Image data application processing system

Abstract

The invention relates to an image data application program processing system, and the system comprises a computer processing device which serves an image data application program and is used for receiving a current operation picture inputted by the image data application program; and the interpolation execution device is used for carrying out interpolation processing on the maximum value filtering image by adopting a reference interpolation algorithm so as to obtain an optimized operation picture corresponding to the maximum value filtering image. The image data application program processing system is stable in operation and intelligent in design. The interpolation algorithm corresponding to the maximum signal-to-noise ratio in each signal-to-noise ratio obtained by traversing various interpolation algorithms through the Hough neural network after each training of the set number of times can be used as the reference interpolation algorithm, and the reference interpolation algorithm is adopted to carry out interpolation processing on the maximum value filtering image. And obtaining an optimized operation picture corresponding to the maximum value filtering image so as to complete interpolation processing which is customized for the maximum value filtering image and has an optimal interpolation effect.

Description

Technical Field

[0001] This invention relates to the field of computer-based vision processing, and more particularly to an image data application processing system. Background Technology

[0002] Due to the complexity and large volume of image data, using computers to perform various image data processing operations is one of the key approaches to addressing the complexity of image processing. However, different image contents have different characteristics, and the same interpolation algorithm may produce different image processing results when used as input to a computer for processing various image contents.

[0003] Therefore, when using a computer processing device that serves an image data application to receive the current operation image input by the image data application for image interpolation processing, it is necessary to select different optimal interpolation algorithms suitable for the content of different current operation images, so as to ensure the interpolation effect and image quality improvement level of different current operation images.

[0004] For example, an AI-based photorealistic frame interpolation algorithm, application publication number CN117240987A, includes the following steps: S1, acquiring an image; S2, processing the acquired image by rasterizing the input image and then extracting pixel features. This AI-based photorealistic frame interpolation algorithm rasterizes the input image and then extracts pixel features, calls an AI module to analyze pixel features and predict pixel distribution, and finally synthesizes and outputs intermediate images. Based on lightweight AI, high-quality intermediate frames can be synthesized using low-cost local devices. It has low requirements for device computing power, does not require server-side computing power, and can achieve pixel-level analysis and processing of graphics and generate photorealistic intermediate frames on local devices. This can significantly reduce film and television production costs, and the resolution and color reproduction are higher than traditional methods. Summary of the Invention

[0005] To address technical issues in related fields, this invention provides an image data application processing system. This system uses the interpolation algorithm corresponding to the maximum signal-to-noise ratio (SNR) among various interpolation algorithms obtained by the Hofit neural network after a set number of training iterations as a reference interpolation algorithm. The system then uses this reference interpolation algorithm to interpolate the maximum-value filtered image, thereby obtaining an optimized operation screen corresponding to the maximum-value filtered image. This completes the customized interpolation processing for the maximum-value filtered image with the best interpolation effect.

[0006] According to the present invention, the system comprises:

[0007] A computer processing device serving an image data application, for receiving a currently operating image input by the image data application;

[0008] A sequential mapping device, serving an image data application and connected to the computer processing device, includes an arithmetic mean filtering device, a dynamic processing device, and a maximum value filtering device. The dynamic processing device is connected to both the arithmetic mean filtering device and the maximum value filtering device. The arithmetic mean filtering device performs arithmetic mean filtering on a received image to obtain and output a corresponding arithmetic mean filtered image. The dynamic processing device performs white balance processing based on a dynamic threshold on the received arithmetic mean filtered image to obtain and output a corresponding dynamic processed image. The maximum value filtering device performs maximum value filtering on the received dynamic processed image to obtain and output a corresponding maximum value filtered image.

[0009] A content determination device, connected to the sequence mapping device, is used to receive the maximum value filtered image, determine whether each pixel in the maximum value filtered image is an edge pixel based on the pixel value gradient of each pixel, and input the color channel values ​​corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm into the Hofit neural network after it has been trained a set number of times, and run the Hofit neural network after it has been trained a set number of times to obtain the signal-to-noise ratio of the image obtained after interpolating the maximum value filtered image using the target interpolation algorithm; wherein, the value of the set number of times has an inverse numerical mapping relationship with each non-edge pixel in the maximum value filtered image;

[0010] The traversal processing device is connected to the content judgment device and is used to take the interpolation algorithm corresponding to the maximum signal-to-noise ratio among the various interpolation algorithms obtained by the Hofit neural network after completing a set number of training iterations as the reference interpolation algorithm.

[0011] An interpolation execution device, connected to the traversal processing device, is used to perform interpolation processing on the maximum value filtered image using a reference interpolation algorithm to obtain an optimized operation screen corresponding to the maximum value filtered image.

[0012] The image data application processing system of this invention is stable in operation and intelligent in design. It can use the interpolation algorithm corresponding to the maximum signal-to-noise ratio (SNR) among various interpolation algorithms obtained after a set number of training iterations of the Hofit neural network as a reference interpolation algorithm, and then use this reference interpolation algorithm to perform interpolation processing on the maximum value filtered image to obtain an optimized operation screen corresponding to the maximum value filtered image. This achieves customized interpolation processing with the best interpolation effect for the maximum value filtered image. Attached Figure Description

[0013] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0014] Figure 1 This is a schematic diagram of the internal structure of an image data application processing system according to a first embodiment of the present invention.

[0015] Figure 2 This is a schematic diagram of the internal structure of an image data application processing system according to a second embodiment of the present invention.

[0016] Figure 3 This is a schematic diagram of the internal structure of an image data application processing system according to a third embodiment of the present invention. Detailed Implementation

[0017] The embodiments of the image data application processing system of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] First Embodiment

[0019] Figure 1 This is a schematic diagram of the internal structure of an image data application processing system according to a first embodiment of the present invention. The system includes:

[0020] A computer processing device serving an image data application, for receiving a currently operating image input by the image data application;

[0021] A sequential mapping device, serving an image data application and connected to the computer processing device, includes an arithmetic mean filtering device, a dynamic processing device, and a maximum value filtering device. The dynamic processing device is connected to both the arithmetic mean filtering device and the maximum value filtering device. The arithmetic mean filtering device performs arithmetic mean filtering on a received image to obtain and output a corresponding arithmetic mean filtered image. The dynamic processing device performs white balance processing based on a dynamic threshold on the received arithmetic mean filtered image to obtain and output a corresponding dynamic processed image. The maximum value filtering device performs maximum value filtering on the received dynamic processed image to obtain and output a corresponding maximum value filtered image.

[0022] For example, the dynamic processing device is connected to the arithmetic mean filtering device and the maximum value filtering device respectively. The arithmetic mean filtering device is used to perform arithmetic mean filtering on the received image to obtain and output a corresponding arithmetic mean filtered image. The dynamic processing device is used to perform white balance processing based on dynamic threshold on the received arithmetic mean filtered image to obtain and output a corresponding dynamic processed image. The maximum value filtering device is used to perform maximum value filtering on the received dynamic processed image to obtain and output a corresponding maximum value filtered image. The arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device are implemented using different SOC devices.

[0023] A content determination device, connected to the sequence mapping device, is used to receive the maximum value filtered image, determine whether each pixel in the maximum value filtered image is an edge pixel based on the pixel value gradient of each pixel, and input the color channel values ​​corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm into the Hofit neural network after it has been trained a set number of times, and run the Hofit neural network after it has been trained a set number of times to obtain the signal-to-noise ratio of the image obtained after interpolating the maximum value filtered image using the target interpolation algorithm; wherein, the value of the set number of times has an inverse numerical mapping relationship with each non-edge pixel in the maximum value filtered image;

[0024] The traversal processing device is connected to the content judgment device and is used to take the interpolation algorithm corresponding to the maximum signal-to-noise ratio among the various interpolation algorithms obtained by the Hofit neural network after completing a set number of training iterations as the reference interpolation algorithm.

[0025] An interpolation execution device, connected to the traversal processing device, is used to perform interpolation processing on the maximum value filtered image using a reference interpolation algorithm to obtain the optimized operation screen corresponding to the maximum value filtered image;

[0026] The process involves inputting the color channel values ​​corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm into the Hofit neural network after it has undergone a set number of training iterations. Then, the Hofit neural network after it has undergone a set number of training iterations is run to obtain the signal-to-noise ratio of the image obtained after interpolating the maximum value filtered image using the target interpolation algorithm. This includes simulation processing to model the Hofit neural network after it has undergone a set number of training iterations using a numerical simulation mode.

[0027] Second Embodiment

[0028] Figure 2 This is a schematic diagram of the internal structure of an image data application processing system according to a second embodiment of the present invention. The diagram includes the following structures:

[0029] A computer processing device serving an image data application, for receiving a currently operating image input by the image data application;

[0030] A sequential mapping device, serving an image data application and connected to the computer processing device, includes an arithmetic mean filtering device, a dynamic processing device, and a maximum value filtering device. The dynamic processing device is connected to both the arithmetic mean filtering device and the maximum value filtering device. The arithmetic mean filtering device performs arithmetic mean filtering on a received image to obtain and output a corresponding arithmetic mean filtered image. The dynamic processing device performs white balance processing based on a dynamic threshold on the received arithmetic mean filtered image to obtain and output a corresponding dynamic processed image. The maximum value filtering device performs maximum value filtering on the received dynamic processed image to obtain and output a corresponding maximum value filtered image.

[0031] A content determination device, connected to the sequence mapping device, is used to receive the maximum value filtered image, determine whether each pixel in the maximum value filtered image is an edge pixel based on the pixel value gradient of each pixel, and input the color channel values ​​corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm into the Hofit neural network after it has been trained a set number of times, and run the Hofit neural network after it has been trained a set number of times to obtain the signal-to-noise ratio of the image obtained after interpolating the maximum value filtered image using the target interpolation algorithm; wherein, the value of the set number of times has an inverse numerical mapping relationship with each non-edge pixel in the maximum value filtered image;

[0032] The traversal processing device is connected to the content judgment device and is used to take the interpolation algorithm corresponding to the maximum signal-to-noise ratio among the various interpolation algorithms obtained by the Hofit neural network after completing a set number of training iterations as the reference interpolation algorithm.

[0033] An interpolation execution device, connected to the traversal processing device, is used to perform interpolation processing on the maximum value filtered image using a reference interpolation algorithm to obtain the optimized operation screen corresponding to the maximum value filtered image;

[0034] A heat detection device is disposed near the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, and is respectively connected to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device;

[0035] The heat detection device, which is located near the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device and is connected to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device respectively, includes: the heat detection device being used to perform on-site measurement of the current heat of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device respectively.

[0036] Third Embodiment

[0037] Figure 3 This is a schematic diagram of the internal structure of an image data application processing system according to a third embodiment of the present invention. The diagram includes the following structures:

[0038] A computer processing device serving an image data application, for receiving a currently operating image input by the image data application;

[0039] A sequential mapping device, serving an image data application and connected to the computer processing device, includes an arithmetic mean filtering device, a dynamic processing device, and a maximum value filtering device. The dynamic processing device is connected to both the arithmetic mean filtering device and the maximum value filtering device. The arithmetic mean filtering device performs arithmetic mean filtering on a received image to obtain and output a corresponding arithmetic mean filtered image. The dynamic processing device performs white balance processing based on a dynamic threshold on the received arithmetic mean filtered image to obtain and output a corresponding dynamic processed image. The maximum value filtering device performs maximum value filtering on the received dynamic processed image to obtain and output a corresponding maximum value filtered image.

[0040] A content determination device, connected to the sequence mapping device, is used to receive the maximum value filtered image, determine whether each pixel in the maximum value filtered image is an edge pixel based on the pixel value gradient of each pixel, and input the color channel values ​​corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm into the Hofit neural network after it has been trained a set number of times, and run the Hofit neural network after it has been trained a set number of times to obtain the signal-to-noise ratio of the image obtained after interpolating the maximum value filtered image using the target interpolation algorithm; wherein, the value of the set number of times has an inverse numerical mapping relationship with each non-edge pixel in the maximum value filtered image;

[0041] The traversal processing device is connected to the content judgment device and is used to take the interpolation algorithm corresponding to the maximum signal-to-noise ratio among the various interpolation algorithms obtained by the Hofit neural network after completing a set number of training iterations as the reference interpolation algorithm.

[0042] An interpolation execution device, connected to the traversal processing device, is used to perform interpolation processing on the maximum value filtered image using a reference interpolation algorithm to obtain the optimized operation screen corresponding to the maximum value filtered image;

[0043] A throughput detection device is disposed near the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, and is respectively connected to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device;

[0044] The throughput detection device, which is located near the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device and is connected to each of the following: the throughput detection device is used to perform on-site measurement of the current data throughput of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device.

[0045] Next, the specific structure of the image data application processing system of the present invention will be further described.

[0046] In the various image data application processing systems according to embodiments of the present invention:

[0047] An image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

[0048] In the various image data application processing systems according to embodiments of the present invention:

[0049] The image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively. This includes performing homomorphic filtering on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

[0050] In the various image data application processing systems according to embodiments of the present invention:

[0051] The image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively. This includes performing Wiener filtering on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

[0052] In the various image data application processing systems according to embodiments of the present invention:

[0053] The image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively. This includes performing arithmetic mean filtering on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

[0054] And in the various image data application processing systems according to embodiments of the present invention:

[0055] The image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively. This includes performing harmonic mean filtering on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

[0056] In addition, in the image data application processing system, the values ​​of each color channel corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm are input into the Hofit neural network after a set number of training iterations, and the Hofit neural network after a set number of training iterations is run to obtain the signal-to-noise ratio of the image obtained after interpolating the maximum value filtered image using the target interpolation algorithm. This also includes: performing binary value conversion processing on each color channel value corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm, and then inputting them into the Hofit neural network after a set number of training iterations.

[0057] The present invention has the following significant technical effects:

[0058] (1) Based on the pixel value gradient of each pixel, determine whether each pixel in the maximum value filtered image is an edge pixel. Input the color channel values ​​corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm into the Hofit neural network after completing a set number of training iterations. Run the Hofit neural network after completing a set number of training iterations to obtain the signal-to-noise ratio of the image obtained after interpolating the maximum value filtered image using the target interpolation algorithm. The set number of training iterations is inversely related to the numerical mapping of each non-edge pixel in the maximum value filtered image.

[0059] (2) The interpolation algorithm corresponding to the maximum signal-to-noise ratio among the various interpolation algorithms obtained by the Hofit neural network after completing a set number of training iterations is used as the reference interpolation algorithm. The reference interpolation algorithm is then used to interpolate the maximum value filtered image to obtain the optimized operation screen corresponding to the maximum value filtered image, thereby completing the interpolation processing with the best interpolation effect customized for the maximum value filtered image.

[0060] Those skilled in the art can make modifications and variations to the present invention after reading the above description of preferred embodiments. Therefore, the scope of the present invention is limited only by the claims.

Claims

1. An image data application processing system, characterized in that, The system includes: A computer processing device serving an image data application, for receiving a currently operating image input by the image data application; A sequential mapping device, serving an image data application and connected to the computer processing device, includes an arithmetic mean filtering device, a dynamic processing device, and a maximum value filtering device. The dynamic processing device is connected to both the arithmetic mean filtering device and the maximum value filtering device. The arithmetic mean filtering device performs arithmetic mean filtering on a received image to obtain and output a corresponding arithmetic mean filtered image. The dynamic processing device performs white balance processing based on a dynamic threshold on the received arithmetic mean filtered image to obtain and output a corresponding dynamic processed image. The maximum value filtering device performs maximum value filtering on the received dynamic processed image to obtain and output a corresponding maximum value filtered image. A content determination device, connected to the sequence mapping device, is used to receive the maximum value filtered image, determine whether each pixel in the maximum value filtered image is an edge pixel based on the pixel value gradient of each pixel, and input the color channel values ​​corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm into the Hofit neural network after it has been trained a set number of times, and run the Hofit neural network after it has been trained a set number of times to obtain the signal-to-noise ratio of the image obtained after interpolating the maximum value filtered image using the target interpolation algorithm; wherein, the value of the set number of times has an inverse numerical mapping relationship with each non-edge pixel in the maximum value filtered image; The traversal processing device is connected to the content judgment device and is used to take the interpolation algorithm corresponding to the maximum signal-to-noise ratio among the various interpolation algorithms obtained by the Hofit neural network after completing a set number of training iterations as the reference interpolation algorithm. An interpolation execution device, connected to the traversal processing device, is used to perform interpolation processing on the maximum value filtered image using a reference interpolation algorithm to obtain an optimized operation screen corresponding to the maximum value filtered image.

2. The image data application processing system as described in claim 1, characterized in that: The color channel values ​​corresponding to each edge pixel in the maximum value filtered image, the total number of non-edge pixels in the maximum value filtered image, the sharpness of the maximum value filtered image, the number of pixels in the maximum value filtered image, and the algorithm identifier of the target interpolation algorithm are all input into the Hofit neural network after it has been trained a set number of times. The Hofit neural network after it has been trained a set number of times is then run to obtain the signal-to-noise ratio of the image obtained after interpolating the maximum value filtered image using the target interpolation algorithm. This includes simulation processing to model the Hofit neural network after it has been trained a set number of times using numerical simulation mode.

3. The image data application processing system as described in claim 2, characterized in that, The system also includes: A heat detection device is disposed near the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, and is respectively connected to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device; The heat detection device, which is located near the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device and is connected to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device respectively, includes: the heat detection device being used to perform on-site measurement of the current heat of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device respectively.

4. The image data application processing system as described in claim 2, characterized in that, The system also includes: A throughput detection device is disposed near the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, and is respectively connected to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device; The throughput detection device, which is located near the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device and is connected to each of the following: the throughput detection device is used to perform on-site measurement of the current data throughput of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device.

5. The image data application processing system as described in any one of claims 2-4, characterized in that: An image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

6. The image data application processing system as described in claim 5, characterized in that: The image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively. This includes performing homomorphic filtering on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

7. The image data application processing system as described in claim 5, characterized in that: The image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively. This includes performing Wiener filtering on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

8. The image data application processing system as described in claim 5, characterized in that: The image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively. This includes performing arithmetic mean filtering on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

9. The image data application processing system as described in claim 5, characterized in that: The image processing chip is used to perform image data processing on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively. This includes performing harmonic mean filtering on the output data of the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device to obtain the output processing data corresponding to the content judgment device, the arithmetic mean filtering device, the dynamic processing device, and the maximum value filtering device, respectively.

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