CN113824681A - Image data encryption transmission system based on compressed sensing - Google Patents
Image data encryption transmission system based on compressed sensing Download PDFInfo
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Abstract
The invention discloses an image data encryption transmission system based on compressed sensing, which comprises a data compression module, a data encryption module, a data transmission module, a data decryption module and a data recovery module, wherein the data compression module is used for randomly collecting part of pixel points in original image data pixel points for multiple times by utilizing a random numerical control sampling switch to obtain observation data of an original image; the data encryption module is used for encrypting the observation data; the data transmission module is used for transmitting the encrypted observation data to a data receiving end; the data decryption module is used for decrypting the encrypted observation data at the information receiving end; and the data recovery module is used for recovering the decrypted observation data in the sparse domain. The invention adopts the combination of the volatile memristor and the compressive sensing technology to sample at a rate far lower than the Nyquist sampling rate, thereby obviously reducing the power consumption generated by data storage and transmission and accelerating the data sampling and recovery rate.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to an image data encryption transmission system based on compressed sensing.
Background
In recent years, with the development of artificial intelligence, a large number of perception networks are constructed to realize human-computer interaction. It is expected that by 2025 the number of sensing nodes will reach 750 hundred million and by 2030 will proliferate to 1250 million, however a significant portion of the raw data generated in these sensing nodes is unstructured and redundant. In order to perform efficient data transmission and reduce redundant data, it is necessary to perform data compression near or within the sensory network. The shannon/nyquist sampling theorem tells us that in order to sample a signal uniformly without losing information, the sampling speed must be at least twice as fast as its bandwidth.
The compressed sensing technology, namely an architecture integrating compression and sensing, is one of the important leading-edge technologies for developing artificial intelligence, and can perform sub-sampling at a rate far lower than the nyquist sampling theorem, and then recover an image by eliminating artifacts in an irrelevant domain, so that low-power consumption and high-rate data transmission are performed. In the conventional image compression, full sampling is firstly performed on a signal, small coefficients are discarded, large coefficients are reserved, and an encoder faces the overhead of large coefficient encoding. Under the Nyquist sampling framework, the separability of information is embodied by non-overlapping spectrums in the frequency domain, so that the information is embodied on the spectrums, and the information cannot be separated due to the overlapping of spectrum confusion caused by insufficient sampling rate.
In addition, security is currently an important issue of research in addition to efficient transmission of information. Images are widely applied to the space of the internet of things as a common data form, and the safe transmission of image information becomes more and more important.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides an image data encryption transmission system based on compressed sensing. The technical problem to be solved by the invention is realized by the following technical scheme:
the invention provides an image data encryption transmission system based on compressed sensing, which comprises a data compression module, a data encryption module, a data transmission module, a data decryption module and a data recovery module, wherein,
the data compression module is used for generating random numbers by using the volatile memristor and controlling the sampling switch to randomly collect part of pixel points in the original image data for multiple times by using the random numbers to obtain observation data of the original image; the data encryption module is used for encrypting the observation data to obtain encrypted observation data; the data transmission module is used for transmitting the encrypted observation data to a data receiving end; the data decryption module is used for decrypting the encrypted observation data at the information receiving end to obtain decrypted observation data; and the data recovery module is used for carrying out data processing on the decrypted observation data in a sparse domain to obtain the recovery data of the original image.
In one embodiment of the invention, the data compression module comprises a plurality of pixel data random acquisition sub-modules and a data processing sub-module, wherein,
each pixel data random acquisition submodule is respectively used for generating a random number and controlling the on-off of a sampling switch according to the random number, and carrying out data acquisition on corresponding pixel points in the original image to obtain acquired pixel data;
and the data processing submodule is used for acquiring the acquired pixel data acquired by all the pixel data random acquisition submodules and overlapping the acquired pixel data to acquire overlapped image data.
In an embodiment of the present invention, the data processing sub-module is further configured to control the multiple pixel data random acquisition sub-modules to perform M times of random acquisition on the pixels of the original image, so as to obtain M pieces of superimposed image data in total, and form an M × 1 observation vector Y, where M is smaller than the number of pixels in the original image.
In one embodiment of the present invention, the pixel data random acquisition sub-module includes a random number generation unit, an image data input unit, and a data sampling unit, wherein,
the random number generation unit is used for generating random numbers 0 or 1 which are distributed according to Bernoulli 0, 1;
the image data input unit is used for receiving data of corresponding pixels in an original image and converting the data into current signals;
the data sampling unit is used for collecting the current signal when the random number is 1 and not collecting the current signal when the random number is 0.
In one embodiment of the present invention, the random number generation unit includes a first pulse generator S1, a volatile memristor TSM, a first resistor R1, and a comparator P1, wherein,
the first pulse generator S1 is used for inputting a rectangular wave with a constant frequency;
the volatile memristor TSM is connected between the output terminal of the first pulse generator S1 and the positive input terminal of the comparator P1, and the first resistor R1 is connected between the positive input terminal of the comparator P1 and the ground terminal;
the negative input end of the comparator P1 is used for inputting the pulse voltage VthAnd the output end of the comparator P1 is connected with the data sampling unit.
In one embodiment of the present invention, the image data input unit includes a first pulse generator S2 and a sensor, which are connected to each other, wherein,
the first pulse generator S2 is configured to obtain data of a corresponding pixel in an original image and convert the data into a voltage signal;
the sensor is connected with the data sampling unit and used for transmitting the voltage signal to the data sampling unit.
In one embodiment of the present invention, the data sampling unit includes a second resistor R2, a third resistor R3, and a switching transistor N-MOS, wherein,
one end of the second resistor R2 is connected with a power supply terminal VCC, and the other end is connected with the output end of the comparator P1;
the grid electrode of the switch tube N-MOS is connected with the output end of the comparator P1, the drain electrode of the switch tube N-MOS is connected with the output end of the sensor, and the source electrode of the switch tube N-MOS is used as the output end of the data sampling unit;
the third resistor R3 is connected between the grid electrode of the switch tube N-MOS and the source electrode of the switch tube N-MOS.
In an embodiment of the present invention, the data encryption module is specifically configured to:
and multiplying the scrambling matrix S by the observation vector Y to perform scrambling operation on the observation value in the Mx 1 observation vector Y to obtain the scrambled and encrypted observation vector T.
In an embodiment of the present invention, the data recovery module is specifically configured to:
acquiring an M × N-dimensional observation matrix phi composed of random numbers 0 or 1 from the random number generation unit;
acquiring an NxN-dimensional sparse transformation matrix psi;
obtaining a sparse signal A by utilizing the observation data Y, the observation matrix phi and the sparse transformation matrix psi;
and obtaining a restored signal of the original image data by utilizing the correlation recovery algorithm and the sparse signal A in an uncorrelated sparse domain.
In one embodiment of the invention, the sparse transform matrix is a fourier transform matrix, a wavelet transform matrix or a discrete cosine transform matrix.
Compared with the prior art, the invention has the beneficial effects that:
1. the image data encryption transmission system based on the compressed sensing generates the unpredictable true random number with high fault tolerance by utilizing the natural behavior of the internal ion defect of the volatile memristor, has high fault tolerance, and can effectively compress the original data. The volatile memristor has the advantages of simple structure, compatibility with CMOS, low power consumption and the like, and after the volatile memristor is combined with the compressive sensing technology, sampling is carried out at a rate far lower than the Nyquist sampling rate, so that the power consumption generated by data storage and transportation is greatly reduced, and the data sampling and recovery rate is accelerated.
2. The system of the invention directly sub-samples the original image data, thus avoiding the power waste caused by the prior full sampling, and compared with the scheme of generating random numbers by a computer, the system has the advantages of more stability, higher quality and more safety.
3. According to the image data encryption transmission system based on compressed sensing, encryption operation is performed by disordering the row/column number of the observation vector Y, so that compressed signals can be safely transmitted in the transmission process, the encrypted observation vector is directly reconstructed, and an encrypted image with changed statistical characteristics can be obtained, namely, an original image cannot be found out through the pixel statistical characteristics.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
FIG. 1 is a block diagram of an image data encryption transmission system based on compressed sensing according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a data compression module according to an embodiment of the present invention;
FIG. 3 is a circuit diagram of a pixel data random access sub-module according to an embodiment of the present invention;
fig. 4 is a comparison simulation diagram of the result of encrypting an observation signal by using the image data encryption transmission system and the conventional full sampling system provided by the embodiment of the present invention;
fig. 5 is a schematic diagram of a result of reconstructing the decrypted compressed data by using the image data encryption transmission system according to the embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined objects, the following describes an image data encryption transmission system based on compressed sensing according to the present invention in detail with reference to the accompanying drawings and the detailed description.
The foregoing and other technical matters, features and effects of the present invention will be apparent from the following detailed description of the embodiments, which is to be read in connection with the accompanying drawings. The technical means and effects of the present invention adopted to achieve the predetermined purpose can be more deeply and specifically understood through the description of the specific embodiments, however, the attached drawings are provided for reference and description only and are not used for limiting the technical scheme of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of additional like elements in the article or device comprising the element.
Referring to fig. 1, fig. 1 is a block diagram of an image data encryption transmission system based on compressed sensing according to an embodiment of the present invention. The image data encryption transmission system comprises a data compression module 1, a data encryption module 2, a data transmission module 3, a data decryption module 4 and a data recovery module 5, wherein the data compression module 1 is used for generating random numbers by using a volatile memristor and controlling a sampling switch to randomly collect part of pixel points in original image data for multiple times by using the random numbers to obtain observation data of the original image; the data encryption module 2 is used for encrypting the obtained observation data of the original image to obtain encrypted observation data; the data transmission module 3 is used for transmitting the encrypted observation data to a data receiving end, and the data decryption module 4 is used for decrypting the encrypted observation data at the information receiving end to obtain decrypted observation data; and the data recovery module 5 is used for performing data recovery on the decrypted observation data in a sparse domain to obtain recovered data of the original image.
Further, please refer to fig. 2, wherein fig. 2 is a schematic structural diagram of a data compression module according to an embodiment of the present invention. The data compression module 1 of this embodiment includes a plurality of pixel data random acquisition sub-modules 11 and a data processing sub-module 12, where each pixel data random acquisition sub-module 11 is respectively configured to generate a random number and control the on/off of a sampling switch according to the random number, and acquire data of a corresponding pixel in the original image to obtain acquired pixel data; the data processing submodule 12 is configured to obtain the acquired pixel data obtained by all the pixel data random acquisition submodules 11, and superimpose the acquired pixel data to obtain superimposed image data.
The data processing submodule 12 is further configured to control the multiple pixel data random acquisition submodules 11 to perform M times of random acquisition on the pixels of the original image, so as to obtain M pieces of superimposed image data in total, and form M × 1-dimensional observation data, where M is smaller than the number of pixels in the original image.
The random numbers of this embodiment are generated using volatile memristors, conforming to the bernoulli 0,1 distribution. Specifically, when the random number generated in the pixel data random acquisition submodule 11 is 1, the sampling switch is turned on to acquire the pixel data corresponding to the pixel data random acquisition submodule 11, and when the random number generated in the pixel data random acquisition submodule 11 is 0, the sampling switch is turned off to acquire the pixel data corresponding to the pixel data random acquisition submodule 11. Each pixel data random acquisition submodule 11 obtains a random number at the same time, so that the corresponding random number determines whether to sample, and superimposes all sampled pixel data to obtain a superimposed pixel data. At the same time, a sequence of N random numbers is obtained.
In this embodiment, the original image is sub-sampled by 50%, that is, the pixel data random acquisition sub-modules 11 randomly sample 50% of the pixels in the original image by using the generated random numbers.
Further, the pixel data random acquisition submodule 11 of the present embodiment includes a random number generation unit 111, an image data input unit 112, and a data sampling unit 113, where the random number generation unit 111 is configured to generate random numbers 0 or 1 that are distributed in accordance with bernoulli 0, 1; the image data input unit 112 is used for receiving data of corresponding pixels in an original image and converting the data into current signals; the data sampling unit 113 is configured to collect the current signal when the random number is 1, and not collect the current signal when the random number is 0. The current signal is actually the result of multiplying the original pixel data by 0 and 1 random numbers through the sampling switch. That is, when the random number is 0, the resultant data is also 0, and when the random number is 1, the current pixel data is obtained.
Referring to fig. 3, fig. 3 is a circuit diagram of a pixel data random acquisition submodule according to an embodiment of the present invention. The random number generation unit 111 includes a first pulse generator S1, a volatile memristor TSM, a first resistor R1, and a comparator P1, wherein the first pulse generator S1 is used for inputting a rectangular wave of a constant frequency; the volatile memristor TSM is connected between the output end of the first pulse generator S1 and the positive input end of the comparator P1, and the first resistor R1 is connected between the positive input end of the comparator P1 and the ground end; the negative input terminal of the comparator P1 is used for inputting the pulse voltage VthAnd the output end of the comparator P1 is connected with the data sampling unit.
The image data input unit 112 includes a first pulse generator S2 and a sensor connected to each other, wherein the first pulse generator S2 is used for acquiring data of a corresponding pixel in an original image and converting the data into a voltage signal; the sensor is connected with the data sampling unit and used for transmitting the voltage signal to the data sampling unit.
The data sampling unit 113 comprises a second resistor R2, a third resistor R3 and a switching tube N-MOS, wherein one end of the second resistor R2 is connected to a power supply terminal VCC, and the other end is connected to the output terminal of the comparator P1; the grid electrode of the switch tube N-MOS is connected with the output end of the comparator P1, the drain electrode of the switch tube N-MOS is connected with the output end of the sensor, and the source electrode of the switch tube N-MOS is used as the output end of the data sampling unit; the third resistor R3 is connected between the grid electrode of the switch tube N-MOS and the source electrode of the switch tube N-MOS.
Specifically, in the present embodiment, the random number generation unit 111 is built by using a volatile memristor TSM, the volatile memristor TSM and a pull-up resistor R1 are connected to the positive input terminal of the voltage comparator P1, and the negative input terminal of the voltage comparator P1 is a constant threshold voltage VthThe threshold voltage VthAnd is set according to specific device characteristics. Applying a circuit constant frequency square wave at the first pulse generator S1, a true random number of 1 or 0 can be output at the voltage comparator P1.
The output end of the voltage comparator P1 is connected with a pull-up resistor R2, the other end of the resistor is connected with a power supply VCC, and corresponding output voltage can be obtained at the output end of the voltage comparator P1 by changing the size of the power supply VCC and the pull-up resistors R1 and R2, so that the switch tube N-MOS is driven. The driving voltage of the switching tube N-MOS of this embodiment is 2-4V, and the magnitudes of the power supply VCC and the pull-up resistor R2 can be adjusted, so that when the voltage comparator P1 outputs "1", the switching tube N-MOS is turned on, and then the pixel data obtained by the sensor Sense is acquired, and when the voltage comparator P1 outputs "0", the switching tube N-MOS is turned off, and then the pixel data obtained by the sensor Sense is not acquired. It is noted that the probability of the output bernoulli distribution can be adjusted by controlling the frequency and amplitude of the input waveform of the first pulse generator S1. The pixel data random acquisition submodule built by the volatile memristor is a natural probability behavior and has high fault tolerance.
In this embodiment, each pixel data random acquisition submodule controls the acquisition of one pixel data in the original image, so that the number of the required pixel data random acquisition submodules is the size of the image of the data to be acquired, that is, the number of the pixel points in the image.
In the actual data collecting process, after one-time collection is completed, the data processing submodule 12 is further configured to control the multiple pixel data random collecting submodule 11 to perform M-time random collection on the pixels of the original image, so as to obtain M superimposed image data in total, and form M × 1-dimensional observation data Y, where M is smaller than the number of pixels in the original image. Specifically, after one-time acquisition, a piece of superimposed pixel data is obtained, the multiple pixel data random acquisition sub-module 11 may repeatedly perform multiple random acquisitions on the pixel points of the original image to obtain M pieces of superimposed pixel data, and since each sampling generates one random number sequence of which the number is N, an mxn random number matrix, also called an observation matrix Φ, is obtained at this time.
The data encryption module 2 of the present embodiment can perform scrambling operation on the obtained observation data of the original image to realize encryption of the observation data Y.
The data encryption module 2 performs scrambling operation on the numerical value in the M × 1-dimensional observation data (observation vector) Y, that is, multiplies the observation vector Y by a scrambling matrix S, thereby obtaining a scrambled and encrypted observation vector based on the observation vector Y.
Specifically, a row/column scrambling operation is performed on the observation vector Y, and a matrix expression is written as follows:
wherein the content of the first and second substances,
in order to scramble the encrypted observation vector,
in order to replace the observation vector before encryption,
represents a scrambling matrix, T may be represented by SijIs represented by a radical, Ti＝Si1*Y1+Si2*Y2+…+SiM*YMFor example, it is necessary to convert Y in the original observation vectoriAnd YjCarry out the exchange scrambling by only Sii＝0，Sij＝1，SjjAnd SjiIn the same way, the ith row and the jth row of the scrambling matrix S are exchanged, and the scrambling and encryption of the observation vector Y can be completed.
In other embodiments, the data encryption module 2 may encrypt the observation vector Y in any other suitable manner, which is not listed here.
It is worth pointing out that, by adopting the row/column scrambling encryption operation, the signal is transmitted after being encrypted, which not only ensures the security of the signal in the transmission process, but also can obtain the encrypted image with completely changed statistical characteristics after the image is reconstructed, so that the original image cannot be found out through the image statistical characteristics. Therefore, in the embodiment, the observation data Y after the compressed observation is encrypted by using the compressed sensing technology, and a safer and more effective encryption mode than the traditional method of directly encrypting the full-sampling image can be obtained.
Subsequently, the data transmission block 3 transmits the observation vector T subjected to the above-described encryption processing to the information receiving terminal. And the data decryption module 4 positioned at the information receiving terminal decrypts the observation vector T subjected to the encryption processing to obtain decrypted observation data.
In this embodiment, at the information processing terminal, the inverse scramble operation is performed on the row/column corresponding to the observation vector T subjected to the encryption processing based on the key (i.e., the scramble matrix S) to restore to the original position, and the decryption of the observation vector Y is completed, i.e., STT ═ Y, where STRepresenting the inverse of the scrambling matrix S.
In other embodiments, observation vector T is decrypted using a decryption method corresponding to the encryption method described above.
Then, the data recovery module 5 performs data processing on the decrypted observation data in a sparse domain to obtain the restored data of the original data.
The data recovery module 5 of this embodiment is specifically configured to:
(1) an M × N-dimensional observation matrix Φ composed of random numbers 0 or 1 is acquired from the random number generation unit.
As described above, in the process of data sampling and compression, each sampling generates a random number sequence with the number of N, and after M times of repeated acquisition, an M × N random number matrix is obtained, that is, the observation matrix Φ.
(2) A sparse transform matrix ψ of dimension N × N is acquired.
In this embodiment, the sparse transform matrix is a fourier transform matrix, a wavelet transform matrix, or a discrete cosine transform matrix. Fourier transform, wavelet transform or discrete cosine transform are all commonly used data transformation processes, and are not described herein.
(3) And obtaining a sparse signal A by utilizing the decrypted observation data Y, the observation matrix phi and the sparse transformation matrix psi.
Specifically, the sparse signal a is solved using the following equation:
Y＝Φ*Ψ*A，
where Y represents M × 1-dimensional observation data, Φ represents M × N observation matrix, and Ψ is N × N-dimensional sparse transformation matrix. The sparse signal a obtained as described above is an N × 1 vector.
(4) And obtaining a restored signal of the original image data by utilizing the correlation recovery algorithm and the sparse signal A in an uncorrelated sparse domain.
Specifically, the sparse signal a may be directly multiplied by the sparse transform matrix Ψ to obtain a restored signal of the original image data. It should be noted that, for large matrix operation, minimizing the residual error by using an OMP (Orthogonal Matching Pursuit) algorithm has a better recovery effect, and the specific process is not described herein again.
Referring to fig. 4, fig. 4 is a simulation diagram comparing the results of encrypting the observation signal by using the image data encryption transmission system and the conventional full sampling system according to the embodiment of the present invention, where (a) is an original image, (d) is a pixel histogram of the original image, (b) is a schematic diagram of the results of the conventional full sampling row-column scrambling encryption, (e) is a schematic diagram of the pixel histogram of the encryption result of (b), (c) is a schematic diagram of the results of encrypting the observation signal by using the system according to the embodiment of the present invention, and (f) is a corresponding pixel histogram of (c). As can be seen from the graphs (b) and (e), the conventional full sampling system cannot change the statistical properties of the image after the line scrambling encryption algorithm is applied to the data, and the encrypted data is very easy to be broken. As can be seen from fig. 4(c) and 4(f), the image data transmission image data encryption transmission system provided by the embodiment of the present invention performs 50% of sub-sampling and then performs row/column scrambling encryption, so that the statistical characteristics of the image are changed, and the information is more secure.
Referring to fig. 5, fig. 5 is a schematic diagram illustrating a result of reconstructing the decrypted compressed data by the image data encryption transmission system according to the embodiment of the present invention. It can be seen that when the sampling point is 50% of the original data, the numbers can be clearly distinguished, thereby proving that the image data encryption transmission system of the embodiment is a very potential compressed sensing system. Due to the internal natural probability behavior of the volatile memristor, the generated true random number has the advantages of being unpredictable, good in stability, high in fault tolerance rate and the like, the built compression sensing system can sample at a rate far lower than the Nyquist sampling rate, power consumption required by data storage and transportation is reduced, and the operation speed is obviously improved.
In summary, in the image data encryption transmission system based on compressed sensing, a natural behavior of an internal ion defect of a volatile memristor is utilized to generate a true random number which is unpredictable and has high fault tolerance, so that the image data encryption transmission system has high fault tolerance and can effectively compress original data. The volatile memristor has the advantages of simple structure, compatibility with CMOS, low power consumption and the like, and after the volatile memristor is combined with the compressive sensing technology, sampling is carried out at a rate far lower than the Nyquist sampling rate, so that the power consumption generated by data storage and transportation is greatly reduced, and the data sampling and recovery rate is accelerated. Because the system of the embodiment directly sub-samples the original image data, the power waste caused by the previous full sampling is avoided, and compared with the scheme of generating random numbers by a computer, the system has the advantages of more stability, higher quality and higher safety; the system conducts encryption operation by disordering the row/column number of the observation vector Y, so that compressed signals can be safely transmitted in the transmission process, the encrypted observation vector is directly reconstructed, and an encrypted image with changed statistical characteristics can be obtained, namely, an original image cannot be found out through pixel statistical characteristics.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. An image data encryption transmission system based on compressed sensing is characterized by comprising a data compression module (1), a data encryption module (2), a data transmission module (3), a data decryption module (4) and a data recovery module (5),
the data compression module (1) is used for generating random numbers by using a volatile memristor, and controlling a sampling switch to randomly collect part of pixel points in original image data for multiple times by using the random numbers to obtain observation data of the original image; the data encryption module (2) is used for encrypting the observation data to obtain encrypted observation data; the data transmission module (3) is used for transmitting the encrypted observation data to a data receiving end; the data decryption module (4) is used for decrypting the encrypted observation data at the information receiving end to obtain decrypted observation data; the data recovery module (5) is used for performing data processing on the decrypted observation data in a sparse domain to obtain the restored data of the original image.
2. The compressed sensing-based image data encryption transmission system according to claim 1, wherein the data compression module (1) comprises a plurality of pixel data random acquisition sub-modules (11) and a data processing sub-module (12), wherein,
each pixel data random acquisition submodule (11) is respectively used for generating a random number and controlling the on-off of a sampling switch according to the random number, and carrying out data acquisition on corresponding pixel points in the original image to obtain acquired pixel data;
the data processing submodule (12) is used for acquiring the acquired pixel data obtained by all the pixel data random acquisition submodules and overlapping the acquired pixel data to acquire overlapped image data.
3. The compressed sensing-based image data encryption transmission system according to claim 2, wherein the data processing sub-module (12) is further configured to control the multiple pixel data random acquisition sub-modules to randomly acquire M times the pixels of the original image, so as to obtain M superimposed image data in total to form an M × 1 observation vector Y, where M is smaller than the number of pixels in the original image.
4. The compressed sensing-based image data encryption transmission system according to claim 2, wherein the pixel data random acquisition sub-module (11) includes a random number generation unit (111), an image data input unit (112), and a data sampling unit (113), wherein,
the random number generation unit (111) is used for generating random numbers 0 or 1 which are distributed according to Bernoulli 0, 1;
the image data input unit (112) is used for receiving data of corresponding pixels in an original image and converting the data into current signals;
the data sampling unit (113) is configured to collect the current signal when the random number is 1, and not collect the current signal when the random number is 0.
5. The compressive sensing-based image data encryption transmission system according to claim 4, wherein the random number generation unit (111) includes a first pulse generator S1, a volatile memristor TSM, a first resistor R1, and a comparator P1, wherein,
the first pulse generator S1 is used for inputting a rectangular wave with a constant frequency;
the volatile memristor TSM is connected between the output terminal of the first pulse generator S1 and the positive input terminal of the comparator P1, and the first resistor R1 is connected between the positive input terminal of the comparator P1 and the ground terminal;
the negative input end of the comparator P1 is used for inputting the pulse voltage VthAnd the output end of the comparator P1 is connected with the data sampling unit.
6. The compressed sensing-based image data encryption transmission system according to claim 5, wherein the image data input unit (112) includes a first pulse generator S2 and a sensor connected to each other, wherein,
the first pulse generator S2 is configured to obtain data of a corresponding pixel in an original image and convert the data into a voltage signal;
the sensor is connected with the data sampling unit and used for transmitting the voltage signal to the data sampling unit.
7. The compressed sensing-based image data encryption transmission system according to claim 6, wherein the data sampling unit (113) includes a second resistor R2, a third resistor R3 and a switch tube N-MOS, wherein,
one end of the second resistor R2 is connected with a power supply terminal VCC, and the other end is connected with the output end of the comparator P1;
the grid electrode of the switch tube N-MOS is connected with the output end of the comparator P1, the drain electrode of the switch tube N-MOS is connected with the output end of the sensor, and the source electrode of the switch tube N-MOS is used as the output end of the data sampling unit;
the third resistor R3 is connected between the grid electrode of the switch tube N-MOS and the source electrode of the switch tube N-MOS.
8. The compressed sensing-based image data encryption transmission system according to claim 3, wherein the data encryption module (2) is specifically configured to:
and multiplying the scrambling matrix S by the observation vector Y to perform scrambling operation on the observation value in the Mx 1 observation vector Y to obtain the scrambled and encrypted observation vector T.
9. The compressed sensing-based image data encryption transmission system according to claim 1, wherein the data recovery module (5) is specifically configured to:
acquiring an M × N-dimensional observation matrix Φ composed of random numbers 0 or 1 from the random number generation unit (111);
acquiring an NxN-dimensional sparse transformation matrix psi;
obtaining a sparse signal A by utilizing the observation data Y, the observation matrix phi and the sparse transformation matrix psi;
and obtaining a restored signal of the original image data by utilizing the correlation recovery algorithm and the sparse signal A in an uncorrelated sparse domain.
10. The compressed sensing-based image data encryption transmission system according to claim 9, wherein the sparse transform matrix is a fourier transform matrix, a wavelet transform matrix, or a discrete cosine transform matrix.
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