CN111175618B - Local correlation filtering method suitable for simultaneously processing infrared and local discharge data - Google Patents

Local correlation filtering method suitable for simultaneously processing infrared and local discharge data Download PDF

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CN111175618B
CN111175618B CN201911425743.2A CN201911425743A CN111175618B CN 111175618 B CN111175618 B CN 111175618B CN 201911425743 A CN201911425743 A CN 201911425743A CN 111175618 B CN111175618 B CN 111175618B
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infrared
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CN111175618A (en
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胡华
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Zhejiang Heika Electric Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry

Abstract

The invention discloses a method for simultaneously processing infrared and partial dischargeA method of local correlation filtering of data, comprising: collecting partial discharge data, or collecting infrared data, or simultaneously collecting partial discharge data and infrared data; taking the first frame data as a key frame, marking as an I frame, and dividing the I frame into a plurality of matrixes M; obtaining a matrix by using a weighted mean filtering algorithm for each matrix M in the I frame
Figure DDA0002349135940000011
Further obtain
Figure DDA0002349135940000012
A frame; marking the next frame as a P frame, and carrying out denoising processing on the P frame to obtain the P frame
Figure DDA0002349135940000013
A frame; computing each point in the P-frame
Figure DDA0002349135940000014
The variance of the difference value of each point in the frame, if the value of any variance is larger than the threshold value, the P frame is taken as an I frame; if either value is less than the threshold, then the next frame is processed. The method can effectively remove the noise of the infrared and partial discharge data at the same time, and provides effective data guarantee for subsequent infrared image processing and partial discharge data display. The invention can process the infrared or partial discharge data independently and can process the infrared and partial discharge data collected simultaneously.

Description

Local correlation filtering method suitable for simultaneously processing infrared and local discharge data
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a local correlation filtering method suitable for simultaneously processing infrared and partial discharge data.
Background
In the field of power equipment, partial discharge data refers to a discharge spectrum image formed by digital-to-analog conversion of electromagnetic wave signals around charged equipment acquired by various instruments. The infrared data is an infrared thermography image obtained by reflecting an infrared radiation energy distribution pattern of a detected target received by an infrared detector and an optical imaging objective on a photosensitive element of the infrared detector, and the thermography image corresponds to a thermal distribution field on the surface of an object. Conventionally, thermal infrared imagers convert the invisible infrared energy emitted by an object into visible thermal images, the different colors on the thermal images representing the different temperatures of the object being measured.
In the prior art, in the process of independent infrared data processing, inherent noise of an infrared focal plane array is effectively solved through a two-point correction algorithm and a single-point correction algorithm, and a better image effect is obtained through image processing methods such as edge detection and image blurring. In the process of processing the single partial discharge data, the noise of the data is effectively removed through conventional methods such as mean value filtering, median filtering and the like, and better data is obtained. However, when data processing is performed on infrared and partial discharge data simultaneously, because two signals are acquired simultaneously, interference to a certain extent may be generated, and if the two methods are continuously used, not only the implementation difficulty of the FPGA may be increased, but also the denoising effect for a newly generated interference signal is not very obvious.
In view of the above, it is necessary to develop a new method, which is applicable to both infrared and partial discharge data, and also applicable to infrared or partial discharge data alone, so as to reduce noise and improve data quality.
Disclosure of Invention
The invention provides a local correlation filtering method suitable for simultaneously processing infrared and partial discharge data, which can effectively remove noise of the infrared and partial discharge data simultaneously and provide effective data guarantee for subsequent infrared image processing and partial discharge data display.
The technical scheme of the invention is as follows: a local correlation filtering method suitable for simultaneous processing of infrared and partial discharge data, comprising the steps of:
s1, collecting partial discharge data, or collecting infrared data, or collecting partial discharge data and infrared data at the same time;
s2, taking the first frame data as a key frame, marking as an I frame, and dividing the I frame into a plurality of matrixes M;
s3, obtaining a matrix by each matrix M in the I frame through a weighted mean filtering algorithm
Figure BDA0002349135920000021
Further obtain
Figure BDA0002349135920000022
A frame;
s4, recording the next frame as a P frame, and carrying out denoising processing on the P frame to obtain the P frame
Figure BDA0002349135920000023
A frame;
s5, calculating P (i, j) and each point in the P frame
Figure BDA0002349135920000024
Each point in the frame
Figure BDA0002349135920000025
Variance S of difference of (i, j)XIf any of the variances SXIs greater than a threshold value SthresholdTaking the P frame as an I frame; if any SXIs less than a threshold value SthresholdThen the next frame is processed.
Preferably, in step S3, selecting a filtering window W with each point M (I, j) of the matrix M as a center, calculating a weight of each point M (I, j) through formulas (1) to (3), and performing weighted calculation on each point in the matrix M and a corresponding weight thereof through formula (4), and using the result as an output value of the center point of the filtering window W, and similarly, calculating an output value of the center point of the filtering window W of each point M (I, j) in all the matrices M of the frame I to obtain an output value I (I, j);
Ak＝|Mk-Mean(M[f(i，j)])| (1)；
Figure BDA0002349135920000026
Figure BDA0002349135920000031
Figure BDA0002349135920000032
wherein Mean (M [ f (i, j) ]) is the Mean of each point M (i, j) in the matrix M;
k is the number of M inner points;
Mkvalues for each point in the matrix;
Akfor each point in the matrix M and Mean (M [ f (i, j))]) The absolute value of the difference;
t is all AkRepresents a threshold value;
qk (i, j) is the weight of each point M (i, j) in the matrix M;
Figure BDA0002349135920000033
is the output value of the central point of the filtering window W.
Preferably, in step S4, the denoising process is performed according to the following formula (5) to obtain
Figure BDA0002349135920000034
The number of frames in a frame is,
Figure BDA0002349135920000035
preferably, the partial discharge data is 16-bit AD data.
Preferably, the size of the filter window W is 3 × 3.
Preferably, the I-frame is divided into several matrices at a resolution of 32 × 24.
Preferably, the I-frame is divided into 402 matrices at a resolution of 32 × 24.
Preferably, the threshold value Sthreshold640 x 480 x 64.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the key frame is selected by utilizing the unique characteristic of infrared and partial discharge data, namely the characteristic that the data is slowly changed, the common frame is calculated with the key frame to carry out interframe denoising processing, the common pixel jitter problem of the infrared and partial discharge data is effectively solved, the key frame is subjected to matrix processing, each matrix adopts a weighted filtering algorithm, noise is removed by modules, and the image blurring problem caused by integral data denoising is avoided. In addition, the invention has wider application range, can process infrared data or partial discharge data independently, and can process infrared and partial discharge data acquired simultaneously.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
Example 1
A local correlation filtering method suitable for simultaneous processing of infrared and partial discharge data, comprising the steps of:
s1, collecting partial discharge data and infrared data at the same time, wherein the infrared resolution is 640 multiplied by 480, and the partial discharge data is 1024 bytes as an example;
s2, taking the first frame data as a key frame, and recording as an I frame; dividing the I frame into 402 small matrixes M according to the resolution of 32 multiplied by 24, and respectively recording each small matrix M as M1、M2、…MN-2、MN-1、MNWherein M isNAnd MN-1Is partial discharge data; in this embodiment, the matrix is divided into 402 small matrices, denoted as M1、M2、…M400、M401、M402Wherein M is401And M402The data of each point in the matrix is marked as M (i, j);
s3, selecting a filtering window W with the size of 3 multiplied by 3 by taking each point M (I, j) of the matrix M as a center, calculating the weight of each point M (I, j) through formulas (1) to (3), performing weighted calculation on each point in the matrix M and the corresponding weight through a formula (4), taking the result as the output value of the center point of the filtering window W, and calculating the output value of the center point of the filtering window W of each point M (I, j) in all the matrixes M of the frame I in the same way to obtain an output value I (I, j);
Ak＝|Mk-Mean(M[f(i，j)])| (1)；
Figure BDA0002349135920000041
Figure BDA0002349135920000051
Figure BDA0002349135920000052
wherein Mean (M [ f (i, j) ]) is the Mean of each point M (i, j) in the matrix M;
k is the number of M inner points;
Mkvalues for each point in the matrix;
Akfor each point in the matrix M and Mean (M [ f (i, j))]) The absolute value of the difference;
t is all AkRepresents a threshold value;
Qk(i, j) is the weight of each point M (i, j) in the matrix M;
Figure BDA0002349135920000053
is the output value of the central point of the filtering window W.
S4, marking the next frame as a P frame, carrying out denoising processing on the P frame, and calculating a point after filtering according to the following formula (5);
Figure BDA0002349135920000054
s5, calculating the variance S of the difference value between each point of the P frame and the I frameXIf S isXLess than threshold 640 x 480 x 64, then processing the next frame; otherwise, replacing the I frame with the P frame, and calculating the output value of each point of the P frame after weighted filtering according to formulas (1) to (4).
Example 2
The present embodiment is different from embodiment 1 in that only infrared data is collected in the present embodiment.
Example 3
The present embodiment is different from embodiment 1 in that only partial discharge data is collected in the present embodiment.

Claims (1)

1. A local correlation filtering method suitable for simultaneously processing infrared and local discharge data, comprising the steps of:
s1, collecting partial discharge data and infrared data at the same time;
s2, taking the first frame data as a key frame, marking as an I frame, and dividing the I frame into a plurality of matrixes M;
s3, obtaining a matrix by each matrix M in the I frame through a weighted mean filtering algorithm
Figure FDA0003552001100000011
Further obtain
Figure FDA0003552001100000012
A frame;
s4, recording the next frame as a P frame, and carrying out denoising processing on the P frame to obtain the P frame
Figure FDA0003552001100000013
A frame;
s5, calculating
Figure FDA0003552001100000014
Each point in the frame
Figure FDA0003552001100000015
And
Figure FDA0003552001100000016
each point in the frame
Figure FDA0003552001100000017
Variance S of the difference ofXIf any of the variances SXIs greater than a threshold value SthresholdTaking the P frame as an I frame; if any SXIs less than a threshold value SthresholdThen processing the next frame;
in step S3, selecting a filtering window W with each point M (I, j) of the matrix M as a center, calculating a weight of each point M (I, j) according to formulas (1) to (3), and performing weighted calculation on each point in the matrix M and a corresponding weight thereof according to formula (4), and using the result as an output value of the center point of the filtering window W;
Ak＝|Mk-Mean(M[f(i,j)])| (1)；
Figure FDA0003552001100000018
Figure FDA0003552001100000019
Figure FDA00035520011000000110
wherein Mean (M [ f (i, j) ]) is the Mean of each point M (i, j) in the matrix M;
k is the number of M inner points;
Mkvalues for each point in the matrix;
Akfor each point in the matrix M and Mean (M [ f (i, j))]) The absolute value of the difference;
t is all AkRepresents a threshold value;
Qk(i, j) is the weight of each point M (i, j) in the matrix M;
Figure FDA0003552001100000021
the output value is the central point of the filtering window W;
in the step S4, the denoising process is performed according to the following formula (5) to obtain
Figure FDA0003552001100000022
The number of frames in a frame is,
Figure FDA0003552001100000023
the partial discharge data adopts 16-bit AD data;
the size of the filtering window W is 3 x 3;
dividing the I frame into 402 matrixes according to the resolution of 32 x 24;
the threshold value Sthreshold640 x 480 x 64.
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Citations (5)

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Publication number Priority date Publication date Assignee Title
US5519790A (en) * 1992-12-15 1996-05-21 Viacom International Method for reducing noise in digital video information
CN105184791A (en) * 2015-09-02 2015-12-23 国网吉林省电力有限公司电力科学研究院 Power transmission line video image insulator positioning method
CN106559714A (en) * 2016-11-14 2017-04-05 上海工程技术大学 A kind of extraction method of key frame towards digital video copyright protection
CN107392864A (en) * 2017-07-01 2017-11-24 南京理工大学 A kind of mixed noise filtering method for removing Gaussian noise and impulsive noise
CN109714501A (en) * 2019-01-15 2019-05-03 武汉鸿瑞达信息技术有限公司 A kind of frame is averaged noise-reduction method and device

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JP6365253B2 (en) * 2014-11-12 2018-08-01 富士通株式会社 VIDEO DATA PROCESSING DEVICE, VIDEO DATA PROCESSING PROGRAM, AND VIDEO DATA PROCESSING METHOD

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5519790A (en) * 1992-12-15 1996-05-21 Viacom International Method for reducing noise in digital video information
CN105184791A (en) * 2015-09-02 2015-12-23 国网吉林省电力有限公司电力科学研究院 Power transmission line video image insulator positioning method
CN106559714A (en) * 2016-11-14 2017-04-05 上海工程技术大学 A kind of extraction method of key frame towards digital video copyright protection
CN107392864A (en) * 2017-07-01 2017-11-24 南京理工大学 A kind of mixed noise filtering method for removing Gaussian noise and impulsive noise
CN109714501A (en) * 2019-01-15 2019-05-03 武汉鸿瑞达信息技术有限公司 A kind of frame is averaged noise-reduction method and device

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Denomination of invention: A Local Correlation Filtering Method Suitable for Simultaneously Processing Infrared and Partial Discharge Data

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