阅读说明：本技术 一种新能源汽车充电桩智能管理系统 (New energy automobile fills electric pile intelligent management system ) 是由 李国安 于 2020-07-10 设计创作，主要内容包括：一种新能源汽车充电桩智能管理系统,包括参数检测模块、图像检测模块、信息传输模块和智能管理终端,所述参数检测模块用于采集新能源汽车充电桩表面的温度数据,所述图像检测模块用于采集新能源汽车充电桩的图像,所述信息传输模块用于将采集的温度数据和图像传输至智能管理终端,所述智能管理终端用于对接收到的温度数据进行处理和判断,并对接收到的图像进行处理和显示。采用本发明确定的最终局部邻域能够实现对待检测像素的有效检测的同时,保证图像的细节信息；根据确定的局部邻域计算待检测像素对应的像素检测阈值,根据待检测像素和像素检测阈值之间的情况判断所述待检测像素是否为噪声像素,从而能够有效的检测出图像中的噪声像素。(The utility model provides a new energy automobile fills electric pile intelligent management system, includes parameter detection module, image detection module, information transmission module and intelligent management terminal, parameter detection module is used for gathering the temperature data on new energy automobile fills electric pile surface, image detection module is used for gathering new energy automobile fills electric pile's image, information transmission module is used for the temperature data and the image transmission to the intelligent management terminal of gathering, the intelligent management terminal is used for handling and judging received temperature data to handle and show received image. The final local neighborhood determined by the method can realize effective detection of the pixel to be detected and ensure the detail information of the image; and calculating a pixel detection threshold corresponding to the pixel to be detected according to the determined local neighborhood, and judging whether the pixel to be detected is a noise pixel according to the condition between the pixel to be detected and the pixel detection threshold, so that the noise pixel in the image can be effectively detected.)

1. The intelligent management system for the new energy automobile charging pile is characterized by comprising a parameter detection module, an image acquisition module, an information transmission module and an intelligent management terminal, wherein the parameter detection module adopts a sensor node to acquire temperature data on the surface of the new energy automobile charging pile and transmits the acquired temperature data to the intelligent management terminal through the information transmission module, the image acquisition module comprises an acquisition control unit and an image acquisition unit, the acquisition control unit is used for controlling the image acquisition unit to acquire an image of the new energy automobile charging pile according to a received acquisition instruction, the image acquisition unit transmits the acquired image of the new energy automobile charging pile to the intelligent management terminal through the information transmission module, the intelligent management terminal comprises a danger analysis unit, an image optimization unit and an image display unit, and the danger analysis unit is used for processing the received temperature data, the processed temperature data is compared with a preset safety threshold value, when the temperature data exceeds the preset safety threshold value, early warning is carried out, an acquisition instruction is sent to an acquisition control unit through an information transmission module, and the image optimization unit is used for processing the received image and displaying the processed image on an image display unit;

the image optimization unit is used for denoising the acquired image, setting I to represent the image to be denoised, I (x, y) to represent the pixel at the coordinate (x, y) in the image I, determining the local neighborhood of the pixel I (x, y) in an iterative mode, and setting omega^r(x，y)Representing a local neighborhood of (2r +1) × (2r +1) centered on pixel I (x, y) at the r-th iteration, N^r(x, y) denotes the local neighborhood Ω^rSet of pixels in (x, y) for local neighborhood Ω^rAnd (5) screening pixels in (x, y), specifically:

is provided with

set N^rThe set of remaining pixels screened in (x, y) is denoted N^r′(x, y) defining a local neighborhood Ω^rThe iterative decision function corresponding to (x, y) is W^r(x, y), and W^rThe expression of (x, y) is:

W^r(x，y)＝ω^r(x，y)-ω^r-1(x，y)

in the formula, ω^r(x, y) denotes the local neighborhood Ω at the r-th iteration^rLocal detection coefficient, ω, corresponding to (x, y)^r-1(x, y) denotes the local neighborhood Ω at the (r-1) th iteration^r-1(x, y) corresponding local detection coefficient, I^r′(e, z) and I^r′(p, q) is the set N^r′(x, y) and (e, z) represents a pixel I^r′(e, z) in the local neighborhood Ω^rCoordinates in (x, y), (p, q) denotes a pixel I^r′(p, q) in the local neighborhood Ω^rCoordinates in (x, y), f^r′(e, z) represents a pixel I^r′Gray value of (e, z), f^r′(p, q) represents a pixel I^r′Gradation value of (p, q), M^r′(x, y) denotes the set N^r′Number of pixels in (x, y), N^(r-1)(x, y) denotes the local neighborhood Ω^r-1Set of pixels of (x, y), N^(r-1)′(x, y) denotes the set N^(r-1)(x, y) screening the remaining set of pixels, I^(r-1)′(v, g) and I^(r-1)′(s, u) is the set N^(r-1)′(x, y) and (v, g) represents a pixel I^(r-1)′(v，g)In the local neighborhood Ω^r-1Coordinates in (x, y), (s, u) denotes a pixel I^(r-1)′(s, u) in the local neighborhood Ω^r-1Coordinates in (x, y), f^(r-1)′(v, g) represents a pixel I^(r-1)′(v, g) gradation value, f^(r-1)′(s, u) represents a pixel I^(r-1)′Gradation value of (s, u), M^(r-1)′(x, y) denotes the set N^(r-1)′The number of pixels in (x, y);

when local neighborhood Ω^r(x, y) corresponding iterative decision function W^r(x, y) satisfies

let omega^σ(x, y) represents the final local neighborhood, Ω, of pixel I (x, y) determined using the method described above^σ(x, y) is the local neighborhood of (2 σ +1) × (2 σ +1) centered around pixel I (x, y) at the σ -th iteration, N^σ(x, y) denotes the local neighborhood Ω^σSet of pixels in (x, y), N^σ′(x, y) denotes the set N^σ(x, y) selecting a set of remaining pixels, and given that the pixel detection threshold corresponding to the pixel I (x, y) is H (x, y), the expression of the slice (x, y) is:

in the formula I^σ′(G, D) represents the set N^σ′A pixel in (x, y), and I^σ′(G, D) ≠ I (x, y), (G, D) denotes the pixel I^σ′(G, D) in the local neighborhood Ω^σCoordinates in (x, y), f^σ′(G, D) represents a pixel I^σ′Gradation of (G, D)Value, I^σ′(A, B) represents a set N^σ′A pixel in (x, y), and I^σ′(A, B) ≠ I (x, y), (A, B) denotes a pixel I^σ′(A, B) in a local neighborhood Ω^σCoordinates in (x, y), f^σ′(A, B) represents a pixel I^σ′Gradation values of (A, B), M^σ′(x, y) denotes the set N^σ′The number of pixels in (x, y);

when the gray value f (x, y) of the pixel I (x, y) satisfiesThen, the pixel I (x, y) is determined as a noise pixel, and the order is

2. The system for intelligently managing the charging piles of the new energy vehicles according to claim 1, wherein the information transmission module is used for transmitting information in a GPRS communication mode.

3. The system of claim 1, wherein the information transmission module is configured to transmit information in a 4G or 5G communication mode.

4. The system for intelligently managing the new energy vehicle charging piles according to any one of claims 1 to 3, wherein the image acquisition unit adopts a camera to acquire images of the new energy vehicle charging piles.

5. The new energy vehicle charging pile intelligent management system according to claim 4, wherein the image acquisition unit adopts a high-definition camera to acquire images of the new energy vehicle charging pile.

Technical Field

The invention relates to the field of safety monitoring, in particular to an intelligent management system for a new energy automobile charging pile.

Background

The new energy electric vehicle charging pile has the function similar to that of an oiling machine in a gas station, can be fixed on the ground or on the wall, is installed in public buildings (public buildings, markets, public parking lots and the like) and residential area parking lots or charging stations, and can charge electric vehicles of various models according to different voltage levels.

Guangdong product quality supervision and inspection research institute has announced electric automobile fills electric pile product risk monitoring result for the first time. The results show that more than 70% of the monitored samples present a safety hazard. The collection of this detection sample for the institute from market purchase 9 manufacturing enterprises' 10 batch electric automobile fill electric pile products, wherein 7 batches do not accord with national standard requirement, have serious potential safety hazard. Risk monitoring shows that four items of the sample do not meet the national standard requirements, and the sample is easy to catch fire and cause electric shock of a user. The development of new energy automobiles in China is a need for meeting important challenges of energy conservation and emission reduction, and is also a need for the cross-over development of the automobile industry and the improvement of international competitiveness. The popularization of electric vehicles must be based on the construction of charging piles, and the charging piles of new energy vehicles become an important way for electric vehicles to obtain electric energy with the advantages of environmental protection, convenience in use and easiness in installation. In order to improve the convenience of charging electric vehicles, the new energy vehicle charging pile generally adopts the installation mode of dispersed layout. In order to solve the problems that the safety of a charging pile of a new energy automobile installed dispersedly is poor, the maintenance difficulty and the management inefficiency are solved, the application provides an intelligent management system of the charging pile of the new energy automobile, and the effective management of the safe operation of the charging pile of the new energy automobile can be realized.

Disclosure of Invention

Aiming at the problems, the invention aims to provide an intelligent management system for a new energy automobile charging pile.

The purpose of the invention is realized by the following technical scheme:

an intelligent management system for new energy automobile charging piles comprises a parameter detection module, an image acquisition module, an information transmission module and an intelligent management terminal, wherein the parameter detection module adopts a sensor node to acquire temperature data of the surfaces of the new energy automobile charging piles and transmits the acquired temperature data to the intelligent management terminal through the information transmission module, the image acquisition module comprises an acquisition control unit and an image acquisition unit, the acquisition control unit is used for controlling the image acquisition unit to acquire images of the new energy automobile charging piles according to received acquisition instructions, the image acquisition unit transmits the acquired images of the new energy automobile charging piles to the intelligent management terminal through the information transmission module, the intelligent management terminal comprises a danger analysis unit, an image optimization unit and an image display unit, and the danger analysis unit is used for processing the received temperature data, and comparing the processed temperature data with a preset safety threshold, giving an early warning when the temperature data exceeds the preset safety threshold, sending an acquisition instruction to an acquisition control unit through an information transmission module, and processing the received image by the image optimization unit and displaying the processed image on an image display unit.

Preferably, the image optimization unit is configured to denoise the acquired image, set I to represent the image to be denoised, I (x, y) to represent a pixel at a coordinate (x, y) in the image I, determine a local neighborhood of the pixel I (x, y) in an iterative manner, and set Ω^r(x, y) represents the local neighborhood of (2r +1) × (2r +1) centered around pixel I (x, y) at the r-th iteration, N^r(x, y) denotes the local neighborhood Ω^rSet of pixels in (x, y) for local neighborhood Ω^rAnd (5) screening pixels in (x, y), specifically:

is provided withRepresenting a local neighborhood Ω^rThe maximum value of the gray value of the pixel in (x, y),representing a local neighborhood Ω^rMinimum value of pixel gray values in (x, y), given a first screening thresholdAnd a second screening thresholdAnd is

Wherein M is^r(x, y) denotes the local neighborhood Ω^rNumber of pixels in (x, y), Ω^r-1(x, y) denotes the local neighborhood of (2(r-1) +1) × (2(r-1) +1) centered on pixel I (x, y) at the (r-1) th iteration, Mr-1x, y denotes the number of pixels in the local neighborhood Ω r-1(x, y), MI denotes the number of pixels in image I, C (I) denotes the length of image I, K (I) denotes the width of image I, let I (x, y) denote the width of image I^r(m, n) denotes the local neighborhood Ω^rA pixel at coordinate (m, n) in (x, y), and I^r(m，n)≠I(x，y)，f^r(m, n) represents a pixel I^rGray scale value of (m, n)

OrThen the pixel I is^r(m, N) in the set N^r(x, y) deleted;

set N^rThe set of remaining pixels screened in (x, y) is denoted N^r' (x, y) defining a local neighborhood Ω^rThe iterative decision function corresponding to (x, y) is W^r(x, y), and W^rThe expression of (x, y) is:

W^r(x，y)＝ω^r(x，y)-ω^r-1(x，y)

in the formula, ω^r(x, y) denotes the local neighborhood Ω at the r-th iteration^rLocal detection coefficient, ω, corresponding to (x, y)^r-1(x, y) denotes the local neighborhood Ω at the (r-1) th iteration^r-1(x, y) corresponding local detection coefficient, I^r′(e，z) and I^r' (p, q) is the set N^r' (x, y) and (e, z) denote a pixel I^r' (e, z) in the local neighborhood Ω^rCoordinates in (x, y), (p, q) denotes a pixel I^r' (p, q) in the local neighborhood Ω^rCoordinates in (x, y), f^r' (e, z) denotes a pixel I^r' (e, z) grayscale value, f^r' (p, q) denotes a pixel I^r' (p, q) grayscale value, M^r' (x, y) denotes the set N^r' (x, y) number of pixels, N^(r-1)(x, y) denotes the local neighborhood Ω^r-1Set of pixels of (x, y), N^(r-1)' (x, y) denotes the set N^(r-1)(x, y) screening the remaining set of pixels, I^(r-1)' (v, g) and I^(r-1)' (s, u) is the set N^(r-1)' (x, y) and (v, g) denotes a pixel I^(r-1)' (v, g) in the local neighborhood Ω^r-1Coordinates in (x, y), (s, u) denotes a pixel I^(r-1)' (s, u) in the local neighborhood Ω^r-1Coordinates in (x, y), f^(r-1)' (v, g) denotes a pixel I^(r-1)' (v, g) grayscale value, f^(r-1)' (s, u) denotes a pixel I^(r-1)' (s, u) grayscale value, M^(r-1)' (x, y) denotes the set N^(r-1)' (x, y) number of pixels;

when local neighborhood Ω^r(x, y) corresponding iterative decision function W^r(x, y) satisfiesThen, the local neighborhood of the pixel I (x, y) is determined continuously in an iterative manner until W^r(x, y) satisfiesStopping continuous iteration, and obtaining the final local neighborhood of the pixel I (x, y) as the local neighborhood of the current iteration in the last iteration;

let omega^σ(x, y) represents the final local neighborhood, Ω, of pixel I (x, y) determined using the method described above^σ(x, y) is centered around pixel I (x, y) at the σ -th iteration(2 σ +1) × (2 σ +1), N^σ(x, y) denotes the local neighborhood Ω^σSet of pixels in (x, y), N^σ' (x, y) denotes the set N^σAnd (x, y) screening the remaining pixels to form a set, and if the pixel detection threshold corresponding to the given pixel I (x, y) is H (x, y), the expression of H (x, y) is as follows:

in the formula I^σ' (G, D) denotes the set N^σ' (x, y), and I^σ' (G, D) ≠ I (x, y) and (G, D) denotes a pixel I^σ' (G, D) in the local neighborhood Ω^σCoordinates in (x, y), f^σ' (G, D) denotes a pixel I^σ' (G, D) grayscale value, I^σ' (A, B) denotes the set N^σ' (x, y), and I^σ' (A, B) ≠ I (x, y), and (A, B) denotes a pixel I^σ' (A, B) in the local neighborhood Ω^σCoordinates in (x, y), f^σ' (A, B) denotes a pixel I^σ' (A, B) grayscale value, M^σ' (x, y) denotes the set N^σ' (x, y) number of pixels;

when the gray value f (x, y) of the pixel I (x, y) satisfiesIf so, the pixel I (x, y) is determined to be a noise pixel, and the order isWhen the gray value f (x, y) of the pixel I (x, y) satisfiesThen, the pixel I (x, y) is determined to be a normal pixel.

The beneficial effects created by the invention are as follows:

(1) the utility model provides a new energy automobile fills electric pile intelligent management system, through the temperature data judgement on collection new energy automobile charging pile surface whether the new energy automobile charging pile safe operation, when judging that the new energy automobile charging pile appears danger in the operation process, gather the image that the new energy automobile charging pile and show, can more audio-visual know the operation condition that the electric pile was filled to current new energy automobile.

(2) When pixels in an image are processed, a local neighborhood of the pixels to be detected is determined in an iteration mode, a first screening threshold and a second screening threshold are given, the pixels in the corresponding local neighborhood during each iteration are screened according to the given first screening threshold and the given second screening threshold, so that noise pixels existing in the local neighborhood are removed, the first screening threshold and the second screening threshold are self-adaptively adjusted along with the variation of the number of pixels in the local neighborhood during current iteration and the current iteration frequency, more noise data are introduced at a higher probability when the number of pixels in the local neighborhood during current iteration is increased more or the range of the local neighborhood during current iteration is larger, so that the first screening threshold is increased and the second screening threshold is reduced, so that more noise pixels in the local neighborhood can be screened, the determination result of the local neighborhood is prevented from being influenced by the noise pixel; when the pixel to be detected is a noise pixel and the pixel to be detected is in an image region with high noise density, and when the local neighborhood of the pixel to be detected is determined by adopting a traditional method, most of the pixels in the determined local neighborhood are noise pixels, so that the noise pixel cannot be effectively detected when the pixel to be detected is detected according to the pixel in the local neighborhood, aiming at the above situation, the preferred embodiment defines an iteration judgment function, the local detection coefficient in the iteration judgment function measures the uniformity of the pixels in the local neighborhood according to the difference condition between pixel gray values, the iteration judgment function judges the uniformity of the pixels in the local neighborhood in the current iteration to be compared with the variation condition of the uniformity of the pixels in the local neighborhood in the last iteration according to the variation condition of the local detection coefficient of the local neighborhood in the two iterations, when the value of the iteration judgment function is in a certain range, iteration is continued, so that the diversity of pixels in the local neighborhood of the pixel to be detected is increased, the phenomenon that the pixels in the local neighborhood of the pixel to be detected are all noise pixels is avoided, and when the value of the iteration judgment function exceeds the certain range, the iteration is stopped, so that the uniformity of the pixels in the local neighborhood of the pixel to be detected is ensured while the diversity of the pixels in the local neighborhood of the pixel to be detected is increased, namely, the detail information of the image is ensured while the final local neighborhood determined by adopting the preferred embodiment can realize effective detection of the pixel to be detected; and calculating a pixel detection threshold corresponding to the pixel to be detected according to the determined local neighborhood, and judging whether the pixel to be detected is a noise pixel according to the condition between the pixel to be detected and the pixel detection threshold, so that the noise pixel in the image can be effectively detected.

Drawings

The invention is further described with the aid of the accompanying drawings, in which, however, the embodiments do not constitute any limitation to the invention, and for a person skilled in the art, without inventive effort, further drawings may be derived from the following figures.

FIG. 1 is a schematic diagram of the present invention.

Detailed Description

The invention is further described with reference to the following examples.

Referring to fig. 1, the new energy vehicle charging pile intelligent management system of the embodiment includes a parameter detection module, an image acquisition module, an information transmission module and an intelligent management terminal, wherein the parameter detection module acquires temperature data of the surface of a new energy vehicle charging pile by using a sensor node and transmits the acquired temperature data to the intelligent management terminal through the information transmission module, the image acquisition module includes an acquisition control unit and an image acquisition unit, the acquisition control unit is used for controlling the image acquisition unit to acquire an image of the new energy vehicle charging pile according to a received acquisition instruction, the image acquisition unit transmits the acquired image of the new energy vehicle charging pile to the intelligent management terminal through the information transmission module, the intelligent management terminal includes a risk analysis unit, an image optimization unit and an image display unit, the danger analysis unit is used for denoising the received temperature data, comparing the denoised temperature data with a preset safety threshold, giving an early warning when the temperature data exceeds the preset safety threshold, sending an acquisition instruction to the acquisition control unit through the information transmission module, and the image optimization unit is used for denoising the received image and displaying the denoised image on the image display unit.

The information transmission module adopts a GPRS communication mode or a 4G and 5G communication mode to carry out information transmission.

The image acquisition unit adopts the camera to acquire the image of new energy automobile charging pile.

The image optimization unit is used for denoising the acquired image, setting I to represent the image to be denoised, I (x, y) to represent the pixel at the coordinate (x, y) in the image I, determining the local neighborhood of the pixel I (x, y) in an iterative mode, and setting omega^r(x, y) represents the local neighborhood of (2r +1) × (2r +1) centered around pixel I (x, y) at the r-th iteration, N^r(x, y) denotes the local neighborhood Ω^rSet of pixels in (x, y) for local neighborhood Ω^rAnd (5) screening pixels in (x, y), specifically:

is provided withRepresenting a local neighborhood Ω^rThe maximum value of the gray value of the pixel in (x, y),representing a local neighborhood Ω^rMinimum value of pixel gray values in (x, y), given a first screening thresholdAnd a second screening threshold

And is

Wherein M is^r(x, y) denotes the local neighborhood Ω^rNumber of pixels in (x, y), Ω^r-1(x, y) denotes the local neighborhood of (2(r-1) +1) × (2(r-1) +1) centered on pixel I (x, y) at the (r-1) th iteration, M^r-1(x, y) denotes the local neighborhood Ω^r-1Number of pixels in (x, y), m (I) number of pixels in image I, c (I) length of image I, k (I) width of image I; let I^r(m, n) denotes the local neighborhood Ω^rA pixel at coordinate (m, n) in (x, y), and I^r(m，n)≠I(x，y)，f^r(m, n) represents a pixel I^rGray scale value of (m, n)OrThen, the pixels Ir (m, N) are put into the set N^r(x, y) deleted;

set N^rThe set of remaining pixels screened in (x, y) is denoted N^r' (x, y) defining a local neighborhood Ω^rThe iterative decision function corresponding to (x, y) is W^r(x, y), and W^rThe expression of (x, y) is:

W^r(x，y)＝ω^r(x，y)-ω^r-1(x，y)

in the formula, ω^r(x, y) denotes the local neighborhood Ω at the r-th iteration^rLocal detection coefficient, ω, corresponding to (x, y)^r-1(x, y) denotes the local neighborhood Ω at the (r-1) th iteration^r-1(x, y) corresponding local detection coefficient, I^r' (e, z) and I^r' (p, q) isSet N^r' (x, y) and (e, z) denote a pixel I^r' (e, z) in the local neighborhood Ω^rCoordinates in (x, y), (p, q) denotes a pixel I^r' (p, q) in the local neighborhood Ω^rCoordinates in (x, y), f^r' (e, z) denotes a pixel I^r' (e, z) grayscale value, f^r' (p, q) denotes a pixel I^r' (p, q) grayscale value, M^r' (x, y) denotes the set N^r' (x, y) number of pixels, N^(r-1)(x, y) denotes the local neighborhood Ω^r-1Set of pixels of (x, y), N^(r-1)' (x, y) denotes the set N^(r-1)(x, y) screening the remaining set of pixels, I^(r-1)' (v, g) and I^(r-1)' (s, u) is the set N^(r-1)' (x, y) and (v, g) denotes a pixel I^(r-1)' (v, g) in the local neighborhood Ω^r-1Coordinates in (x, y), (s, u) denotes a pixel I^(r-1)' (s, u) in the local neighborhood Ω^r-1Coordinates in (x, y), f^(r-1)' (v, g) denotes a pixel I^(r-1)' (v, g) grayscale value, f^(r-1)' (s, u) denotes a pixel I^(r-1)' (s, u) grayscale value, M^(r-1)' (x, y) denotes the set N^(r-1)' (x, y) number of pixels;

when local neighborhood Ω^r(x, y) corresponding iterative decision function W^r(x, y) satisfies

Then, the local neighborhood of the pixel I (x, y) is determined continuously in an iterative manner until W^r(x, y) satisfies

Stopping continuous iteration, and obtaining the final local neighborhood of the pixel I (x, y) as the local neighborhood of the current iteration in the last iteration;

let omega^σ(x, y) represents the final local neighborhood, Ω, of pixel I (x, y) determined using the method described above^σ(x, y) is the local neighborhood of (2 σ +1) × (2 σ +1) centered around pixel I (x, y) at the σ -th iterationDomain, N^σ(x, y) denotes the local neighborhood Ω^σSet of pixels in (x, y), N^σ' (x, y) denotes the set N^σAnd (x, y) screening the remaining pixels to form a set, and if the pixel detection threshold corresponding to the given pixel I (x, y) is H (x, y), the expression of H (x, y) is as follows:

in the formula I^σ' (G, D) denotes the set N^σ' (x, y), and I^σ' (G, D) ≠ I (x, y) and (G, D) denotes a pixel I^σ' (G, D) in the local neighborhood Ω^σCoordinates in (x, y), f^σ' (G, D) denotes a pixel I^σ' (G, D) grayscale value, I^σ' (A, B) denotes the set N^σ' (x, y), and I^σ' (A, B) ≠ I (x, y), and (A, B) denotes a pixel I^σ' (A, B) in the local neighborhood Ω^σCoordinates in (x, y), f^σ' (A, B) denotes a pixel I^σ' (A, B) grayscale value, M^σ' (x, y) denotes the set N^σ' (x, y) number of pixels;

when the gray value f (x, y) of the pixel I (x, y) satisfies

Then, the pixel I (x, y) is determined as a noise pixel, and the order is

When the gray value f (x, y) of the pixel I (x, y) satisfies

Then, the pixel I (x, y) is determined to be a normal pixel.

In the preferred embodiment, when pixels in an image are processed, a local neighborhood of the pixel to be detected is determined in an iteration mode, a first screening threshold and a second screening threshold are given, the pixels in the corresponding local neighborhood during each iteration are screened according to the given first screening threshold and the given second screening threshold, so that noise pixels existing in the local neighborhood are removed, the first screening threshold and the second screening threshold are self-adaptively adjusted along with the variation of the number of pixels in the local neighborhood during current iteration and the current iteration frequency, more noise data are introduced at a higher probability when the number of pixels in the local neighborhood during current iteration is increased more or the range of the local neighborhood during current iteration is larger, therefore, the first screening threshold is increased and the second screening threshold is reduced, so that more noise pixels in the local neighborhood can be screened, the determination result of the local neighborhood is prevented from being influenced by the noise pixel; when the pixel to be detected is a noise pixel and the pixel to be detected is in an image region with high noise density, and when the local neighborhood of the pixel to be detected is determined by adopting a traditional method, most of the pixels in the determined local neighborhood are noise pixels, so that the noise pixel cannot be effectively detected when the pixel to be detected is detected according to the pixel in the local neighborhood, aiming at the above situation, the preferred embodiment defines an iteration judgment function, the local detection coefficient in the iteration judgment function measures the uniformity of the pixels in the local neighborhood according to the difference condition between pixel gray values, the iteration judgment function judges the uniformity of the pixels in the local neighborhood in the current iteration to be compared with the variation condition of the uniformity of the pixels in the local neighborhood in the last iteration according to the variation condition of the local detection coefficient of the local neighborhood in the two iterations, when the value of the iteration judgment function is in a certain range, iteration is continued, so that the diversity of pixels in the local neighborhood of the pixel to be detected is increased, the phenomenon that the pixels in the local neighborhood of the pixel to be detected are all noise pixels is avoided, and when the value of the iteration judgment function exceeds the certain range, the iteration is stopped, so that the uniformity of the pixels in the local neighborhood of the pixel to be detected is ensured while the diversity of the pixels in the local neighborhood of the pixel to be detected is increased, namely, the detail information of the image is ensured while the final local neighborhood determined by adopting the preferred embodiment can realize effective detection of the pixel to be detected; and calculating a pixel detection threshold corresponding to the pixel to be detected according to the determined local neighborhood, and judging whether the pixel to be detected is a noise pixel according to the condition between the pixel to be detected and the pixel detection threshold, so that the noise pixel in the image can be effectively detected.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.