Smart city monitoring image enhancement system

Abstract

The invention relates to a smart city monitoring image enhancement system. The system comprises a signal capturing mechanism, a box type filtering device, a curve processing device, a direction sharpening device, a signal pre-judging device and a self-adaptive enhancement device. The smart city monitoring image enhancement system is compact in structure and stable in operation.

Description

Technical Field

[0001] The present invention relates to the field of smart city management, and more specifically, to a smart city monitoring image enhancement system. Background Art

[0002] Smart city management is essential for the operation of modern cities, and many similar technologies are disclosed in the prior art. For example, CN119169543A discloses an abnormal behavior monitoring method, system and program product suitable for smart cities, wherein the method includes: based on a preset sampling moment, obtaining a continuous multi-frame sampling image and calculating the corresponding disparity map; using a segmentation algorithm to perform target segmentation on any calculated frame disparity map, and calculating the spatial information of each divided target based on the disparity map, wherein the spatial information at least includes the target position; based on the spatial information, each target in the continuous multi-frame disparity map is divided in turn to generate a corresponding target group; for any target group, the moving trajectory is calculated based on the target position and the preset sampling moment; when it is determined that the moving trajectory of at least two target groups meets the abnormal judgment rule, an abnormal behavior alarm is generated. Through the technical solution in this application, the moving trajectory of each target is directly calculated in the disparity map without the need for image recognition, which improves the universality of abnormal behavior detection in smart cities.

[0003] CN119152722A discloses a comprehensive public transportation control system for smart cities. By processing three data items, namely, route lane data, route platform passenger flow and route signal light data, a static characterization value of bus driving data is obtained. Based on the static characterization value of bus driving data, bus schedules and routes can be formulated for bus companies. By obtaining a representative value of the road congestion level in the monitoring area based on the representative value of traffic flow, the traffic police department can judge the road congestion level, reasonably allocate police force and adjust the timing of traffic lights in a timely manner. Combining the static characterization value of bus driving data with the dynamic characterization value of bus driving data can provide the traffic police department with the causes and patterns of road congestion. Summary of the invention

[0004] In order to solve the technical problems in related fields, the present invention provides a smart city monitoring image enhancement system, which can use a signal capture mechanism set at the high-speed railway platform to perform an image signal capture operation on the environment where the high-speed railway platform is located to obtain a corresponding real-time captured image, and then customize and optimize the real-time captured image to obtain a direction-sharpened image. The set enhancement algorithm corresponding to the highest clarity among the various picture clarities obtained by intelligent prediction of each set enhancement algorithm is used as the target enhancement algorithm. The target enhancement algorithm is used to perform enhancement processing on the direction-sharpened image obtained after customized optimization processing of the monitoring picture of the high-speed railway platform to obtain a corresponding adaptive enhanced image, thereby selecting the best enhancement algorithm suitable for the signal characteristics of different image signals.

[0005] According to the present invention, a smart city monitoring image enhancement system is provided, the system comprising: The signal capture mechanism is arranged at the high-speed railway platform, and is used to perform an image signal capture operation on the environment where the high-speed railway platform is located when there is a high-speed railway train on the track corresponding to the high-speed railway platform, so as to obtain a corresponding real-time captured image; a box filter device connected to the signal capture mechanism and configured to perform a box filter process on the received instant capture image to obtain and output a corresponding box filter image; A curve processing device, connected to the box filter device, for adjusting the maximum curvature of each curve in the received box filter image to be lower than or equal to a preset curve maximum curvature threshold, so as to obtain and output a corresponding curvature adjusted image; A directional sharpening device, connected to the curve processing device, for sequentially performing horizontal direction sharpening processing and vertical direction sharpening processing on the received curvature-adjusted image to obtain and output a corresponding directional sharpened image; A signal prediction device, connected to the direction sharpening device and comprising a grayscale analysis device, a learning processing device and a clarity prediction device, wherein the clarity prediction device is respectively connected to the grayscale analysis device and the learning processing device, and the signal prediction device is used to extract a plurality of information parameters from the direction sharpened image, wherein the plurality of information parameters include the number of pixel rows, pixel column values, respective maximum noise amplitudes corresponding to various noise types, and respective grayscale values ​​corresponding to each pixel point of the direction sharpened image, perform multiple learning on the deep neural network to obtain a deep neural network model, and based on the deep neural network model, intelligently predict the picture clarity obtained after the direction sharpened image is enhanced by the enhancement algorithm according to an algorithm ID corresponding to a certain set enhancement algorithm, the contrast of the direction sharpened image, and the plurality of information parameters, wherein the number of deep neural learning times is proportional to the resolution of the direction sharpened image; The adaptive enhancement device is connected to the signal prediction device, and is used to use the set enhancement algorithm corresponding to the highest clarity among the various picture clarities obtained by intelligent prediction of each set enhancement algorithm as the target enhancement algorithm, and use the target enhancement algorithm to perform enhancement processing on the directionally sharpened image to obtain the corresponding adaptive enhanced image.

[0006] Therefore, the present invention has at least the following four beneficial technical effects: Firstly, the set enhancement algorithm corresponding to the highest definition among the various picture definitions obtained through intelligent prediction of each set enhancement algorithm is used as the target enhancement algorithm, and the target enhancement algorithm is used to perform enhancement processing on the direction-sharpened image obtained after customized optimization processing of the monitoring picture of the high-speed railway platform to obtain the corresponding adaptive enhanced image, so as to select the best enhancement algorithm suitable for the signal characteristics of different image signals, thereby ensuring the image quality enhancement effect of each frame of the on-site monitoring image; Secondly: using a signal prediction device including a grayscale analysis device, a learning processing device and a clarity prediction device, wherein the clarity prediction device is connected to the grayscale analysis device and the learning processing device respectively, so as to provide prediction data for the enhancement effect of each enhancement algorithm on the directionally sharpened image; Again: extracting multiple information parameters from the direction-sharpened image, the multiple information parameters including the number of pixel rows, pixel column values, maximum noise amplitudes corresponding to various noise types, and grayscale values ​​corresponding to each pixel point of the direction-sharpened image, and intelligently predicting the image clarity obtained after the direction-sharpened image is enhanced by using the enhancement algorithm according to the algorithm ID corresponding to a certain enhancement algorithm, the contrast of the direction-sharpened image, and the multiple information parameters based on the deep neural network model; Finally: performing multiple learning on the deep neural network to obtain a deep neural network model, wherein the number of deep neural learning is proportional to the resolution of the directionally sharpened image.

[0007] The smart city monitoring image enhancement system of the present invention has a compact structure and stable operation. After obtaining the real-time captured image corresponding to the environment where the high-speed railway platform is located, the real-time captured image can be customized and optimized to obtain a directionally sharpened image, and the set enhancement algorithm corresponding to the highest clarity of each set enhancement algorithm obtained through intelligent prediction is used as the target enhancement algorithm, thereby selecting the best enhancement algorithm for different image signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Those skilled in the art may better understand the numerous advantages of the present invention by referring to the accompanying drawings, in which:

[0009] Figure 1It is a schematic diagram of the internal structure of the smart city monitoring image enhancement system according to the first embodiment of the present invention.

[0010] Figure 2 Schematic diagram of the internal structure of a smart city monitoring image enhancement system according to the second embodiment of the present invention.

[0011] Figure 3 Schematic diagram of the internal structure of a smart city monitoring image enhancement system according to the third embodiment of the present invention. DETAILED DESCRIPTION

[0012] Smart city management involves many aspects, among which the places with huge flow of people are the key and difficult points of management. Among them, stations are undoubtedly places with large flow of people. At present, with the development of high-speed rail technology, high-speed rail stations have accounted for a high proportion in cities. Among them, the high-speed rail platform refers to a platform parallel to the train door steps for passengers to board the train after entering the high-speed rail station; on ordinary roads, it usually refers to the roadside bus stop. The platform can be roughly divided into island platform, side platform, bay platform, cross-station station building, special station, and signal station. Early stations were usually used for both passengers and freight. Such stations still exist today; but in some countries and regions, freight is generally concentrated in major stations.

[0013] Due to the complexity of the high-speed railway platform environment and the diversity of content over time, the enhancement algorithms selected for different time-sharing acquisition images of the same high-speed railway platform scene may be different. How to select an enhancement algorithm suitable for the acquisition images of the same type of high-speed railway platform scene to ensure the best enhancement effect of different acquisition images is one of the difficult problems that need to be solved in the existing technology.

[0014] The implementation scheme of the smart city monitoring image enhancement system of the present invention will be described in detail below with reference to the accompanying drawings.

[0015] Figure 1 1 is a schematic diagram of the internal structure of a smart city monitoring image enhancement system according to a first embodiment of the present invention, the system comprising: The signal capture mechanism is arranged at the high-speed railway platform, and is used to perform an image signal capture operation on the environment where the high-speed railway platform is located when there is a high-speed railway train on the track corresponding to the high-speed railway platform, so as to obtain a corresponding real-time captured image; For example, a signal capture mechanism is provided at a high-speed railway platform, and is used to perform an image signal capture operation on the environment where the high-speed railway platform is located when a high-speed railway train exists on the track corresponding to the high-speed railway platform, so as to obtain a corresponding real-time captured image, including: the signal capture mechanism includes a train detection unit and a signal capture unit, and the train detection unit is used to complete the detection of whether there is a high-speed railway train on the track corresponding to the high-speed railway platform based on a train schedule or a train presence profile; The signal capture unit is connected to the train detection unit, and is used to trigger an image signal capture operation performed on the environment where the high-speed railway platform is located when the train detection unit detects that there is a high-speed railway train on the track corresponding to the high-speed railway platform, so as to obtain a corresponding real-time captured image; a box filter device connected to the signal capture mechanism and configured to perform a box filter process on the received instant capture image to obtain and output a corresponding box filter image; A curve processing device, connected to the box filter device, for adjusting the maximum curvature of each curve in the received box filter image to be lower than or equal to a preset curve maximum curvature threshold, so as to obtain and output a corresponding curvature adjusted image; A directional sharpening device, connected to the curve processing device, for sequentially performing horizontal direction sharpening processing and vertical direction sharpening processing on the received curvature-adjusted image to obtain and output a corresponding directional sharpened image; A signal prediction device, connected to the direction sharpening device and comprising a grayscale analysis device, a learning processing device and a clarity prediction device, wherein the clarity prediction device is respectively connected to the grayscale analysis device and the learning processing device, and the signal prediction device is used to extract a plurality of information parameters from the direction sharpened image, wherein the plurality of information parameters include the number of pixel rows, pixel column values, respective maximum noise amplitudes corresponding to various noise types, and respective grayscale values ​​corresponding to each pixel point of the direction sharpened image, perform multiple learning on the deep neural network to obtain a deep neural network model, and based on the deep neural network model, intelligently predict the picture clarity obtained after the direction sharpened image is enhanced by the enhancement algorithm according to an algorithm ID corresponding to a certain set enhancement algorithm, the contrast of the direction sharpened image, and the plurality of information parameters, wherein the number of deep neural learning times is proportional to the resolution of the direction sharpened image; An adaptive enhancement device, connected to the signal prediction device, used to use the set enhancement algorithm corresponding to the highest definition among the various picture definitions obtained by intelligent prediction of the various set enhancement algorithms as the target enhancement algorithm, and use the target enhancement algorithm to perform enhancement processing on the directionally sharpened image to obtain a corresponding adaptively enhanced image; Among them, the deep neural network is subjected to multiple learning to obtain a deep neural network model, and the deep neural network model is used to intelligently predict the picture clarity obtained after the direction-sharpened image is enhanced by the certain enhancement algorithm according to the algorithm ID corresponding to a certain setting enhancement algorithm, the contrast of the direction-sharpened image, and the multiple information parameters. The number of deep neural learning is proportional to the resolution of the direction-sharpened image, including: using a numerical simulation mode to implement the simulation operation of the processing process of intelligently predicting the picture clarity obtained after the direction-sharpened image is enhanced by the certain enhancement algorithm according to the algorithm ID corresponding to a certain setting enhancement algorithm, the contrast of the direction-sharpened image, and the multiple information parameters based on the deep neural network model.

[0016] Figure 2 Schematic diagram of the internal structure of the smart city monitoring image enhancement system according to the second embodiment of the present invention.

[0017] and Figure 1 different, Figure 2 The smart city monitoring image enhancement system can also include the following components: A humidity detection device is arranged near the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device and is connected to the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device respectively; Among them, the humidity detection device is arranged near the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device and is respectively connected to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device, including: the humidity detection device is used to respectively realize on-site measurement of the current humidity of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device.

[0018] Figure 3 Schematic diagram of the internal structure of a smart city monitoring image enhancement system according to the third embodiment of the present invention.

[0019] and Figure 1 different, Figure 3 The smart city monitoring image enhancement system can also include the following components: A thickness detection device is arranged near the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device and is connected to the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device respectively; Among them, the thickness detection device is arranged near the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device and is respectively connected to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device, including: the thickness detection device is used to respectively realize on-site measurement of the current thickness of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device.

[0020] Next, the specific structure of the smart city monitoring image enhancement system of the present invention is further described.

[0021] In the smart city monitoring image enhancement system according to any embodiment of the present invention: An embedded processing device is used to perform image data processing on the output data of the box filter device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain output processing data corresponding to the box filter device, the curve processing device, the direction sharpening device, and the signal prediction device, respectively.

[0022] In the smart city monitoring image enhancement system according to any embodiment of the present invention: Using an embedded processing device to perform image data processing on the output data of the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device respectively includes: performing color scale adjustment on the output data of the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device respectively.

[0023] In the smart city monitoring image enhancement system according to any embodiment of the present invention: Using an embedded processing device to perform image data processing on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively includes: performing image restoration on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively.

[0024] In the smart city monitoring image enhancement system according to any embodiment of the present invention: Using an embedded processing device to perform image data processing on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively includes: performing rotation correction on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively.

[0025] And in the smart city monitoring image enhancement system according to any embodiment of the present invention: Using an embedded processing device to perform image data processing on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively includes: performing alpha mean filtering processing on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively.

[0026] In addition, in the smart city monitoring image enhancement system, multiple learnings are performed on the deep neural network to obtain a deep neural network model, and the deep neural network model is used to intelligently predict the picture clarity obtained after the direction-sharpened image is enhanced using the certain enhancement algorithm according to the algorithm ID corresponding to the certain enhancement algorithm, the contrast of the direction-sharpened image, and the multiple information parameters. The number of deep neural learnings is proportional to the resolution of the direction-sharpened image and also includes: using a numerical mapping function to represent the numerical mapping relationship in which the number of deep neural learnings is proportional to the resolution of the direction-sharpened image.

[0027] Therefore, the descriptions limiting the shapes, etc. disclosed above are examples for easy understanding of the present invention and are not intended to limit the present invention. Therefore, the descriptions of component names that exclude part or all of the limitations on their shapes, materials, etc. are also included in the present invention.

Claims

1. A smart city monitoring image enhancement system, characterized in that: The system comprises: The signal capture mechanism is arranged at the high-speed railway platform, and is used to perform an image signal capture operation on the environment where the high-speed railway platform is located when there is a high-speed railway train on the track corresponding to the high-speed railway platform, so as to obtain a corresponding real-time captured image; a box filter device connected to the signal capture mechanism and configured to perform a box filter process on the received instant capture image to obtain and output a corresponding box filter image; A curve processing device, connected to the box filter device, for adjusting the maximum curvature of each curve in the received box filter image to be lower than or equal to a preset curve maximum curvature threshold, so as to obtain and output a corresponding curvature adjusted image; A directional sharpening device, connected to the curve processing device, for sequentially performing horizontal direction sharpening processing and vertical direction sharpening processing on the received curvature-adjusted image to obtain and output a corresponding directional sharpened image; A signal prediction device, connected to the direction sharpening device and comprising a grayscale analysis device, a learning processing device and a clarity prediction device, wherein the clarity prediction device is respectively connected to the grayscale analysis device and the learning processing device, and the signal prediction device is used to extract a plurality of information parameters from the direction sharpened image, wherein the plurality of information parameters include the number of pixel rows, pixel column values, respective maximum noise amplitudes corresponding to various noise types, and respective grayscale values ​​corresponding to each pixel point of the direction sharpened image, perform multiple learning on the deep neural network to obtain a deep neural network model, and based on the deep neural network model, intelligently predict the picture clarity obtained after the direction sharpened image is enhanced by the enhancement algorithm according to an algorithm ID corresponding to a certain set enhancement algorithm, the contrast of the direction sharpened image, and the plurality of information parameters, wherein the number of deep neural learning times is proportional to the resolution of the direction sharpened image; The adaptive enhancement device is connected to the signal prediction device, and is used to use the set enhancement algorithm corresponding to the highest clarity among the various picture clarities obtained by intelligent prediction of each set enhancement algorithm as the target enhancement algorithm, and use the target enhancement algorithm to perform enhancement processing on the directionally sharpened image to obtain the corresponding adaptive enhanced image.

2. The smart city monitoring image enhancement system according to claim 1, characterized in that: The deep neural network is subjected to multiple learning to obtain a deep neural network model, and based on the deep neural network model, the algorithm ID corresponding to a certain set enhancement algorithm, the contrast of the direction-sharpened image, and the multiple information parameters are intelligently predicted to obtain the picture clarity after the direction-sharpened image is enhanced by using the certain enhancement algorithm, and the number of deep neural network learning is proportional to the resolution of the direction-sharpened image, including: using a numerical simulation mode to implement the simulation operation of the processing process of intelligently predicting the picture clarity after the direction-sharpened image is enhanced by using the certain enhancement algorithm based on the deep neural network model according to the algorithm ID corresponding to a certain set enhancement algorithm, the contrast of the direction-sharpened image, and the multiple information parameters.

3. The smart city monitoring image enhancement system according to claim 2, characterized in that: The system further comprises: A humidity detection device is arranged near the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device and is connected to the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device respectively; Among them, the humidity detection device is arranged near the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device and is respectively connected to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device, including: the humidity detection device is used to respectively realize on-site measurement of the current humidity of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device.

4. The smart city monitoring image enhancement system according to claim 2, characterized in that: The system further comprises: A thickness detection device is arranged near the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device and is connected to the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device respectively; Among them, the thickness detection device is arranged near the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device and is respectively connected to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device, including: the thickness detection device is used to respectively realize on-site measurement of the current thickness of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device.

5. The smart city monitoring image enhancement system according to any one of claims 2 to 4, characterized in that: An embedded processing device is used to perform image data processing on the output data of the box filter device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain output processing data corresponding to the box filter device, the curve processing device, the direction sharpening device, and the signal prediction device, respectively.

6. The smart city monitoring image enhancement system according to claim 5, characterized in that: Using an embedded processing device to perform image data processing on the output data of the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device respectively includes: performing color scale adjustment on the output data of the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filter device, the curve processing device, the direction sharpening device, and the signal prediction device respectively.

7. The smart city monitoring image enhancement system according to claim 5, characterized in that: Using an embedded processing device to perform image data processing on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively includes: performing image restoration on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively.

8. The smart city monitoring image enhancement system as claimed in claim 5, characterized in that: Using an embedded processing device to perform image data processing on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively includes: performing rotation correction on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively.

9. The smart city monitoring image enhancement system according to claim 5, characterized in that: Using an embedded processing device to perform image data processing on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively includes: performing alpha mean filtering processing on the output data of the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device to obtain the output processing data corresponding to the box-type filtering device, the curve processing device, the direction sharpening device, and the signal prediction device respectively.

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