Image big data enhancement system using algorithm analysis

Abstract

The invention relates to an image big data enhancement system based on algorithm analysis, and the system comprises a target screening mechanism which is used for applying various enhancement algorithms to an artificial intelligence model in a time-sharing manner, so as to obtain each definition corresponding to each enhancement algorithm outputted by the artificial intelligence model in a time-sharing manner, outputting the enhancement algorithm corresponding to the definition of the highest value as a target enhancement algorithm; and the enhancement operation mechanism is connected with the target screening mechanism and is used for performing enhancement processing on the instant sharpening image by adopting the target enhancement algorithm so as to obtain a final optimized image corresponding to the instant sharpening image. The image big data enhancement system based on algorithm analysis is stable in operation and intelligent in control. Due to the fact that various enhancement algorithms can be traversed to obtain the optimal enhancement algorithm of the real-time picture of the doctor seeing scene in the doctor seeing hall of the hospital, the intelligent level of picture content processing is improved.

Description

Technical Field

[0001] The present invention relates to the field of image enhancement, and in particular to an image big data enhancement system using algorithm analysis. Background Art

[0002] The method of image enhancement is to add some information or transform data to the original image by certain means, selectively highlight the features of interest in the image or suppress (cover) some unnecessary features in the image, so that the image matches the visual response characteristics. In the process of image enhancement, the reasons for image degradation are not analyzed, and the processed image may not be close to the original image. Image enhancement technology can be divided into two categories: algorithms based on the spatial domain and algorithms based on the frequency domain, depending on the different spaces where the enhancement process is located.

[0003] Currently, in order to continuously monitor the hospital's treatment hall, it is necessary to use image acquisition equipment to capture real-time images of the treatment scenes inside the hospital's treatment hall at different times. The real-time images of the treatment scenes inside the hospital's treatment hall have different contents at different times, and the enhancement algorithms required to obtain the best image quality are also different. How to match the optimal enhancement algorithm for different video picture contents is one of the technical problems that need to be solved at present.

[0004] The related technical content in the prior art discloses "a method and system for intelligent analysis of pipe gallery videos based on cloud processing", and the application publication number is CN112861681A. The method includes: obtaining normal and abnormal learning sample sets of pipe gallery; training deep neural network classifier; using the trained deep neural network classifier to intelligently analyze the newly acquired images to be identified; performing data enhancement processing to eliminate the interference of uneven frequencies between sample categories; using the picture enhancement algorithm to pre-process images with too dark brightness to eliminate the interference of weak light sources; performing time continuity analysis to eliminate the influence of camera shaking. The invention can effectively identify and analyze pipe gallery videos, quickly carry out abnormal warnings, and improve the efficiency of daily operation, maintenance and management of pipe gallery. Summary of the invention

[0005] In order to solve the above problems, the present invention provides an image big data enhancement system using algorithm analysis, which can apply various enhancement algorithms to an artificial intelligence model in a time-sharing manner to obtain the respective clarity corresponding to the various enhancement algorithms output by the artificial intelligence model in a time-sharing manner, output the enhancement algorithm corresponding to the highest clarity value as the target enhancement algorithm, and use the target enhancement algorithm to perform enhancement processing on the instant sharpened image to obtain a final optimized image corresponding to the instant sharpened image. At the same time, the scene monitoring device arranged directly above the center of the hospital's treatment hall is triggered only when the current moment is within the treatment time interval to perform real-time image acquisition of the treatment scene inside the hospital's treatment hall, thereby minimizing the waste of energy consumption of the monitoring system.

[0006] According to the present invention, a system for enhancing image big data using algorithm analysis is provided, the system comprising: The time period judgment device is arranged in the control room of the hospital's consultation hall, and is used to send a monitoring trigger signal when the current moment is within the consultation time interval, and is also used to send a monitoring termination signal when the current moment is within the non-consultation time interval; A scene monitoring device is arranged just above the center of the hospital's consultation hall and connected to the time period determination device, and is used to perform real-time image acquisition of the consultation scene inside the hospital's consultation hall upon receiving the monitoring trigger signal to obtain the corresponding internal consultation image; A sequential mapping device, arranged in a control room of a hospital consultation hall and connected to the scene monitoring device, comprises a primary mapping unit, an intermediate mapping unit and a final mapping unit, wherein the intermediate mapping unit is respectively connected to the primary mapping unit and the final mapping unit, and the primary mapping unit is also connected to the scene monitoring device, and is used to perform bilinear interpolation processing on the received internal consultation picture to obtain and output a corresponding content processing image, the intermediate mapping unit is also used to perform box filtering processing on the received content processing image to obtain and output a corresponding box filtering image, and the final mapping unit is also used to perform sharpening processing based on a USM filter on the received box filtering image to obtain and output a corresponding instant sharpened image; A model application mechanism, connected to the sequential mapping device, for performing multiple training processes on the deep feedforward network to obtain an artificial intelligence model, wherein the number of training times is inversely correlated with the contrast of the received instant sharpened image, and simultaneously analyzing the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image, generally inputting the binary value of the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image and the algorithm identifier of the current enhancement algorithm into the artificial intelligence model, and running the artificial intelligence model to obtain the clarity of the picture obtained after the current enhancement algorithm is executed on the instant sharpened image as output; A target screening mechanism, connected to the model application mechanism, is used to apply various enhancement algorithms to the artificial intelligence model in a time-sharing manner to obtain the respective sharpnesses corresponding to the various enhancement algorithms output by the artificial intelligence model in a time-sharing manner, and output the enhancement algorithm corresponding to the sharpness with the highest value as the target enhancement algorithm; The enhancement operation mechanism is connected to the target screening mechanism and is used to perform enhancement processing on the instant sharpening image using the target enhancement algorithm to obtain a final optimized image corresponding to the instant sharpening image.

[0007] The image big data enhancement system using algorithm analysis of the present invention is stable in operation and intelligent in control. Since it can traverse various enhancement algorithms to obtain the best enhancement algorithm for the real-time image of the medical scene inside the hospital's medical hall, the intelligent level of image content processing is improved. DETAILED DESCRIPTION

[0008] The following is a detailed description of an embodiment of the image big data enhancement system using algorithm analysis of the present invention.

[0009] First embodiment

[0010] According to a first embodiment of the present invention, a system for enhancing image big data using algorithm analysis is shown, the system comprising: The time period judgment device is arranged in the control room of the hospital's consultation hall, and is used to send a monitoring trigger signal when the current moment is within the consultation time interval, and is also used to send a monitoring termination signal when the current moment is within the non-consultation time interval; For example, a time period determination device is arranged in a control room of a hospital consultation hall, and is used to send a monitoring trigger signal when the current moment is within a consultation time interval, and is also used to send a monitoring termination signal when the current moment is within a non-consultation time interval. The time period determination device includes an interval analysis unit and a signal sending unit, and the signal sending unit is connected to the interval analysis unit, and the two cooperate to send a monitoring trigger signal when the current moment is within the consultation time interval, and is also used to send a monitoring termination signal when the current moment is within the non-consultation time interval; A scene monitoring device is arranged just above the center of the hospital's consultation hall and connected to the time period determination device, and is used to perform real-time image acquisition of the consultation scene inside the hospital's consultation hall upon receiving the monitoring trigger signal to obtain the corresponding internal consultation image; A sequential mapping device, arranged in a control room of a hospital consultation hall and connected to the scene monitoring device, comprises a primary mapping unit, an intermediate mapping unit and a final mapping unit, wherein the intermediate mapping unit is respectively connected to the primary mapping unit and the final mapping unit, and the primary mapping unit is also connected to the scene monitoring device, and is used to perform bilinear interpolation processing on the received internal consultation picture to obtain and output a corresponding content processing image, the intermediate mapping unit is also used to perform box filtering processing on the received content processing image to obtain and output a corresponding box filtering image, and the final mapping unit is also used to perform sharpening processing based on a USM filter on the received box filtering image to obtain and output a corresponding instant sharpened image; A model application mechanism, connected to the sequential mapping device, for performing multiple training processes on the deep feedforward network to obtain an artificial intelligence model, wherein the number of training times is inversely correlated with the contrast of the received instant sharpened image, and simultaneously analyzing the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image, generally inputting the binary value of the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image and the algorithm identifier of the current enhancement algorithm into the artificial intelligence model, and running the artificial intelligence model to obtain the clarity of the picture obtained after the current enhancement algorithm is executed on the instant sharpened image as output; A target screening mechanism, connected to the model application mechanism, is used to apply various enhancement algorithms to the artificial intelligence model in a time-sharing manner to obtain the respective sharpnesses corresponding to the various enhancement algorithms output by the artificial intelligence model in a time-sharing manner, and output the enhancement algorithm corresponding to the sharpness with the highest value as the target enhancement algorithm; An enhancement operation mechanism, connected to the target screening mechanism, for performing enhancement processing on the instant sharpened image using the target enhancement algorithm to obtain a final optimized image corresponding to the instant sharpened image; Wherein, the scene monitoring device is also used to terminate the real-time image collection of the medical scene inside the hospital's medical hall when receiving the monitoring termination signal.

[0011] Second embodiment

[0012] According to the second embodiment of the present invention, a large image data enhancement system using algorithm analysis is shown.

[0013] Compared with the first embodiment, the image big data enhancement system using algorithm analysis shown in the second embodiment of the present invention may also include: a width sensor, connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, respectively, and used to measure the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively; Wherein, the width sensor device is respectively connected with the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, and is used to respectively measure the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, including: the width sensor device includes a plurality of width measurement units, which are respectively connected with the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, so as to complete the measurement of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit; Wherein, the width sensor device comprises a plurality of width measurement units, which are used to be respectively connected with the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit to complete the respective measurement of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, including: the plurality of width measurement units are a plurality of width sensors, which are used to be respectively connected with the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit to complete the respective measurement of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit; Wherein, the plurality of width measurement units are a plurality of width sensors, which are used to be connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively, so as to complete the respective measurement of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively, including: the plurality of width sensors have the same structure; Among them, the multiple width measurement units are multiple width sensors, which are used to be connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively, so as to complete the respective measurements of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, and also include: the multiple width sensors have the same width measurement upper limit value and width measurement lower limit value.

[0014] Third embodiment

[0015] The third embodiment of the present invention shows a system for enhancing big image data using algorithm analysis.

[0016] Compared with the first embodiment, the image big data enhancement system using algorithm analysis shown in the third embodiment of the present invention may also include: a width display mechanism, connected to the plurality of width measurement units of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, respectively, and used for synchronously displaying the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively; Wherein, the width display mechanism is respectively connected to the plurality of width measurement units of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, and is used to synchronously display the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively, and comprises: the width display mechanism is an LED display array; Among them, the width display mechanism is respectively connected to multiple width measurement units of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, and is used to synchronously display the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively, including: the width display mechanism is an LCD display array.

[0017] In addition, in the image big data enhancement system using algorithm analysis, multiple training processes are performed on the deep feedforward network to obtain an artificial intelligence model, the number of training times is inversely correlated with the contrast of the received instant sharpened image, and the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image are analyzed at the same time, and the binary values ​​of the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image and the algorithm identifier of the current enhancement algorithm are generally input into the artificial intelligence model, and the artificial intelligence model is run to obtain its output. The clarity of the picture obtained after the current enhancement algorithm is executed on the instant sharpened image includes: using a numerical simulation mode to complete multiple training processes performed on the deep feedforward network to obtain the artificial intelligence model.

[0018] It can be seen that the present invention mainly has the following three significant technical effects: First: performing multiple training processes on the deep feedforward network to obtain an artificial intelligence model, the number of training times is inversely correlated with the contrast of the received instant sharpened image, and simultaneously parsing the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image, inputting the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image and the binary value of the algorithm identifier of the current enhancement algorithm into the artificial intelligence model, and running the artificial intelligence model to obtain the clarity of the picture obtained after the current enhancement algorithm is executed on the instant sharpened image as output; Secondly: applying various enhancement algorithms to the artificial intelligence model in a time-sharing manner to obtain the respective sharpnesses corresponding to the various enhancement algorithms output by the artificial intelligence model in a time-sharing manner, outputting the enhancement algorithm corresponding to the highest value of sharpness as the target enhancement algorithm, and performing enhancement processing on the instant sharpening image using the target enhancement algorithm to obtain a final optimized image corresponding to the instant sharpening image; Finally: only when the current moment is within the consultation time zone, the scene monitoring device arranged just above the center of the hospital's consultation hall is triggered to perform real-time image acquisition of the consultation scene inside the hospital's consultation hall, thereby minimizing the waste of energy consumption of the monitoring system.

[0019] While embodiments of the present invention have been shown and described with reference to exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made without departing from the spirit and scope of the embodiments of the invention as defined by the following claims.

Claims

1. An image big data enhancement system using algorithm analysis, characterized in that: The system comprises: The time period judgment device is arranged in the control room of the hospital's consultation hall, and is used to send a monitoring trigger signal when the current moment is within the consultation time interval, and is also used to send a monitoring termination signal when the current moment is within the non-consultation time interval; A scene monitoring device is arranged just above the center of the hospital's consultation hall and connected to the time period determination device, and is used to perform real-time image acquisition of the consultation scene inside the hospital's consultation hall upon receiving the monitoring trigger signal to obtain the corresponding internal consultation image; A sequential mapping device, arranged in a control room of a hospital consultation hall and connected to the scene monitoring device, comprises a primary mapping unit, an intermediate mapping unit and a final mapping unit, wherein the intermediate mapping unit is respectively connected to the primary mapping unit and the final mapping unit, and the primary mapping unit is also connected to the scene monitoring device, and is used to perform bilinear interpolation processing on the received internal consultation picture to obtain and output a corresponding content processing image, the intermediate mapping unit is also used to perform box filtering processing on the received content processing image to obtain and output a corresponding box filtering image, and the final mapping unit is also used to perform sharpening processing based on a USM filter on the received box filtering image to obtain and output a corresponding instant sharpened image; A model application mechanism, connected to the sequential mapping device, for performing multiple training processes on the deep feedforward network to obtain an artificial intelligence model, wherein the number of training times is inversely correlated with the contrast of the received instant sharpened image, and simultaneously analyzing the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image, generally inputting the binary value of the number of pixel rows, the number of pixel columns, and the total number of noise types of the instant sharpened image and the algorithm identifier of the current enhancement algorithm into the artificial intelligence model, and running the artificial intelligence model to obtain the clarity of the picture obtained after the current enhancement algorithm is executed on the instant sharpened image as output; A target screening mechanism, connected to the model application mechanism, is used to apply various enhancement algorithms to the artificial intelligence model in a time-sharing manner to obtain the respective sharpnesses corresponding to the various enhancement algorithms output by the artificial intelligence model in a time-sharing manner, and output the enhancement algorithm corresponding to the sharpness with the highest value as the target enhancement algorithm; The enhancement operation mechanism is connected to the target screening mechanism and is used to perform enhancement processing on the instant sharpening image using the target enhancement algorithm to obtain a final optimized image corresponding to the instant sharpening image.

2. The image big data enhancement system using algorithm analysis as claimed in claim 1, characterized in that: The scene monitoring device is also used to terminate the real-time image acquisition of the medical scene inside the hospital's medical hall when receiving the monitoring termination signal.

3. The image big data enhancement system using algorithm analysis as claimed in claim 2, characterized in that: The system further comprises: a width sensor, connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, respectively, and used to measure the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively; Among them, the width sensor device is respectively connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, and is used to respectively measure the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit. The width sensor device includes multiple width measurement units, which are respectively connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit to complete the respective measurements of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit.

4. The image big data enhancement system using algorithm analysis as claimed in claim 3, characterized in that: The width sensor device includes a plurality of width measurement units, which are used to be respectively connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit to complete the respective measurements of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit. The plurality of width measurement units are a plurality of width sensors, which are used to be respectively connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit to complete the respective measurements of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit.

5. The image big data enhancement system using algorithm analysis as claimed in claim 4, characterized in that: The multiple width measurement units are multiple width sensors, which are used to be connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively, so as to complete the respective measurements of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, including: the structures of the multiple width sensors are the same.

6. The image big data enhancement system using algorithm analysis as claimed in claim 5, characterized in that: The multiple width measurement units are multiple width sensors, which are used to be connected to the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively, so as to complete the respective measurements of the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit. The multiple width sensors also have the same width measurement upper limit value and width measurement lower limit value.

7. The image big data enhancement system using algorithm analysis as described in any one of claims 3 to 6, characterized in that: The system further comprises: The width display mechanism is respectively connected to the multiple width measurement units of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, and is used to synchronously display the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit.

8. The image big data enhancement system using algorithm analysis as claimed in claim 7, characterized in that: The width display mechanism is respectively connected to the multiple width measurement units of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, and is used to synchronously display the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively, including: the width display mechanism is an LED display array.

9. The image big data enhancement system using algorithm analysis as claimed in claim 7, characterized in that: The width display mechanism is respectively connected to the multiple width measurement units of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit, and is used to synchronously display the current real-time width values ​​of the model application mechanism, the primary mapping unit, the intermediate mapping unit and the final mapping unit respectively, including: the width display mechanism is an LCD display array.