137 results about "Bispectrum" patented technology

The present invention is a system and method that produces features and indices that indicate the presence or absence of a disease or condition, or of the progression of a disease or condition. The system and method of the present invention also produce features and indices that predict responsiveness to medication from a premedication baseline. The system and method of the present invention further incorporates a testing methodology to improve the performance characteristics of the features or indices. To obtain such features and indices, time domain, power spectrum, bispectrum and higher order spectrum values are derived from biopotential signals taken from the subject being tested.

Factor retrieving is a major approach for pulse wave analysis. Stiffness index and cardiac output are widely used factors for cardiac risk detection. Research has been done on clinical pulse wave data which are collected by pulse oximeter. The result shows that collected factors have a positive correlation with certain cardiac risks. Some adjustments have been applied on the algorithms that increase the significance. In addition to the factor based analysis, other signal processing techniques for pulse waveforms are included such as bispectrum estimation, Wavelet transform, and weighted dynamic time warping. Bispectrum estimation and Wavelet transform have meaningful features of pulse waveforms with some special shapes. Weighted dynamic time warping compares the similarity of waveforms. It also includes medical significance into the calculation by adjusting the weight vector. This algorithm has higher accuracy when providing more samples to compare. The factor based analysis and waveform analysis compose an analytic model which can be used for risk evaluation, classification and disease detection.

An apparatus for diagnosing sleep disorders such as OASHS from snore sounds includes a segmentation module (126) coupled to a data logger (124) to provide segments of the digitized audio signal to a Snore Segment Identifier (128). A total airways response (TAR) module (130), pitch calculator (132) and MFCC calculator (134) are each coupled to an output side of the snore segment identifier module (128). Each of these modules is respectively arranged to calculate pitch, bispectrum, diagonal slice and MFCC parameters for the snore segments received from the snore segment identifier (128). Similarly, the NGI calculator (136) produces a non-Gaussianity index for the digitized audio signal. A classification module (144) is arranged to process the calculated parameters and compare a resulting diagnosis probability to a predetermined threshold value. The results of this comparison are then indicated on video display (142), which communicates with the classification module (133) via display controller (140) and bus (145). For example, if the results of the comparison are over threshold then display (142) is driven to indicate “OS AHS is present”.

The invention discloses a fundamental wave based on Kaiser Window bispectrum line interpolation FFT (Fast Fourier Transform) and a harmonic wave detection method. The method comprises the following steps: analog-to-digital conversion and low-pass filtration treatment is conducted on the signal to be detected; the Kaiser Window with free proportion selection between the attenuation of a main lobe and side lobes, flexible design realization, low side lobe level and quick attenuation speed is adopted to conduct windowing FFT operation on the signal; and bispectrum line interpolation algorithm is used for detecting base wave and harmonic quantities of the detected signal. The following aspects are included: (1) analog-to-digital conversion and low-pass filtration; (2) the FFT operation based on the Kaiser Window; (3) base wave and harmonic wave parameters derived by bispectrum line interpolation and polynomial fitting.

An apparatus for diagnosing sleep disorders such as OASHS from snore sounds includes a segmentation module (126) coupled to a data logger (124) to provide segments of the digitized audio signal to a Snore Segment Identifier (128). A total airways response (TAR) module (130), pitch calculator (132), and MFCC calculator (134) are each coupled to an output side of the snore segment identifier module (128). Each of these modules is respectively arranged to calculate pitch, bispectrum, diagonal slice, and MFCC parameters for the snore segments received from the snore segment identifier (128). Similarly, the NGI calculator (136) produces a non-Gaussianity index for the digitized audio signal. A classification module (144) is arranged to process the calculated parameters and compare a resulting diagnosis probability to a predetermined threshold value. The results of this comparison are then indicated on video display (142), which communicates with the classification module (133) via display controller (140) and bus (145). For example, if the results of the comparison are over threshold then display (142) is driven to indicate “OS AHS is present.”

The invention discloses a radar radiation source identification method based on phase noise unintentional modulation characteristic, relates to an identification method of a radar radiation source, and aims to solve the problem that the identification rate of an existing radiation source identification method based on phase noise is not high. The method disclosed by the invention comprises the following steps of analyzing the structure of a phase-lock frequency synthesizer in a radar transmitter system; building a model of phase noise generated by the phase-lock frequency synthesizer; calculating a bispectrum diagonal slice characteristic and a bispectrum non-diagonal slice characteristic; forming a characteristic matrix Y by using a bispectrum diagonal slice characteristic matrix A1 and a bispectrum non-diagonal slice characteristic matrix B1; performing PCA (Principal Component Analysis) dimensional reduction and building a type-known transmitter vector machine model; identifying a transmission signal of a type-unknown transmitter by utilizing the built vector machine model so as to realize the identification of a radar radiation source. The method disclosed by the invention is applicable to the identification of the radar radiation source.

The invention discloses a bispectra and EMD fusion feature-based mobile phone individual identification method. The method comprises the steps of performing bispectra calculation on all samples, and performing dimension reduction by utilizing PCA to obtain a feature set X; calculating empirical mode decomposition of each sample to obtain a power spectrum of a signal stray component, and performing Fisher judgment analysis to obtain a feature set Y; performing CCA feature fusion on the feature set X and the feature set Y to obtain a fused feature set Z; and performing horizontal segmentation on Z according to a ratio of m% to n% to obtain a training set ZTrain and a test set ZTest, training a random forest classifier by using the training set ZTrain, performing classification decision on the test set ZTest by utilizing the trained classifier, and finally outputting a mobile phone individual identification result.

The aim of the invention is to provide an individual identification method of an FSK (frequency-shift keying) signal based on slice bi-spectrum and wavelet transformation. The method comprises the following steps: carrying out bi-spectrum and slice bi-spectrum analysis on the received signal to obtain a spectrogram of the slice bi-spectrum of the signal; respectively building envelope parameter characteristic databases under different signal to noise ratios; carrying out wavelet transformation on the received signal, and extracting a mean value of low-frequency wavelet coefficients; simultaneously building characteristic databases of 4FSK signals of different M numbers and different modulation parameters under different signal to noise ratios; fusing the slice bi-spectrum envelope parameter characteristics and the low-frequency wavelet coefficients; identifying the modulation type of the FSK signal, and achieving individual identification of the 4FSK signals of different parameters by the same signal processing process. According to the individual identification method, the defects of high requirements on the signal to noise ratios of the existing class modulation identification method can be overcome, and individual identification on the FSK signal is carried out in real time under the conditions of low signal to noise ratio and less priori knowledge.

The invention discloses a carrier frequency estimation method for an underlay spectrum sharing time-frequency overlapped MASK signal. The method comprises the following steps: calculating a diagonal slice spectrum of a cyclic bispectrum of the received time-frequency overlapped MASK signal, and intercepting a f= 0 section; carrying out noise elimination and enhancement on a spectral line by using a minimum optimization algorithm with L0 norm; carrying out segmental windowing on the spectral line, setting a local threshold, and only retaining a discrete spectral line larger than the threshold; calculating a strength mean ratio of the spectral line in each window, searching a local maximum value of the strength mean ratio of the spectral line in each window, counting the local maximum values, and sorting the local maximum values according to size; and obtaining frequency corresponding to the local maximum values according to the size sequence in accordance with the number of time-frequency overlapped signal components, namely carrier frequency of the time-frequency overlapped MASK signal. According to the invention, the estimation performance in an environment with a low signal to noise ratio is good; and in the case that two signals or three signals are overlapped, when the signal to noise is larger than 5dB, the estimation performance of the carrier frequency is better.

The invention provides a frequency mixing nonlinear ultrasonic detection method for structure microcracks based on bispectrum analysis, and belongs to the detection field of nondestructive examination. The method comprises the steps of firstly, obtaining an amplitude-versus-frequency curve of an excited probe and an excited receiving probe through frequency sweep experiments, and determining probe excitation frequency or range according to the amplitude-versus-frequency curve; then, carrying out different lateral excitation frequency mixing mode experiments, tracking difference frequency and sum frequency signals, and determining an optimal frequency of the excitation signals according to amplitude-versus-frequency response characteristics of the difference frequency and sum frequency signals; performing bispectrum analysis on the time domain signals at the optimal frequency, and determining whether structure microcracks exist in a test piece or not according to whether mixing components appear in the bispectra or not; and scanning the length direction of a test piece and tracking the difference frequency and sum frequency signals by changing time delay of the excitation signals, and determining the positions of the structure microcracks according to the amplitude-versus-frequency response characteristics of the difference frequency and sum frequency signals. Influences on experimental results caused by nonlinearity of an experiment apparatus can be prevented by employing two probes to excite the signals; and the positions of the structure microcracks can be identified by making the two signals encountered through controlling the delay time of the excited signals.

A method for analyzing instantly weighted synchronous extrusion wavelet bispectrum, comprising six steps as follows: step 1, determining minimum segmentation number of a signal, step 2, respectively calculating weight coefficient corresponded by each segmentation, step 3, forming a weight matrix of the signal with the weight coefficient corresponded by each signal segmentation and calculating the frequency sequence corresponded by the weight coefficient in each list, step 4, calculating synchronous extrusion wavelet coefficient of the signal and obtaining a time-frequency domain expression whose distribution is more compact, step 5, weighting the synchronous extrusion wavelet transformation coefficient according to an frequency interval to obtain a corrected wavelet coefficient, step 6, calculating weighted synchronous extrusion wavelet bispectrum and instantly weighted synchronous extrusion wavelet bispectrum. The method of the invention can be used for accurately differentiating the frequency component of the signal by using the synchronous extrusion wavelet transformation with higher frequency resolution and adding weight to all calculated synchronous extrusion wavelet coefficient via slicing according to the time in accordance with the information of the total signal frequency distribution, which overcomes the shortcomings that the traditional method is low in accuracy.

The invention relates to a bispectrum weighted spatial correlation matrix-based speech sound source localization method. The objective of the invention is to solve problems in microphone array-based robust sound source localization in an actual complex noise environment. According to the method, the special mathematical properties of speech signals and noise signals received by a microphone array on a bispectrum domain are utilized. The method includes the following steps that: framing and bispectrum estimation are performed on signals acquired by the microphone array; on the bispectrum domain, the bispectrum phase difference of each microphone and a reference microphone is calculated; the signals of the reference microphone are adopted to estimate bispectrum unit weight; a bispectrum weighted spatial correlation matrix corresponding to a candidate direction is calculated according to the bispectrum phase difference and the bispectrum unit weight; a sound source direction cost function for a current candidate direction is calculated based on the eigenvalue of the bispectrum weighted spatial correlation matrix; and the direction of a speech sound source is estimated according to a direction corresponding to the maximum value of the sound source direction cost function.

The invention discloses a radar target identification method under a noise background based on bispectrum de-noising and the method is mainly used for solving the problems in the prior art that target identification is carried out by directly utilizing a distance image which is not de-noised under the noise background so that the identification rate is poor. The realization process comprises the following steps of: normalizing a training sample and extracting the power spectrum characteristic of the normalized training sample; utilizing a power spectrum characteristic training classifier of the training sample to obtain a weight coefficient of the classifier; utilizing a bispectrum de-noising method to carry out de-noising treatment on a testing sample and recover an average distance image which is de-noised, and normalizing the distance image; extracting a power spectrum characteristic of the normalized average distance image; and utilizing the trained classifier to classify the power spectrum characteristic of the normalized de-noised average distance image, and determining a target class mark number. The radar target identification method disclosed by the invention has the advantages of stabilizing noises and recovering the average distance image which is de-noised, and can be used for radar target identification.

Optical probes for medical applications are provided. The probe is devised so as to be able to be held in one hand. A basic version of the probe includes: a first excitation lighting source suitable for causing a fluorescence radiation of predetermined substances; a second visible lighting source, the first and the second source being devised so as to illuminate a common zone termed the intervention zone; a first photosensitive matrix sensor; and a second photosensitive matrix sensor. The first and second photosensitive matrix sensors are devised in such a way that, when the optical probe is arranged a predetermined distance from the intervention zone, the image in the visible spectrum of the said zone is formed on the photosensitive surface of the first matrix sensor and the image in the fluorescence spectrum of the said zone is formed on the photosensitive surface of the second sensor. A first variant of the probe includes only a single optical objective, a second variant only a single photosensitive matrix sensor, and a third variant makes it possible to work under polarized light.

The invention belongs to the field of digital speech signal processing, and discloses a polyphony multi-pitch estimation method and application of pseudo bispectra to multi-pitch estimation to solve the problem of multi-pitch estimation of polyphony. The method is technically characterized by comprising the steps of framing input audio frequency; solving the pseudo bispectrum of each frame of signals, ranking the values of the matching cross-correlation function between a two-dimensional template and pseudo bispectra from large to small, and taking the first several frequencies as candidate pitches; calculating the weighting harmonic energy sum of each candidate pitch, and selecting the candidate pitch with the largest weighting harmonic energy sum as the most significantly estimated pitch output by this time of iteration. The method has the advantages that normal work can be conducted when the number of harmonic components is small, notes with overlapped harmonic frequency components can be distinguished, calculation amount is small, and the method is easy to realize and can also be used for base frequency extraction of harmonic signals besides polyphony.

The invention relates to an anti-seismic property probability assessment method based on behavior bispectrum. The anti-seismic property probability assessment method comprises the following steps that1, the anti-seismic property level of an engineering structure is determined; 2, property level displacements for setting a single-freedom-degree system under different property levels are confirmed;3, a simulation test is performed through a single-freedom-degree elastic-plastic differential equation; 4, a behavior earthquake dynamic-inputting spectrum and a behavior response spectrum are respectively obtained; 5, seismic oscillation probability distribution models of each period point and seismic response probability distribution models under different property levels are determined through K-S inspection; 6, probability assessment is conducted on input seismic oscillations under the different property levels; 7, probability assessment is conducted on seismic response accelerations reaching the different property levels; 8, probability assessment is conducted on structural base shear parameters; 9, probability assessment is conducted on a structural seismic action effect. By adopting the anti-seismic property probability assessment method, more scientific and accurate assessment results are provided for the safety of anti-seismic structures at the probability level based on theanti-seismic property level.

The invention discloses a communication radiation source individual identification method and system. According to the technical scheme, the method comprises the steps of: receiving communication radiation source signal, carrying out radio frequency preselection amplification, carrying out frequency mixing, carrying out intermediate frequency filtering, carrying out A / D conversion, carrying out digital orthogonal demodulation, carrying out rectangular integration double-spectrum transformation, dividing rectangular integration double-spectrum characteristic vectors into a training sample set and a testing sample set, constructing a rectangular integration double-spectrum characteristic dictionary, carrying out non-linear transformation, carrying out mapping to a cooperation projection matrix, constructing a classifier, obtaining classification residual errors, and using the type corresponding to the smallest classification residual error as type of a communication radiation source individual. According to the invention, a small sample problem in a communication radiation source individual identification process is solved, the time complexity of the algorithm is lowered, and the phase and amplitude information distortion problem in the process when an existing method based on the time domain, frequency domain or time frequency domain is used to process non-stable or non-Gaussian signals is adopted.

The invention discloses a totally-blind digital speech authentication method, which comprises the following steps: (1) performing framing treatment on a speech signal to be judged; (2) dividing each frame signal into K sections, and performing mean value processing on various sections of values; (3) performing FFT transformation on various sections of data of various frames, and solving double spectral values of the various frames of data; (4) making a double spectral contour diagram through the double spectral estimate values of the various frames of data, which are obtained by smoothing; (5) judging whether nonlinear coupling exists through the double spectral contour diagram of the frame speech signals; (6) judging whether the falsified frame speech signal which is determined for the first time needs the secondary determination of more precise falsified position; and (7) performing more fine framing on the falsified frame speech signal requiring secondary determination, repeating the steps from (2) to (6), and determining more accurate falsified position of the frame speech signal. The method can effectively judge whether the digital speech signal is falsified and determine the falsified position of the digital speech signal under the condition of the deficiency of an authentication message of digital watermarking or digital signature, and does not need to embed any data into the speech signal in advance in the authentication process.

The invention provides a transmitter individual recognition method based on combination of bispectrum analysis and a deep convolutional neural network. In order to cope with the increasingly complicated electromagnetic environment, intercepted radio signals need to be classified and analyzed, and different transmitters further need to be recognized. A radio signal transmitter individual recognition method adopted at present is poor in effect under the low signal-to-noise ratio condition. The transmitter individual recognition method based on combination of bispectrum analysis and the deep convolutional neural network comprises the main steps that first, signals transmitted by different transmitter individuals are analyzed through a direct bispectrum method, and thus a bispectrum feature matrix is obtained and converted into two-dimensional feature images; second, the two-dimensional feature images are classified through the convolutional neural network; and third, a classifying resultis output, and the different transmitter individuals are recognized. The transmitter individual recognition method can be applied to the fields of individual recognition of communication transmittingequipment, recognition of the various radiation source transmitters in electronic countermeasure and the like.

The invention discloses a radiation source identification method based on multi-feature fusion. Aiming at the problem that the radiation source identification accuracy is low, and on the basis of an existing radiation source identification method based on rectangular integral bispectrum and principal component analysis, the invention provides a radiation source identification method based on the fusion of a box dimension after local mean decomposition (LMD) and a rectangular integral bispectrum (SIB) after dimension reduction. Therefore, the fusion feature formed by the SIB feature and the time-frequency analysis feature is used as the radiation source feature for identification, the negative influence caused by signal waveform abrupt change is eliminated, and the identification accuracy and robustness are remarkably improved. Meanwhile, under the condition that the sampling rate and the sensitivity of the sampling equipment are lower, the effective identification of the radiation source can also be achieved.

The invention discloses a double spectrum characteristic and deep learning based mobile phone individual identification method. All samples are sampled and preprocessed, four types of integral double-spectrums are calculated and combined to obtain a characteristic set of a training convolutional neural network, and the characteristic set is divided into a training characteristic set YTrain and a test characteristic set Ytest; and the training characteristic set YTrain is used to train the convolutional neural network, the trained convolutional neural network is used to carry out classified decision on the test characteristic set YTest, and a mobile phone individual identification result is output.

The invention discloses a communication radiation source individual recognition method based on related entropy sparse representation. The method comprises the following steps: receiving communicationradiation source signals, extracting rectangular integration bispectrum feature vectors of I / Q two-path digital zero intermediate frequency signals, dividing the rectangular integration bispectrum feature vectors of all the received communication radiation source signals into training sample sets and testing sample sets, forming a rectangular integration bispectrum feature dictionary from the training sample sets, respectively performing related entropy sparse representation on each testing sample in the testing sample sets on the rectangular integration bispectrum feature dictionary, obtaining a representation coefficient vector of each testing sample, obtaining representation residuals of different categories of training sample sub-sets to the testing samples according to the representation coefficient vector, and taking the categories corresponding to the minimum representation residual as categories of communication radiation source individuals. The problems existing in the conventional method that the method cannot be used for recognizing communication radiation sources of the same manufacturer and the same model, the same signal source cannot be recognized in different noiseenvironments and the like are solved.

The invention belongs to the field of mechanical equipment fault diagnosis. The invention discloses a bearing fault diagnosis method using a probabilistic principal component to enhance a cyclic bispectrum. The method includes the steps of firstly, establishing a probabilistic principal component model capable of reflecting a potential relation between an original signal and a principal componentvector; secondly, de-noising the original signal by using the probabilistic principal component model to completely preserve useful components of the signal, greatly increase the signal-to-noise ratioand solve the problem that a fault signal is susceptible to noise interference; and thirdly, subjecting the de-noised signal to cyclic bispectrum analysis, and diagnosing a bearing fault from a contour map of the single cyclic frequency bispectrum. For one thing, by using the probabilistic principal component analysis for de-noising, the method of the invention can effectively increase the signal-to-noise ratio and suppress the noise to highlight a phenomenon of fault frequency modulation near the bearing resonance frequency, thereby increasing the definition of a hexagonal structure formed by the cyclic bispectrum; and for another, can visually detect the type of bearing fault from the contour map of the single cyclic frequency bispectrum.

The invention relates to a bi-spectrum analysis method based on combination of a circulation modulation frequency spectrum and segmentation mutual correlation. The method comprises steps of calculating a circulation modulation frequency spectrum of a signal to noise ratio hydrophone signal; carrying out Fourier transform on the circulation modulation frequency spectrum to obtain a time domain signal; inputting the time domain signal into a full-phase position digital filter; dividing a filtered signal into multiple segments of mutually correlated bi-spectrums and then carrying out combination;calculating the significance level of the bi-spectrum; taking the significance level as an acceptance region of secondary phase position coupling; calculating to obtain a slice spectrum of a bi-coherent index spectrum; and finally, extracting an axis frequency and harmonic waves of each order of a target subjected to the secondary phase position coupling from the low-frequency band of the slicingspectrum. According to the invention, under the condition of a low signal to noise ratio, the axis frequency and harmonic waves subjected to the secondary phase position coupling in the hydrophone signal can be effectively detected; the method has quite high sensitivity and robustness; and data verification shows that even under the condition of the low signal to noise ratio, the axis frequency and harmonic waves of each order of the target subjected to the secondary phase position coupling can be precisely detected.

A mechanism of quantitative dual-spectrum IR imaging (QDS-IR) system for examining the breast cancer is reported. The major mechanism of the system is a pair of long-wave Infra-red (LIR) and middle-wave Infra-red (MIR) cameras with the keen temperature sensitivity and the high spatial resolution. The optical axes of cameras are calibrated by the help of two calibration makers set up on the seat for carrying an object to make them parallel to each other and locate on the same level. The design provides an imaging system with the high reproducibility supported by 7 free degrees and the high adjustability. The proposed system could ensure the positions of the object and two cameras are the same at the different time points and find the best relative positions between the seat and two cameras for the objects with different body types. Therefore, it has potential ability to detect breast cancer or monitor the effect of chemotherapy.

The invention discloses a radio frequency fingerprint identification method based on QPSK signal bispectrum energy entropy and color moments, relating to the field of wireless communication. A bit stream signal of a sending end is subjected to QPSK mapping to obtain a signal s(n). Through up conversion, a frequency modulation signal p(n) is obtained and is inputted into a power amplifier to output a signal Phi (n), an analog signal is obtained through digital to analog conversion processing, the analog signal is sent out and is added into AWGN in a sending process, a receiving end obtains a digital signal r(n) through analog to digital conversion processing, a baseband signal is obtained through down conversion, and radio frequency fingerprint characteristics including the bispectrum energy entropy, a first-order moment and a second-order moment are extracted from the baseband signal. Then through an SVM classifier, the classification training and testing of the radio frequency fingerprint characteristics are carried out, and a test category result is obtained. Through comparing the test category result and an actual category result, a classification accuracy rate Pc is obtained. According to the method, the radio frequency signals are effectively classified, and the identification accurate rate under a low signal-to-noise ratio is improved by nearly 20% compared with a traditional method.

The invention relates to the field of aseismic design technology, in particular to a multi-modal performance-based aseismic design method based on a condition bispectrum. According to the method, aseismic design is performed directly based on quantitative performance levels, wherein first, a single-freedom-degree elastic-plastic structural system of the condition bispectrum is artificially assumed, by adjusting performance displacement angles rho<obj-m>(d) of structural top layers and second stiffness of a double-broken-line elastic-plastic model, the single-freedom-degree elastic-plastic structural system is identical to a standard single-freedom-degree elastic-plastic structural system, and then structural aseismic response effects under the aseismic performance levels are solved. According to the aseismic design method based on the performance levels, a final destruction state serves as the basis of aseismic design, and therefore the aseismic performance of a structure can be controlled no matter whether an earthquake with super fortification intensity (ground motion parameters) occurs. Obviously, the aseismic design is more scientific compared with traditional performance-basedaseismic design based on fortification intensity (ground motion parameters), and the aseismic behavior of the structure can be controlled.

The invention discloses a wide-waveband large-span tuned filter single-channel colorful low-level-light night vision method and device. The method comprises the following steps of: installing a dual-waveband tuned filter based on low-light-level night vision spectrum break points on a low-light-level imaging detector array of a single-channel low-light-level image detecting system, controlling the dual-waveband tuned filter by frame synchronizing signals so as to alternatively cut the low-level-light waveband into a low-light-level long-waveband and a low-light-level short-waveband according to frame frequency, imaging the low-light-level long-waveband and the low-light-level short-waveband respectively to obtain an image frame sequence with alternated low-light-level long-waveband and low-light-level short-waveband, and storing a long-waveband frame and a short-waveband frame, performing image fusion on the low-light-level long-waveband and short-waveband images to obtain a dual-spectrum fusing image, and performing adjustment and color space mapping on the low-light-level long-waveband and short-waveband images and the dual-spectrum fusing image according to two-dimensional column diagram distribution and three primary color energy ratio of real colorful images of a scene in the day to obtain a wide-waveband large-span tuned filter single-channel colorful low-level-light night vision image. The wide-wave band large-span tuned filter single-channel colorful low-level-light night vision method and device obviously improve the image quality of the traditional night vision system, and increase the probability of detecting and identifying targets.