CN112613055A - Image processing system and method based on distributed cloud server and digital-image conversion - Google Patents
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Abstract
The invention belongs to the technical field of cloud computing, and particularly relates to an image processing system and method based on a distributed cloud server and digital-to-image conversion. The system comprises: the image preprocessing unit is configured for carrying out image binarization on the image to be processed to obtain a binarized image; and the digital conversion module is configured to convert the image to be binarized into a corresponding numerical matrix and then convert the numerical matrix into a continuous numerical string. The image encryption method has the advantages that the image data are converted into numerical data to complete the encryption of the image, and the image encryption method is completely different from the image encryption technology in the prior art, and has the advantages of difficult cracking and high safety; meanwhile, in the encryption process, the segmented numerical data is encrypted by using a distributed cloud server group consisting of a plurality of cloud servers, so that the encryption efficiency is higher.
Description
Technical Field
The invention belongs to the technical field of cloud computing, and particularly relates to an image processing system and method based on a distributed cloud server and digital-to-image conversion.
Background
In computer science, Distributed computing (english) is translated into Distributed computing. This field of research is mainly concerned with how Distributed systems (Distributed systems) perform calculations. A distributed system is a system of computers interconnected via a network to communicate messages and communications and coordinate their activities. The components interact with each other to achieve a common goal. The engineering data which needs to be calculated in large quantity is divided into small blocks, a plurality of computers calculate the small blocks respectively, and after the calculation results are uploaded, the results are unified and combined to obtain the science of data conclusion. Examples of distributed systems come from different service-oriented architectures, massively multiplayer online games, peer-to-peer network applications.
Digital images are one of the most popular forms of multimedia and have wide application in politics, economy, national defense, education, and the like. Digital images also have high security requirements for certain specialized fields, such as military, commercial, and medical.
In order to realize the security of digital images, in actual operation, a two-dimensional image is generally converted into one-dimensional data, and then encrypted by adopting a traditional encryption algorithm. Unlike ordinary text information, images and videos have temporal, spatial, visual perceptibility and lossy compression properties, and these properties make it possible to design more efficient and secure encryption algorithms for images. Since the last 90 s, researchers have proposed various image encryption algorithms using these characteristics. To summarize, the concept of image encryption technology is: a technique for improving the security and the operation efficiency of encryption by using the characteristics of digital images to design an encryption algorithm.
Patent No. CN2007800531085A discloses an image encryption apparatus, an image decryption apparatus, and a method; input data is converted into an image, and an encrypted area, which is an area where the image is encrypted, is specified for the input image. Then, encryption key related information, which is information related to the encryption key, is generated from the encryption key, and the encryption key related information is embedded in the image of the encryption area, thereby generating the 1 st intermediate image. Then, the 1 st intermediate image is transformed according to the encryption key to generate a 2 nd intermediate image. Then, the pixel values of the 2 nd intermediate image are transformed in such a manner that the position of the encryption area can be determined. As a result of the conversion, an encrypted image in which a part of the encrypted area within the input image is encrypted is generated.
Although the encryption of the image is realized, the encryption process and the encryption means still follow the traditional mode of encrypting the original image, and the encryption security and the decryption rate are still high.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide an image processing system and method based on a distributed cloud server and digital-to-image conversion, which utilize conversion of image data into numerical data to complete encryption of an image, and the image processing system and method are completely different from the image encryption technology in the prior art, and have the advantages of being difficult to crack and high in security; meanwhile, in the encryption process, the segmented numerical data is encrypted by using a distributed cloud server group consisting of a plurality of cloud servers, so that the encryption efficiency is higher.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
an image processing system based on distributed cloud servers and map conversion, the system comprising: the image preprocessing unit is configured for carrying out image binarization on the image to be processed to obtain a binarized image; the digital conversion module is configured to convert the image to be binarized into a corresponding numerical matrix and then convert the numerical matrix into a continuous numerical string; the numerical value segmentation module is configured to perform characteristic analysis on the numerical value string, determine segmentation nodes in numerical value penetration, and segment the numerical value string according to the determined segmentation nodes to obtain a plurality of segmentation substrings; the distributed cloud server group comprises a plurality of cloud servers, wherein each cloud server is configured to randomly obtain a sub-character string from a plurality of segmented sub-character strings obtained after segmentation, and digitally encrypt the obtained sub-character string to obtain a sub-encryption result; and the image restoration unit is configured to acquire the sub-encryption results from the cloud servers, decrypt the sub-encryption results to obtain sub-character strings, splice the sub-character strings to obtain numerical value strings, perform an inverse process of converting the numerical value strings into the numerical value strings by using the numerical value strings to obtain the numerical value matrixes, and finally perform an inverse process of converting the binary images into the numerical value matrixes to obtain the binary images.
Further, the image preprocessing unit performing image binarization includes: calculating the image dimensionality of the image to be processed aiming at the gray level image data of the image to be processed, and dividing the image to be processed into low dimensionality or high dimensionality according to the calculated image dimensionality; binarizing the grayscale image data by an integral segmentation method when the image to be processed is of low dimensionality, and binarizing the grayscale image data by a composite segmentation method when the image to be processed is of high dimensionality, thereby generating binarized image data, wherein binarizing the grayscale image data by the composite segmentation method comprises the steps of: by the integral segmentation method, each pixel in the grayscale image data is classified into 3 types: black, white and undetermined; an optimal segmentation threshold is calculated for each pixel belonging to an undetermined class by a local adaptive segmentation method, thereby binarizing the pixel.
Further, the step of converting the image to be binarized into the corresponding numerical matrix by the digital conversion module includes: taking the binarized image as a parameter A, randomly generating a matrix B, and then constructing a conversion matrix based on the parameter A and the matrix B:
and recording a known conversion factor matrix:
converting matrix and conversion factor matrix
Multiplying to obtain a converted numerical matrix X:
and connecting P and Q in the numerical matrix, and converting into a continuous numerical string.
Further, each of the distributed cloud servers includes: an acquisition unit configured to randomly acquire one sub-character string from a plurality of divided sub-character strings obtained after division; and the encryption unit is configured to digitally encrypt the obtained sub-character string to obtain a sub-encryption result.
Further, the digitally encrypting the obtained sub-character string by the encryption unit includes: randomly generating a value m as a first key, and performing Fourier transform of m degrees on the sub-character string by using the following formula:
wherein: x (t) denotes a substring, Xm(u) denotes the converted substring, Km(t, u) is a transformation kernel; said Km(t, u) is expressed using the following formula:
wherein t and u are transformation parameters, delta is the Fourier transform of the Dirac function, delta (u-t) represents the Fourier transform of the Dirac function which performs u-t, and delta (u + t) represents the Fourier transform of the Dirac function which performs u + t; fourier transformed Xm(u) as an encrypted substring.
Further, if the image to be processed is a color image, the color image is converted into a gray scale image to obtain the gray scale image data.
An image processing method based on a distributed cloud server and a number map conversion, the method comprising the steps of:
step 1: carrying out image binarization on an image to be processed to obtain a binarized image;
step 2: converting an image to be binarized into a corresponding numerical matrix, and converting the numerical matrix into a continuous numerical string;
and step 3: performing characteristic analysis on the numerical string, determining segmentation nodes in numerical penetration, and segmenting the numerical string according to the determined segmentation nodes to obtain a plurality of segmentation substrings;
and 4, step 4: the cloud servers respectively randomly obtain a sub-character string from the plurality of segmented sub-character strings obtained after segmentation, and digitally encrypt the obtained sub-character string to obtain a sub-encryption result;
and 5: and acquiring a sub-encryption result from each cloud server, decrypting the sub-encryption result to obtain a sub-character string, splicing the sub-character strings to obtain a numerical value string, performing an inverse process of converting the numerical value string into the numerical value string by using the numerical value string to obtain a numerical value matrix, and finally performing an inverse process of converting the binary image into the numerical value matrix to obtain the binary image.
Further, the image binarization in the step 1 includes: calculating the image dimensionality of the image to be processed aiming at the gray level image data of the image to be processed, and dividing the image to be processed into low dimensionality or high dimensionality according to the calculated image dimensionality; binarizing the grayscale image data by an integral segmentation method when the image to be processed is of low dimensionality, and binarizing the grayscale image data by a composite segmentation method when the image to be processed is of high dimensionality, thereby generating binarized image data, wherein binarizing the grayscale image data by the composite segmentation method comprises the steps of: by the integral segmentation method, each pixel in the grayscale image data is classified into 3 types: black, white and undetermined; an optimal segmentation threshold is calculated for each pixel belonging to an undetermined class by a local adaptive segmentation method, thereby binarizing the pixel.
Further, the step 2 of converting the image to be binarized into a corresponding numerical matrix, and then converting the numerical matrix into a continuous numerical string includes: taking the binarized image as a parameter A, randomly generating a matrix B, and then constructing a conversion matrix based on the parameter A and the matrix B:
and recording a known conversion factor matrix:
converting matrix and conversion factor matrix
Multiplying to obtain a converted numerical matrix X:
and connecting P and Q in the numerical matrix, and converting into a continuous numerical string.
Further, if the image to be processed is a color image, the color image is converted into a gray scale image to obtain the gray scale image data.
The image processing system and method based on the distributed cloud server and the digital-image conversion have the following beneficial effects: the image encryption method has the advantages that the image data are converted into numerical data to complete the encryption of the image, and the image encryption method is completely different from the image encryption technology in the prior art, and has the advantages of difficult cracking and high safety; meanwhile, in the encryption process, a distributed cloud server group consisting of a plurality of cloud servers is used for encrypting the segmented numerical data, so that the encryption efficiency is higher; the method is mainly realized by the following steps: 1. conversion of numbers and images: according to the method, the image to be processed is firstly binarized, then the binarized image is converted into the corresponding numerical value matrix, then the numerical value matrix is converted into the continuous numerical value string, and then the image encryption is carried out on the converted numerical value string, so that compared with the traditional encryption mode, the encryption mode is not easy to crack, and the safety is higher; 2. dividing the numerical string: the method and the system have the advantages that the numerical string is segmented to obtain the sub-character strings, and then the sub-character strings are encrypted by the aid of the cloud servers, so that the encryption efficiency is higher; 3. constructing a numerical matrix: according to the invention, the image data is converted into the numerical data in a manner of constructing the numerical matrix, and compared with a conversion manner in the prior art, the conversion process is simpler and the conversion efficiency is higher; 4. and (3) encryption algorithm: the encryption algorithm is different from the traditional numerical encryption, the numerical value is encrypted in a transformation mode based on Fourier transformation, and the encrypted result is lower in decryption rate due to the fact that the encryption algorithm is different from the prior art.
Drawings
Fig. 1 is a schematic system structure diagram of an image processing system based on a distributed cloud server and digital-to-image conversion according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method of an image processing method based on a distributed cloud server and a digital-to-image conversion according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of an experimental curve of which the cracking rate changes with the cracking times according to the image processing method based on the distributed cloud server and the digital-to-graphic conversion provided by the embodiment of the present invention, and a schematic diagram of a comparative experimental effect in the prior art.
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
Example 1
As shown in fig. 1, an image processing system based on distributed cloud servers and map conversion, the system comprising: the image preprocessing unit is configured for carrying out image binarization on the image to be processed to obtain a binarized image; the digital conversion module is configured to convert the image to be binarized into a corresponding numerical matrix and then convert the numerical matrix into a continuous numerical string; the numerical value segmentation module is configured to perform characteristic analysis on the numerical value string, determine segmentation nodes in numerical value penetration, and segment the numerical value string according to the determined segmentation nodes to obtain a plurality of segmentation substrings; the distributed cloud server group comprises a plurality of cloud servers, wherein each cloud server is configured to randomly obtain a sub-character string from a plurality of segmented sub-character strings obtained after segmentation, and digitally encrypt the obtained sub-character string to obtain a sub-encryption result; and the image restoration unit is configured to acquire the sub-encryption results from the cloud servers, decrypt the sub-encryption results to obtain sub-character strings, splice the sub-character strings to obtain numerical value strings, perform an inverse process of converting the numerical value strings into the numerical value strings by using the numerical value strings to obtain the numerical value matrixes, and finally perform an inverse process of converting the binary images into the numerical value matrixes to obtain the binary images.
By adopting the technical scheme, the image encryption is completed by converting the image data into numerical data, and the image encryption method is completely different from the image encryption technology in the prior art, and has the advantages of difficult cracking and high safety; meanwhile, in the encryption process, a distributed cloud server group consisting of a plurality of cloud servers is used for encrypting the segmented numerical data, so that the encryption efficiency is higher; the method is mainly realized by the following steps: 1. conversion of numbers and images: according to the method, the image to be processed is firstly binarized, then the binarized image is converted into the corresponding numerical value matrix, then the numerical value matrix is converted into the continuous numerical value string, and then the image encryption is carried out on the converted numerical value string, so that compared with the traditional encryption mode, the encryption mode is not easy to crack, and the safety is higher; 2. dividing the numerical string: the method and the system have the advantages that the numerical string is segmented to obtain the sub-character strings, and then the sub-character strings are encrypted by the aid of the cloud servers, so that the encryption efficiency is higher; 3. constructing a numerical matrix: according to the invention, the image data is converted into the numerical data in a manner of constructing the numerical matrix, and compared with a conversion manner in the prior art, the conversion process is simpler and the conversion efficiency is higher; 4. and (3) encryption algorithm: the encryption algorithm is different from the traditional numerical encryption, the numerical value is encrypted in a transformation mode based on Fourier transformation, and the encrypted result is lower in decryption rate due to the fact that the encryption algorithm is different from the prior art.
Example 2
On the basis of the above embodiment, the image preprocessing unit performing image binarization includes: calculating the image dimensionality of the image to be processed aiming at the gray level image data of the image to be processed, and dividing the image to be processed into low dimensionality or high dimensionality according to the calculated image dimensionality; binarizing the grayscale image data by an integral segmentation method when the image to be processed is of low dimensionality, and binarizing the grayscale image data by a composite segmentation method when the image to be processed is of high dimensionality, thereby generating binarized image data, wherein binarizing the grayscale image data by the composite segmentation method comprises the steps of: by the integral segmentation method, each pixel in the grayscale image data is classified into 3 types: black, white and undetermined; an optimal segmentation threshold is calculated for each pixel belonging to an undetermined class by a local adaptive segmentation method, thereby binarizing the pixel.
Specifically, the binarization processing of the image is to set the gray value of a point on the image to be 0 or 255, that is, to make the whole image exhibit a distinct black-and-white effect. That is, a gray scale image with 256 brightness levels is selected by a proper threshold value to obtain a binary image which can still reflect the whole and local features of the image. In digital image processing, binary images are very important, and particularly in practical image processing, many systems are configured by binary image processing, and in order to perform processing and analysis of binary images, a grayscale image is first binarized to obtain a binarized image, which is advantageous in that when an image is further processed, the collective property of the image is only related to the positions of points with pixel values of 0 or 255, and the multi-level values of the pixels are not related, so that the processing is simplified, and the processing and compression amount of data is small. In order to obtain an ideal binary image, a non-overlapping region is generally defined by closed and connected boundaries. All pixels with the gray levels larger than or equal to the threshold are judged to belong to the specific object, the gray level of the pixels is 255 for representation, otherwise the pixels are excluded from the object area, the gray level is 0, and the pixels represent the background or the exceptional object area.
If a particular object has a uniform gray level inside it and is in a uniform background with gray levels of other levels, a comparable segmentation effect can be obtained using thresholding. If the difference between the object and the background is not represented in gray scale values (e.g., different textures), the difference feature can be converted into a gray scale difference, and then the image can be segmented using a threshold selection technique. The threshold value is dynamically adjusted to realize the binarization of the image, and the specific result of the image segmentation can be dynamically observed.
Example 3
On the basis of the above embodiment, the converting the image to be binarized into the corresponding numerical matrix by the digital conversion module includes: taking the binarized image as a parameter A, randomly generating a matrix B, and then constructing a conversion matrix based on the parameter A and the matrix B:
and recording a known conversion factor matrix:
converting matrix and conversion factor matrix
Multiplying to obtain a converted numerical matrix X:
and connecting P and Q in the numerical matrix, and converting into a continuous numerical string.
Specifically, the image data is converted into the numerical data in a numerical matrix constructing mode, and compared with the conversion mode in the prior art, the conversion process is simpler and the conversion efficiency is higher; 4. and (3) encryption algorithm: the encryption algorithm is different from the traditional numerical encryption, the numerical value is encrypted in a transformation mode based on Fourier transformation, and the encrypted result is lower in decryption rate due to the fact that the encryption algorithm is different from the prior art.
Example 4
On the basis of the above embodiment, each of the distributed cloud servers includes: an acquisition unit configured to randomly acquire one sub-character string from a plurality of divided sub-character strings obtained after division; and the encryption unit is configured to digitally encrypt the obtained sub-character string to obtain a sub-encryption result.
Specifically, the industry name of a cloud server is actually a computing unit. The computing unit means that the server can only count the brains of one person, and is equivalent to the CPU of a common computer, and the resources in the server are limited. And if you need to obtain better performance, the solution is to upgrade the cloud server and to deploy other software which consumes the resources of the computing unit on the corresponding cloud service. For example, databases have special cloud database services, static web pages and pictures have special file storage services.
Moreover, cloud servers are not so cheap, but rather are more expensive than a typical VPS. Why? Because it is relatively easy to expand. The cloud server is a good choice when the website is large and has high income.
The cloud server is an important component of cloud computing service and is a service platform for providing comprehensive business capability for various internet users. The platform integrates three core elements of internet application in the traditional sense: computing, storage, network, and providing a user with a public internet infrastructure service.
Example 5
On the basis of the above embodiment, the digitally encrypting the acquired sub-string by the encryption unit includes: randomly generating a value m as a first key, and performing Fourier transform of m degrees on the sub-character string by using the following formula:
wherein: x (t) denotes a substring, Xm(u) denotes the converted substring, Km(t, u) is a transformation kernel; said Km(t, u is expressed using the following formula:
Example 6
On the basis of the above embodiment, if the image to be processed is a color image, it is converted into a grayscale image to obtain the grayscale image data.
Example 7
As shown in fig. 2, the image processing method based on distributed cloud server and map conversion performs the following steps:
step 1: carrying out image binarization on an image to be processed to obtain a binarized image;
step 2: converting an image to be binarized into a corresponding numerical matrix, and converting the numerical matrix into a continuous numerical string;
and step 3: performing characteristic analysis on the numerical string, determining segmentation nodes in numerical penetration, and segmenting the numerical string according to the determined segmentation nodes to obtain a plurality of segmentation substrings;
and 4, step 4: the cloud servers respectively randomly obtain a sub-character string from the plurality of segmented sub-character strings obtained after segmentation, and digitally encrypt the obtained sub-character string to obtain a sub-encryption result;
and 5: and acquiring a sub-encryption result from each cloud server, decrypting the sub-encryption result to obtain a sub-character string, splicing the sub-character strings to obtain a numerical value string, performing an inverse process of converting the numerical value string into the numerical value string by using the numerical value string to obtain a numerical value matrix, and finally performing an inverse process of converting the binary image into the numerical value matrix to obtain the binary image.
Specifically, referring to fig. 3, the present invention completes the encryption of the image by converting the image data into numerical data, which is completely different from the image encryption technology in the prior art, and has the advantages of difficult decryption and high security; meanwhile, in the encryption process, a distributed cloud server group consisting of a plurality of cloud servers is used for encrypting the segmented numerical data, so that the encryption efficiency is higher; the method is mainly realized by the following steps: 1. conversion of numbers and images: according to the method, the image to be processed is firstly binarized, then the binarized image is converted into the corresponding numerical value matrix, then the numerical value matrix is converted into the continuous numerical value string, and then the image encryption is carried out on the converted numerical value string, so that compared with the traditional encryption mode, the encryption mode is not easy to crack, and the safety is higher; 2. dividing the numerical string: the method and the system have the advantages that the numerical string is segmented to obtain the sub-character strings, and then the sub-character strings are encrypted by the aid of the cloud servers, so that the encryption efficiency is higher; 3. constructing a numerical matrix: according to the invention, the image data is converted into the numerical data in a manner of constructing the numerical matrix, and compared with a conversion manner in the prior art, the conversion process is simpler and the conversion efficiency is higher; 4. and (3) encryption algorithm: the encryption algorithm is different from the traditional numerical encryption, the numerical value is encrypted in a transformation mode based on Fourier transformation, and the encrypted result is lower in decryption rate due to the fact that the encryption algorithm is different from the prior art.
Example 8
On the basis of the above embodiment, the performing image binarization in step 1 includes: calculating the image dimensionality of the image to be processed aiming at the gray level image data of the image to be processed, and dividing the image to be processed into low dimensionality or high dimensionality according to the calculated image dimensionality; binarizing the grayscale image data by an integral segmentation method when the image to be processed is of low dimensionality, and binarizing the grayscale image data by a composite segmentation method when the image to be processed is of high dimensionality, thereby generating binarized image data, wherein binarizing the grayscale image data by the composite segmentation method comprises the steps of: by the integral segmentation method, each pixel in the grayscale image data is classified into 3 types: black, white and undetermined; an optimal segmentation threshold is calculated for each pixel belonging to an undetermined class by a local adaptive segmentation method, thereby binarizing the pixel.
Example 9
On the basis of the previous embodiment, the step 2 of converting the image to be binarized into the corresponding numerical matrix, and then converting the numerical matrix into the continuous numerical string includes: taking the binarized image as a parameter A, randomly generating a matrix B, and then constructing a conversion matrix based on the parameter A and the matrix B:
and recording a known conversion factor matrix:
converting matrix and conversion factor matrix
Multiplying to obtain a converted numerical matrix X:
and connecting P and Q in the numerical matrix, and converting into a continuous numerical string.
Example 10
On the basis of the above embodiment, if the image to be processed is a color image, it is converted into a grayscale image to obtain the grayscale image data.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional units, and in practical applications, the functions may be distributed by different functional units according to needs, that is, the units or steps in the embodiments of the present invention are further decomposed or combined, for example, the units in the foregoing embodiment may be combined into one unit, or may be further decomposed into multiple sub-units, so as to complete all or the functions of the units described above. The names of the units and steps involved in the embodiments of the present invention are only for distinguishing the units or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative elements, method steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the elements, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or unit/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or unit/apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent modifications or substitutions of the related art marks may be made by those skilled in the art without departing from the principle of the present invention, and the technical solutions after such modifications or substitutions will fall within the protective scope of the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (10)
1. An image processing system based on a distributed cloud server and a digital graph conversion, the system comprising: the image preprocessing unit is configured for carrying out image binarization on the image to be processed to obtain a binarized image; the digital conversion module is configured to convert the image to be binarized into a corresponding numerical matrix and then convert the numerical matrix into a continuous numerical string; the numerical value segmentation module is configured to perform characteristic analysis on the numerical value string, determine segmentation nodes in the numerical value string, and segment the numerical value string in the numerical value string according to the determined segmentation nodes to obtain a plurality of segmentation substrings; the distributed cloud server group comprises a plurality of cloud servers, wherein each cloud server is configured to randomly obtain a sub-character string from a plurality of segmented sub-character strings obtained after segmentation, and digitally encrypt the obtained sub-character string to obtain a sub-encryption result; and the image restoration unit is configured to acquire the sub-encryption results from the cloud servers, decrypt the sub-encryption results to obtain sub-character strings, splice the sub-character strings to obtain numerical value strings, perform an inverse process of converting the numerical value strings into the numerical value strings by using the numerical value strings to obtain the numerical value matrixes, and finally perform an inverse process of converting the binary images into the numerical value matrixes to obtain the binary images.
2. The system of claim 1, wherein the image pre-processing unit to perform image binarization comprises: calculating the image dimensionality of the image to be processed aiming at the gray level image data of the image to be processed, and dividing the image to be processed into low dimensionality or high dimensionality according to the calculated image dimensionality; binarizing the grayscale image data by an integral segmentation method when the image to be processed is of low dimensionality, and binarizing the grayscale image data by a composite segmentation method when the image to be processed is of high dimensionality, thereby generating binarized image data, wherein binarizing the grayscale image data by the composite segmentation method comprises the steps of: by the integral segmentation method, each pixel in the grayscale image data is classified into 3 types: black, white and undetermined; an optimal segmentation threshold is calculated for each pixel belonging to an undetermined class by a local adaptive segmentation method, thereby binarizing the pixel.
3. The system of claim 2, wherein the digital conversion module converting the image to be binarized into a corresponding matrix of values comprises: will be two-valuedTaking the image as a parameter A, randomly generating a matrix B, and then jointly constructing a conversion matrix based on the parameter A and the matrix B:
and recording a known conversion factor matrix:
converting matrix and conversion factor matrix
Multiplying to obtain a converted numerical matrix X:
and connecting P and Q in the numerical matrix, and converting into a continuous numerical string.
4. The system of claim 3, wherein each of the distributed cloud servers comprises: an acquisition unit configured to randomly acquire one sub-character string from a plurality of divided sub-character strings obtained after division; and the encryption unit is configured to digitally encrypt the obtained sub-character string to obtain a sub-encryption result.
5. The system of claim 4, wherein the encryption unit digitally encrypting the obtained substring comprises: randomly generating a value m as a first key, and performing Fourier transform of m degrees on the sub-character string by using the following formula:
wherein: x (t) denotes a substring, Xm(u) denotes the converted substring, Km(t, u) is a transformation kernel; said Km(t, u) is expressed using the following formula:
wherein t and u are transformation parameters, delta is the Fourier transform of the Dirac function, delta (u-t) represents the Fourier transform of the Dirac function which performs u-t, and delta (u + t) represents the Fourier transform of the Dirac function which performs u + t; fourier transformed Xm(u) as an encrypted substring.
6. The system of claim 5, wherein if the image to be processed is a color image, it is converted into a grayscale image to obtain the grayscale image data.
7. Image processing method based on distributed cloud servers and map transformations based on the system according to one of claims 1 to 6, characterized in that it performs the following steps:
step 1: carrying out image binarization on an image to be processed to obtain a binarized image;
step 2: converting an image to be binarized into a corresponding numerical matrix, and converting the numerical matrix into a continuous numerical string;
and step 3: performing characteristic analysis on the numerical string, determining segmentation nodes in the numerical string, and segmenting the numerical string in the numerical string according to the determined segmentation nodes to obtain a plurality of segmentation substrings;
and 4, step 4: the cloud servers respectively randomly obtain a sub-character string from the plurality of segmented sub-character strings obtained after segmentation, and digitally encrypt the obtained sub-character string to obtain a sub-encryption result;
and 5: and acquiring a sub-encryption result from each cloud server, decrypting the sub-encryption result to obtain a sub-character string, splicing the sub-character strings to obtain a numerical value string, performing an inverse process of converting the numerical value string into the numerical value string by using the numerical value string to obtain a numerical value matrix, and finally performing an inverse process of converting the binary image into the numerical value matrix to obtain the binary image.
8. The method as claimed in claim 7, wherein the binarizing of the image in step 1 comprises: calculating the image dimensionality of the image to be processed aiming at the gray level image data of the image to be processed, and dividing the image to be processed into low dimensionality or high dimensionality according to the calculated image dimensionality; binarizing the grayscale image data by an integral segmentation method when the image to be processed is of low dimensionality, and binarizing the grayscale image data by a composite segmentation method when the image to be processed is of high dimensionality, thereby generating binarized image data, wherein binarizing the grayscale image data by the composite segmentation method comprises the steps of: by the integral segmentation method, each pixel in the grayscale image data is classified into 3 types: black, white and undetermined; an optimal segmentation threshold is calculated for each pixel belonging to an undetermined class by a local adaptive segmentation method, thereby binarizing the pixel.
9. The method as claimed in claim 8, wherein the step 2 of converting the image to be binarized into the corresponding numerical matrix and then converting the numerical matrix into the continuous numerical string comprises: taking the binarized image as a parameter A, randomly generating a matrix B, and then constructing a conversion matrix based on the parameter A and the matrix B:
and recording a known conversion factor matrix:
converting matrix and conversion factor matrix
Multiplying to obtain a converted numerical matrix X:
and connecting P and Q in the numerical matrix, and converting into a continuous numerical string.
10. The method according to claim 9, wherein if the image to be processed is a color image, it is converted into a grayscale image to obtain the grayscale image data.
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