this solution, according to the verification requirements, a suitable computer simulation platform (such as MATLAB, Python, etc.) and the corresponding image processing toolkit (such as OpenCV, scikit-image, etc.) are selected.

[0025] The data of the original image is obtained, and a suitable image dataset is selected or the image data is collected by oneself as the input of the verification. At the same time, the image is preprocessed. According to the verification requirements, the original image is preprocessed to make the image meet the requirements of the simulation verification. According to the verification target, a suitable image processing method is selected to process the preprocessed image, such as filtering, edge detection, image enhancement, etc. The processed image is input into the simulation platform, and the corresponding simulation verification program is run to obtain the processed image result.

[0026] The processed image results are analyzed and evaluated. The image quality and method performance can be evaluated through visualization and quantitative indicators. At the same time, according to the simulation verification results, the method is optimized and improved to further enhance the image processing effect.

[0027] Furthermore, the preprocessing module is used to denoise the image. The noise in the image is removed through a filter to improve the image quality. At the same time, by adjusting the pixel distribution of the image, the contrast of the image is made more uniform and the details of the image are enhanced.

[0028] Specifically, the image size is adjusted to a specific size or ratio to meet the input requirements of subsequent processing algorithms or models, and filters (such as Gaussian filtering, median filtering, etc.) are used to remove the noise in the image to improve the image quality and subsequent processing effect. By adjusting parameters such as the contrast, brightness, and saturation of the image, or applying techniques such as histogram equalization, the visual effect and details of the image are enhanced. At the same time, the color image is converted into a grayscale image to reduce the data dimension and simplify the processing process.

[0029] Furthermore, the preprocessing module performs normalization processing on the pixel values of the image so that the pixel value range of the image is between 0 and 1, and the edge information in the image information is marked through an edge detection algorithm, and the edge part is segmented.

[0030] Specifically, by normalizing the image pixels, the pixel values are mapped to a specific range to facilitate subsequent processing; edge detection algorithms (such as Sobel, Canny, etc.) can also be used to extract edge information in the image for tasks such as object detection and segmentation; the image is converted from one color space (such as RGB) to another color space (such as HSV, Lab, etc.) to meet specific image processing requirements, and according to needs, the image is cropped or padded to adjust the size and position of the region of interest or to make the image have a specific size.

[0031] By performing geometric transformation operations such as rotating and flipping the image, the direction or perspective of the image is changed to make it suitable for subsequent operations; at the same time, through background modeling or segmentation techniques, the foreground objects in the image are separated from the background for subsequent processing or analysis.

[0032] Furthermore, the image information processed by the preprocessing module is input into the simulation verification module, and the corresponding image processing module is run to process the image, and the results after processing are recorded at the same time.

[0033] Specifically, by running algorithms and models in a simulation environment, their performance and effects under different conditions are evaluated; at the same time, by testing and verifying the entire computer system in a simulation environment, its performance under different conditions is detected; and when designing and optimizing in a simulation environment, the best parameters and configurations are found to meet specific needs and requirements; thus, time and costs can be saved, and the efficiency and performance of simulation verification can be improved.

[0034] The CycleGAN simulation method can be used for verification. First, the image data needs to be preprocessed. For the synthesis of the background and the target, since the size of the target image is fixed, to perform the migration of the target, a background image suitable for the target size needs to be obtained; for smaller scene images, after adjusting the size, the migration simulation can be directly performed. For the large background images in the dataset, slicing is required to convert them into background images suitable for the target size. After that, suitable regions need to be selected as the migration background images. The large scene images cannot be directly used for migration, otherwise, the scaling degree of the image content will be inconsistent. It is necessary to ensure that the scaling degree of the target and the scene is the same; secondly, SAR images or optical images are selected according to the expected target, and the image resolution, the number of channels, and the background targets in the two types of images are classified. After that, the size of the image dataset is adjusted, and the dataset is segmented into a training dataset and a test dataset.

[0035] After the dataset is made, the corresponding images are selected and input into the model for application simulation.

[0036] Furthermore, the simulation verification module analyzes and evaluates the processed image results and stores the processed results at the same time.

[0037] Specifically, summarize and store the results after simulation verification, and at the same time evaluate the verified results to give suggestions applicable to the current scenario.

[0038] S1. Summarize the required images to the preprocessing module side and process the images.

[0039] S2. Transmit the processed images to the inside of the simulation verification module to verify the images.

[0040] S3. After the simulation verification module finishes verification, analyze and evaluate the results.

[0041] Embodiment 2:

[0042] A simulation verification device for image signal processing based on artificial intelligence, at least one processor;

[0043] And a memory communicatively connected to the at least one processor;

[0044] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the simulation verification method for image signal processing based on artificial intelligence according to any one of Embodiment 1.

[0045] Specifically, the storage device can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium can be transmitted with any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0046] Only certain exemplary embodiments of the present invention have been described above by way of illustration. Without doubt, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of the claims of the present invention.

Claims

1. A simulation verification method for image signal processing based on artificial intelligence, characterized in that, Including: Before the image signal is transmitted into the simulation verification module, transfer the image signal into the preprocessing module; Transmit the image signal to be verified into the preprocessing module, perform object detection and feature extraction on the image, classify the processed image data, and sequentially transmit the classified image data into the simulation verification module; Compare the processed image signal with the standard value set inside the simulation verification module, mark the abnormal part in the image information according to the comparison result, and at the same time mark the relative optimization and improvement directions in the image information.

2. The simulation verification method for image signal processing based on artificial intelligence according to claim 1, wherein The preprocessing module is used to denoise the image to improve the image quality. At the same time, by adjusting the pixel distribution of the image, the contrast of the image becomes more uniform and the details of the image are enhanced.

3. The simulation verification method for image signal processing based on artificial intelligence according to claim 1, characterized in that The preprocessing module performs normalization processing on the pixel values of the image so that the pixel value range of the image is between 0 and 1, and marks the edge information in the image information through an edge detection algorithm and performs segmentation processing on the edge part.

4. A simulation verification method for image signal processing based on artificial intelligence according to claim 1, characterized in that, Input the image information processed by the preprocessing module into the simulation verification module, run the corresponding image processing module to process the image, and record the processed result at the same time.

5. A simulation verification method for image signal processing based on artificial intelligence according to claim 1, characterized in that The simulation verification module analyzes and evaluates the processed image result and stores the processed result at the same time.

6. A simulation verification device for image signal processing based on artificial intelligence, characterized in that, Including at least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the simulation verification method for image signal processing based on artificial intelligence as described in any one of claims 1 to 5.

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