Wavelet threshold denoising method, device, equipment and medium

Abstract

This disclosure provides a wavelet threshold denoising method, comprising: acquiring a signal to be denoised and an ideal signal; initializing denoising parameters, and using the denoising parameters to denoise the signal to be denoised to obtain a first noise signal; calculating the mean square error between the signal to be denoised and the ideal signal as a first fitness; calculating the mean square error between the first noise signal and the ideal signal as a second fitness; comparing the first fitness and the second fitness, and if the second fitness is less than the first fitness, updating the denoising parameters, and using the updated denoising parameters to denoise the signal to be denoised to obtain a second noise signal; calculating the mean square error between the second noise signal and the ideal signal as a third fitness; comparing the third fitness and the second fitness, and if the third fitness is less than the second fitness, updating the denoising parameters for denoising and fitness calculation, iterating in this way until the minimum fitness is obtained; and using the denoising parameters corresponding to the minimum fitness to denoise the signal to be denoised.

Description

Technical Field

[0001] The present disclosure relates to the technical field of signal denoising, and in particular to a wavelet threshold denoising method, device, equipment and medium. Background Art

[0002] There are three main existing wavelet denoising methods: wavelet correlation denoising, wavelet modulus maximum denoising, and wavelet threshold denoising. Wavelet correlation denoising is suitable for high signal-to-noise ratio signals, with large computational complexity and the need to estimate the noise variance. Wavelet modulus maximum denoising is suitable for high signal-to-noise ratio signals containing many singular points, and the calculation speed is very slow during reconstruction. Wavelet threshold denoising is based on the fact that wavelet transform has a strong data correlation, so that the signal energy is concentrated in some large wavelet coefficients, while the noise energy is distributed in the entire wavelet domain, so the wavelet coefficients are small. Taking advantage of this feature, the threshold screening method is used to retain large signal coefficients and filter out small noise coefficients, thereby achieving the purpose of denoising. This method is applicable to a variety of different noise and signal situations, the noise is almost completely eliminated, and the characteristic peak points reflecting the initial signal are well retained, and it has a wide range of adaptability. However, this method is easily affected by five factors: wavelet basis function, decomposition and reconstruction level, threshold estimation criterion, threshold function form and threshold scaling method. The denoising effect depends mainly on the appropriateness of these parameters. Therefore, how to select these five parameters is the key to wavelet threshold denoising method.

[0003] In response to the above problems, many improved wavelet threshold denoising methods have emerged. Some of them only conduct a large number of research attempts on a certain type of specific signal, and then compare the denoising effects, summarize the parameter selection experience when using the wavelet threshold denoising method to process such signals and noise, or some only optimize and improve a certain major influencing factor such as the form of the threshold function to improve the adaptability of threshold selection, such as the wavelet threshold denoising method based on genetic algorithm, the wavelet threshold denoising method based on particle swarm optimization, and the wavelet denoising method based on bee colony algorithm and parameter threshold function. The above methods obviously have certain shortcomings, such as the single type of signal and noise leads to the empirical method obtained from the attempt is not universal, and the single optimization parameter causes the overall adaptability of the denoising method is still limited.

[0004] The five influencing factors of the above wavelet threshold denoising method all have a variety of different parameter selection types, but there is no universal rule to follow for selecting parameters in these influencing factors. For different signals and noises, it usually takes multiple attempts to determine the corresponding optimal parameters. However, the actual signals and noises in reality are mostly changing. In order to achieve a better denoising effect, these parameters need to be manually adjusted to adapt to signals and noises with different characteristics, which limits the adaptability of the wavelet threshold denoising algorithm. At the same time, when there are many parameter types, it is impossible to guarantee that the parameters adjusted manually are the best.

[0005] Public Content

[0006] Based on this, the present disclosure provides a wavelet threshold denoising method on the one hand, including: obtaining a signal to be denoised and an ideal signal; initializing denoising parameters, and denoising the signal to be denoised using the initialized denoising parameters to obtain a first noise signal; calculating the mean square error between the signal to be denoised and the ideal signal as a first fitness; calculating the mean square error between the first noise signal and the ideal signal as a second fitness; comparing the first fitness with the second fitness, if the second fitness is less than the first fitness, updating the denoising parameters, and denoising the signal to be denoised using the updated denoising parameters to obtain a second noise signal; calculating the mean square error between the second noise signal and the ideal signal as a third fitness; comparing the third fitness with the second fitness, if the third fitness is less than the second fitness, continuing to update the denoising parameters for denoising and fitness calculation, and iterating until a minimum fitness is obtained; and denoising the signal to be denoised using the denoising parameters corresponding to the minimum fitness.

[0007] According to an embodiment of the present disclosure, the updating of denoising parameters includes: updating the denoising parameters respectively in a global search mode or a local search mode.

[0008] According to an embodiment of the present disclosure, the denoising parameters include wavelet basis functions, decomposition and reconstruction layers, threshold estimation criteria, threshold function form and threshold scaling method; in a global search mode, according to:

[0009]

[0010] Update the denoising parameters, where Indicates the current value of the i-th parameter among the five parameters during the t-th iteration update. represents the global optimal value of the i-th parameter in the current iteration, and L is the search step length.

[0011] According to an embodiment of the present disclosure, the denoising parameters include wavelet basis functions, decomposition and reconstruction layers, threshold estimation criteria, threshold function form and threshold scaling method; in the local search mode, according to:

[0012]

[0013] Update the denoising parameters, where Indicates the current value of the i-th parameter among the five parameters during the t-th iteration update. and Indicates that any two local parameters of the ith parameter are different from The value of ε is a proportional coefficient with an interval of (0, 1) and uniform distribution.

[0014] According to an embodiment of the present disclosure, the denoising parameters include wavelet basis functions, decomposition and reconstruction layers, threshold estimation criteria, threshold function form and threshold scaling method; denoising the signal to be denoised using the denoising parameters includes: performing wavelet transform on the denoised signal using the wavelet basis functions; decomposing the signal and noise in the signal to be denoised into different scales according to the decomposition and reconstruction layers; determining the threshold for processing the wavelet coefficients at each scale according to the threshold estimation criteria and the threshold scaling method; setting, reducing or retaining the wavelet coefficients at each scale using the threshold function form; performing inverse wavelet transform on the processed wavelet coefficients at each scale using the wavelet basis functions and the decomposition and reconstruction layers to obtain the denoised signal.

[0015] According to an embodiment of the present disclosure, the method further includes: setting a conversion probability; and selecting a global search method or a local search method according to the conversion probability.

[0016] According to an embodiment of the present disclosure, the denoising parameters include wavelet basis functions, decomposition and reconstruction layers, threshold estimation criteria, threshold function form and threshold scaling method; the method also includes: establishing a correspondence between all optional types of the denoising parameters and the search range of the global search method or the local search method, wherein numbers are used to refer to the search range.

[0017] Another embodiment of the present disclosure provides a wavelet threshold denoising device, including: an acquisition module, used to acquire a signal to be denoised and an ideal signal; an initialization module, used to initialize denoising parameters, and denoise the signal to be denoised using the initialized denoising parameters to obtain a first noise signal; a calculation module, used to calculate the mean square error between the signal to be denoised and the ideal signal as a first fitness; and calculate the mean square error between the first noise signal and the ideal signal as a second fitness; and an update module, used to compare the first fitness and the second fitness. If the second fitness is less than the first fitness, , updating denoising parameters; a denoising module, used to denoise the signal to be denoised using the updated denoising parameters to obtain a second noise signal; the calculation module is also used to calculate the mean square error between the second noise signal and the ideal signal as a third fitness; the update module is also used to compare the third fitness with the second fitness, and if the third fitness is less than the second fitness, continue to update the denoising parameters; wherein, through the iteration of the calculation module, the update module and the denoising module, until the minimum fitness is obtained, the denoising parameters corresponding to the minimum fitness are used to denoise the signal to be denoised.

[0018] On the other hand, the present disclosure further provides an electronic device, including: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method.

[0019] Another aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the above-mentioned method. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0021] Figure 1 The flowchart of the improved wavelet threshold denoising method provided by an embodiment of the present disclosure is schematically shown.

[0022] Figure 2 A curve diagram showing the change of the fitness function with the number of iterations provided by an embodiment of the present disclosure is schematically shown.

[0023] Figure 3 The flowchart of parameter optimization provided by the embodiment of the present disclosure is schematically shown.

[0024] Figure 4 The ideal signal diagram corresponding to the radar signal, electrocardiogram signal and test signal provided by the embodiment of the present disclosure is schematically shown.

[0025] Figure 5 The radar signal and its noise waveform provided by the embodiment of the present disclosure are schematically shown.

[0026] Figure 6 The diagram schematically shows the result of removing three types of noise from radar signals using four wavelet denoising methods provided in an embodiment of the present disclosure.

[0027] Figure 7 The electrocardiogram signal and its noise waveform provided by the embodiment of the present disclosure are schematically shown.

[0028] Figure 8 The diagram schematically shows the results of removing three types of noise from electrocardiogram signals using four wavelet denoising methods provided in an embodiment of the present disclosure.

[0029] Fig. 9 The test signal and its noise waveform diagram provided by the embodiment of the present disclosure are schematically shown.

[0030] Fig.10 The diagram schematically shows the result of removing noise from a test signal using four wavelet denoising methods provided in an embodiment of the present disclosure.

[0031] Fig.11 The structural block diagram of the wavelet threshold denoising device provided by the embodiment of the present disclosure is schematically shown.

[0032] Fig.12 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present disclosure.

[0034] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0035] In the present disclosure, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or can communicate with each other; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present disclosure can be understood according to specific circumstances.

[0036] In the description of the present disclosure, it is necessary to understand that the terms "longitudinal", "length", "circumferential", "front", "rear", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the subsystem or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present disclosure.

[0037] Throughout the drawings, the same elements are represented by the same or similar reference numerals. Conventional structures or configurations will be omitted when they may cause confusion in the understanding of the present disclosure. The shapes, sizes, and positional relationships of the components in the drawings do not reflect the actual size, proportion, and actual positional relationship. In addition, in the claims, any reference symbol between brackets shall not be construed as a limitation to the claims.

[0038] Similarly, in order to simplify the present disclosure and help understand one or more of the various disclosed aspects, in the above description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. The description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0039] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the feature. In the description of the present disclosure, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0040] In view of the problems involved in the existing improved wavelet threshold denoising methods, such as the single processing object lacking universality, the single optimization parameter and the limited adaptability, the present invention proposes an improved wavelet threshold denoising method based on a multi-objective optimization algorithm. On the basis of the wavelet threshold denoising method, an intelligent optimization algorithm is used to determine that the parameters corresponding to the five influencing factors in the threshold denoising process are all the optimal parameter types, thereby ensuring that the wavelet threshold denoising effect can be optimized for different characteristic signals and noises. The method is not limited to a certain type of special signal and noise, not only optimizes a certain influencing factor, but also does not require manual adjustment of parameters. Multiple parameters can be adaptively adjusted simultaneously for a variety of different signals and noises, and finally the five optimal parameters of the wavelet threshold denoising method under the current signal and noise are determined.

[0041] The global optimal solution is solved by seeking the minimum value of the fitness function through multiple iterations, thereby determining the parameter type that makes the denoising effect best under the current signal and noise. Since the fitness function is certain and applicable to a variety of signals, this method has high universality. By setting the number of optimization targets in the initial stage of the algorithm, the algorithm can simultaneously control multiple target parameters during the optimization process, so as to efficiently perform multi-parameter combination optimization and further improve the overall adaptability of the wavelet threshold denoising algorithm. The following is a detailed introduction in conjunction with specific embodiments.

[0042] Figure 1 The flowchart of the improved wavelet threshold denoising method provided by an embodiment of the present disclosure is schematically shown.

[0043] like Figure 1 As shown, the signal processing method may include, for example, operations S101 to S106.

[0044] In operation S101, a signal to be denoised and an ideal signal are obtained.

[0045] According to an embodiment of the present disclosure, the signal to be denoised may include, for example, a radar echo signal, an ECG electrocardiogram signal, a test signal, and the like.

[0046] In operation S102, the denoising parameters are initialized.

[0047] According to an embodiment of the present disclosure, denoising parameters may include wavelet basis functions, decomposition and reconstruction layers, threshold estimation criteria, threshold function form, and threshold scaling method.

[0048] In operation S103, the signal to be denoised is denoised using the initialized denoising parameters to obtain a first noise signal.

[0049] According to an embodiment of the present disclosure, the denoising process may include: performing wavelet transform on the denoised signal using wavelet basis functions. Decomposing the signal and noise in the denoised signal into different scales according to the number of decomposition and reconstruction layers. Determining the threshold for processing the wavelet coefficients at each scale according to the threshold estimation criterion and the threshold scaling method. Using the threshold function form, setting, reducing or retaining the wavelet coefficients at each scale. Performing inverse wavelet transform on the processed wavelet coefficients at each scale using the wavelet basis functions and the number of decomposition and reconstruction layers to obtain the denoised signal.

[0050] Operation S104: Calculate a mean square error between the signal to be denoised and the ideal signal as a first fitness, and calculate a mean square error between the first noise signal and the ideal signal as a second fitness.

[0051] According to the embodiment of the present disclosure, the mean square error between the denoised signal and the ideal signal is used as the fitness function of the denoising parameter optimization algorithm. The selection of the fitness function is very important. It is not only an evaluation index for judging the effectiveness of signal denoising, but also a criterion for judging whether the parameter selection result is appropriate. The embodiment of the present disclosure uses the mean square error (MSE), which is a measure of the degree of difference between the estimator and the estimated quantity, and the formula is as follows:

[0052]

[0053] Where x(m) is the ideal signal, is the denoised signal.

[0054] Figure 2A curve diagram showing the change of the fitness function with the number of iterations provided by an embodiment of the present disclosure is schematically shown.

[0055] like Figure 2 As shown in the figure, through multiple iterations, the MSE gradually decreases. When it reaches the minimum value, the corresponding global optimal solution is the optimal parameter type to be found.

[0056] In operation S105, the first fitness and the second fitness are compared, and if the second fitness is less than the first fitness, the denoising parameters are updated.

[0057] After updating the denoising parameters according to the embodiment of the present disclosure, return to operation S103, use the updated denoising parameters to denoise the signal to be denoised, obtain a second noise signal, and continue to repeat operations S103 to S105 for iterative calculation until the minimum fitness is obtained.

[0058] In operation S106, the signal to be denoised is denoised using the denoising parameter corresponding to the minimum fitness.

[0059] Specifically, the wavelet threshold denoising function is called, the optimal parameters selected by optimization are used as the input parameters of the function, the acquired signal to be denoised is used as the input signal, and wavelet threshold denoising is performed, and the output signal is the denoised signal.

[0060] According to an embodiment of the present disclosure, the updating of the denoising parameters may include, for example: updating the denoising parameters respectively in a global search mode or a local search mode.

[0061] Specifically, the embodiment of the present disclosure is mainly completed through global search and local search in the parameter optimization process. The two search links are determined by the value of the conversion probability parameter p and the random number rand, where rand obeys the (0, 1) uniform distribution. After multiple iterations, the search is continuously switched between global and local, and finally the global optimal solution is found.

[0062] Figure 3 The flowchart of parameter optimization provided by the embodiment of the present disclosure is schematically shown.

[0063] like Figure 3 As shown, the optimization process may include:

[0064] 1. Parameter setting: set the number of search points n, the number of loop iterations N and the conversion probability p.

[0065] 2. Initialization: Initialize the optimization scope, the solution Sol of all parameters, the fitness Fitness, the global optimal fitness Fmin and the global optimal solution Best.

[0066] 3. Global optimization, update the solution S of all parameters found so far.

[0067] 4. Calculate the new fitness Fnew according to the fitness function.

[0068] 5. Get the current fitness Fitness, and compare the new fitness Fnew with the current fitness Fitness. If Fnew < Fitness, then use Fnew to replace Fitness, and S to replace Sol, otherwise it remains unchanged;

[0069] 6. Get the current global optimal fitness Fmin, compare the new fitness Fnew with the current global optimal fitness Fmin, if Fnew<Fmin, then use Fnew to replace Fmin, and S to replace Best, otherwise it remains unchanged.

[0070] 7. When the loop iteration ends, the optimal value Best of the wavelet threshold denoising parameter is obtained, otherwise return to 3 until all loop iterations are completed.

[0071] In the global search mode, you can search based on:

[0072]

[0073] Update the denoising parameters, where Indicates the current value of the i-th parameter among the five parameters during the t-th iteration update. represents the global optimal value of the i-th parameter in the current iteration, L is the search step length, and it obeys the Lévy distribution. The Lévy distribution formula is as follows:

[0074]

[0075] Here Γ(λ) is the standard gamma function, which is valid for step sizes s>0, where λ is usually set to 1.5.

[0076] In local search mode, you can search based on:

[0077]

[0078] Update the denoising parameters, where Indicates the current value of the i-th parameter among the five parameters during the t-th iteration update. and Indicates that any two local parameters of the ith parameter are different from The value of , ε is a proportional coefficient with an interval of (0, 1) and obeys a uniform distribution.

[0079] According to the embodiments of the present disclosure, since only numbers can be processed in the actual parameter optimization process, it is necessary to set a "reference table" to use specific numbers to refer to parameter types, thereby combining the optimization algorithm with wavelet threshold denoising.

[0080] Specifically, the least common multiple of all optional types of the five parameters is used as the maximum range of the algorithm search. There are 60 selection types for wavelet basis functions, 10 selection types for decomposition and reconstruction layers, 2 selection types for threshold function forms, 4 selection types for threshold estimation criteria, and 3 selection types for threshold scaling methods. Therefore, the least common multiple is 60, which means that the algorithm search range is 1-60. Secondly, these numbers are "assigned" to each parameter type in order from small to large based on the principle of equal probability, so that all optional types of the five target parameters can be matched one by one with the algorithm search range, and the "comparison table" is completed.

[0081] The optional type ranges of the five parameters of wavelet threshold denoising and their corresponding algorithm search ranges are shown in Table 1 below:

[0082]

[0083]

[0084] According to the content in Table 1 above, each natural number uniquely refers to a certain optional parameter type, so the global optimal numerical solution finally given by the algorithm can be compared with the above table to determine the best parameter type solution among all parameter types.

[0085] The improved wavelet threshold denoising method provided by the embodiment of the present disclosure continuously iterates and updates the denoising parameters, and uses the mean square error between the signal denoised by the denoising parameters and the ideal signal as the fitness to measure whether the current denoising parameters are the optimal denoising parameters, thereby adaptively finding the optimal denoising parameters to denoise the denoised signal, thereby improving the accuracy of denoising.

[0086] In order to verify the advantages of the improved wavelet threshold denoising provided by the embodiments of the present disclosure, three specific examples are further described below.

[0087] Example 1

[0088] This example performs denoising on three common additive noises in radar echo signals, namely, environmental noise (Gaussian white noise WGN), clutter noise (Gaussian colored noise CGN), and non-stationary noise (non-stationary Gaussian noise), and compares the denoising results. Finally, the denoising performance of the wavelet correlation denoising method, the wavelet modulus maximum denoising method, the wavelet threshold denoising method, and the wavelet threshold denoising method provided in the embodiments of the present disclosure are analyzed by combining the two evaluation indicators of SNR and MSE.

[0089] Figure 4 The ideal signal diagram corresponding to the radar signal, electrocardiogram signal and test signal provided by the embodiment of the present disclosure is schematically shown.

[0090] The simulated waveform of the radar signal, such as Figure 4 As shown in the first figure of a, the formula is as follows:

[0091]

[0092] Figure 5 The radar signal and its noise waveform provided by the embodiment of the present disclosure are schematically shown.

[0093] In the case of environmental noise interference in the radar system, this embodiment uses Gaussian white noise WGN for simulation, whose probability density function obeys Gaussian distribution, and power spectrum density function obeys uniform distribution. The waveform is as follows Figure 5 As shown in the first row of figures. For the case where the radar echo is mixed with clutter, this embodiment uses Gaussian colored noise CGN to simulate it. Its probability density function obeys Gaussian distribution, and its power spectrum density function obeys non-uniform distribution. The waveform is as follows Figure 5 As shown in the second row of figures. Due to some objective factors, the signal strength suddenly changes. This embodiment uses non-stationary Gaussian noise for simulation. Its probability density function obeys Gaussian distribution, and the power spectrum density function is a step function. The waveform is as follows Figure 5 The third row is shown in the figure.

[0094] Figure 6 The diagram schematically shows the result of removing three types of noise from radar signals using four wavelet denoising methods provided in an embodiment of the present disclosure.

[0095] like Figure 6 As shown, firstly, subjective analysis and judgment are performed through human eye observation. Figure 6 The three waveforms in the first row are the effects of removing three types of noise using the wavelet correlation method. It can be seen from the figure that the denoised signal has many obvious "burrs" and is relatively rough, and the denoising effect is not good; Figure 6 The three waveforms in the second row are the effects of removing three types of noise using the wavelet modulus maximum method. It can be seen from the figure that the denoised signal is improved compared with the signal in the previous row, but there are still some "burrs" and it is slightly rough, and the denoising effect is average; Figure 6 The three waveforms in the third row are the effects of removing three types of noise using the wavelet threshold method. It can be seen from the figure that the denoised signal is significantly improved compared with the signals in the previous two rows. The signal is relatively smooth without "burrs", but some details are slightly deformed and distorted, and the denoising effect is good. Figure 6The three waveforms in the 4th row are the effects of the improved wavelet threshold method after removing the three types of noise. It can be seen from the figure that the denoised signal is cleaner and smoother than the signal in the previous row, the deformation in the details is reduced, and only the intensity of some peaks and valleys is slightly fluctuating compared with the ideal signal. The denoising effect is the best among these four methods.

[0096] In order to further verify that the denoising effect of the present disclosure is the best, the following objective analysis and judgment is made by comparing the signal-to-noise ratio (SNR) and mean square error (MSE) of the denoising signal after various methods are denoised. According to the principle that the larger the SNR and the smaller the MSE, the better the denoising effect, the denoising effects of various methods are compared, as shown in Table 2:

[0097]

[0098]

[0099] The index data in the table further illustrate that, for the three types of additive noise in the radar signal, the denoising effect of the improved wavelet threshold denoising method proposed in the present disclosure is significantly better than that of the other three traditional wavelet denoising methods.

[0100] Example 2:

[0101] This embodiment performs denoising on three common additive noises in ECG electrocardiogram signals, namely environmental noise (Gaussian white noise WGN), baseline drift noise (PLN) and electromyographic interference noise (EMG), and compares the denoising results. Finally, the denoising performance of wavelet correlation denoising method, wavelet modulus maximum denoising method, wavelet threshold denoising method and improved wavelet threshold denoising method are analyzed by combining the two evaluation indicators of SNR and MSE.

[0102] The simulated waveform of the ECG signal is generated based on the Fourier series principle and combined with the measured ECG signal characteristics, such as Figure 4 As shown in the second picture.

[0103] Figure 7 The electrocardiogram signal and its noise waveform provided by the embodiment of the present disclosure are schematically shown.

[0104] In the case where there is environmental noise interference in the ECG signal, this embodiment uses Gaussian white noise WGN for simulation, whose probability density function obeys Gaussian distribution, and power spectrum density function obeys uniform distribution. The waveform is as follows Figure 7 As shown in the first row of figures; for the case of ECG baseline drift, this embodiment uses a sine function to simulate it, the formula is as follows, the waveform is as follows Figure 7 As shown in the second row of figures;

[0105] N PLN =200sin(0.002πt) t=0, 1, 2...1024

[0106] For the case where ECG is interfered by electromyography, this embodiment uses uniform noise for simulation, and its probability density function obeys uniform distribution, and the waveform is as follows: Figure 7 The third row is shown in the figure.

[0107] Figure 8 The diagram schematically shows the results of removing three types of noise from electrocardiogram signals using four wavelet denoising methods provided in an embodiment of the present disclosure.

[0108] The results of four wavelet denoising methods for removing the above three types of noise respectively.

[0109] like Figure 8 As shown, firstly, subjective analysis and judgment are performed through human eye observation. Figure 8 The three waveforms in the first row are the effects of removing three types of noise using the wavelet correlation method. It can be seen from the figure that the denoised signal has many obvious "burrs" and is relatively rough, and the denoising effect is not good; Figure 8 The three waveforms in the second row are the effects of removing three types of noise using the wavelet modulus maximum method. It can be seen from the figure that the signal after denoising is seriously distorted, especially the characteristic peaks are basically removed as noise, and the denoising effect is very poor; Figure 8 The three waveforms in the third row are the effects of the wavelet threshold method on the three types of noise removal. It can be seen from the figure that the denoised signal is significantly improved compared with the signals in the previous two rows. The signal is relatively smooth without obvious "burrs", but there is still obvious noise in some details, and the denoising effect is good. Figure 8 The three waveforms in the 4th row are the effects of the improved wavelet threshold method after removing the three types of noise. It can be seen from the figure that the denoised signal is closer to the ideal signal than the signal in the previous row, and the residual noise in the details is slightly reduced. The denoising effect is the best among these four methods.

[0110] In order to further verify that the denoising effect of the present disclosure is the best, the following objective analysis and judgment is made by comparing the signal-to-noise ratio (SNR) and mean square error (MSE) of the denoising signal after various methods are denoised. According to the principle that the larger the SNR and the smaller the MSE, the better the denoising effect, the denoising effects of various methods are compared, as shown in Table 3:

[0111]

[0112]

[0113] The index data in the table further illustrate that, for the three additive noises in the ECG signal, the denoising effect of the improved wavelet threshold denoising method proposed in the present disclosure is significantly better than that of the other three traditional wavelet denoising methods.

[0114] Example 3:

[0115] This embodiment performs denoising on the test signal and noise and compares the denoising results. Finally, the denoising performance of wavelet correlation denoising method, wavelet modulus maximum denoising method, wavelet threshold denoising method and improved wavelet threshold denoising method are analyzed by combining the two evaluation indicators of SNR and MSE.

[0116] The waveform of the test signal is obtained by directly importing the data fdata, such as Figure 4 As shown in the third picture.

[0117] Fig. 9 The test signal and its noise waveform diagram provided by the embodiment of the present disclosure are schematically shown.

[0118] like Fig. 9 The noise of the test signal is also obtained by directly importing the data fdata.

[0119] Fig.10 The diagram schematically shows the result of noise removal of a test signal using four wavelet denoising methods provided in an embodiment of the present disclosure.

[0120] like Fig.10 As shown, firstly, subjective analysis and judgment are performed through human eye observation. Fig.10 The waveform in the first row is the effect of removing the test noise by the wavelet correlation method. It can be seen from the figure that the denoised signal has many obvious "burrs" at the corners and is relatively rough overall, and the denoising effect is not good; Fig.10 The waveform in the second row is the effect of removing the test noise by the wavelet modulus maximum method. It can be seen from the figure that the denoised signal is improved compared with the signal in the previous row. The signal is relatively smooth without "burrs", but there are abnormal peak points at the corners, and the overall signal is slightly rough, and the denoising effect is general. Fig.10 The waveform in the third row is the effect of removing the test noise by the wavelet threshold method. It can be seen from the figure that the denoised signal is smoother than the signals in the previous two rows, but the abnormal peak points at the corners are more obvious, and the denoising effect is relatively general. Fig.10 The waveform in the fourth row is the effect diagram of the test noise removal by the improved wavelet threshold method. It can be seen from the figure that the denoised signal is cleaner and smoother than the above signals, and the abnormal peak at the corner is reduced. The denoising effect is the best among these four methods.

[0121] To further verify that the denoising effect of the present disclosure is the best, the following objective analysis and judgment is made by comparing the signal-to-noise ratio (SNR) and mean square error (MSE) of the denoising signal after various methods are denoised. According to the principle that the larger the SNR and the smaller the MSE, the better the denoising effect, the denoising effects of various methods are compared, as shown in Table 4:

[0122]

[0123] The indicator data in the table further illustrate that, for the test signal noise, the denoising effect of the improved wavelet threshold denoising method proposed in the present disclosure is significantly better than that of the other three traditional wavelet denoising methods.

[0124] In summary, the present disclosure proposes a wavelet threshold denoising method based on an improved intelligent optimization algorithm. This method adaptively and simultaneously optimizes and selects the five influencing parameters required in the wavelet threshold denoising process for a variety of real-life signals such as radar echo signals and ECG electrocardiogram signals, so that it can achieve the best denoising effect when facing different characteristic signals and noises, thereby verifying that the optimization algorithm maximizes the wavelet threshold denoising ability while having strong universality and adaptability. Therefore, there is reason to believe that the present disclosure has a very broad application prospect. For example, radar echo signal detection, physiological signal monitoring such as electrocardiogram and electroencephalogram, diffraction grating interference signal processing and power system harmonic detection all involve the interference of noise on useful signals, so the present disclosure can also be applied to these scientific research and engineering fields.

[0125] Based on the same inventive concept, the present disclosure also provides a wavelet threshold denoising device. Fig.11 The structural block diagram of the wavelet threshold denoising device provided by the embodiment of the present disclosure is schematically shown.

[0126] like Fig.11 As shown, the wavelet threshold denoising device 1100 may include, for example, an acquisition module 1110 , an initialization module 1120 , a calculation module 1130 , an update module 1140 and a denoising module 1150 .

[0127] The acquisition module 1110 is used to acquire the signal to be denoised and the ideal signal.

[0128] The initialization module 1120 is used to initialize denoising parameters, and use the initialized denoising parameters to denoise the signal to be denoised to obtain a first noise signal.

[0129] The calculation module 1130 is used to calculate the mean square error between the signal to be denoised and the ideal signal as the first fitness; and calculate the mean square error between the first noise signal and the ideal signal as the second fitness.

[0130] The updating module 1140 is used to compare the first fitness and the second fitness, and if the second fitness is smaller than the first fitness, update the denoising parameters.

[0131] The denoising module 1150 is configured to denoise the signal to be denoised using the updated denoising parameters to obtain a second noise signal.

[0132] The calculation module 1130 is further configured to calculate a mean square error between the second noise signal and the ideal signal as a third fitness.

[0133] The updating module 1140 is further configured to compare the third fitness with the second fitness, and if the third fitness is smaller than the second fitness, continue to update the denoising parameters.

[0134] The calculation module 1130, the updating module 1140 and the denoising module 1150 are iterated until the minimum fitness is obtained, and the denoising parameter corresponding to the minimum fitness is used to denoise the signal to be denoised.

[0135] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0136] For example, any multiple of the acquisition module 1110, the initialization module 1120, the calculation module 1130, the update module 1140, and the denoising module 1150 can be combined in one module / unit / sub-unit for implementation, or any one of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the acquisition module 1110, the initialization module 1120, the calculation module 1130, the update module 1140 and the denoising module 1150 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the acquisition module 1110, the initialization module 1120, the calculation module 1130, the update module 1140 and the denoising module 1150 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0137] It should be noted that the signal processing device part in the embodiment of the present disclosure corresponds to the signal processing method part in the embodiment of the present disclosure, and their specific implementation details are also the same, which will not be repeated here.

[0138] Fig.12 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Fig.12 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0139] like Fig.12As shown, the electronic device 1200 according to an embodiment of the present disclosure includes a processor 1201, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1202 or a program loaded from a storage part 12012 into a random access memory (RAM) 1203. The processor 1201 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1201 may also include an onboard memory for caching purposes. The processor 1201 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0140] In RAM 1203, various programs and data required for the operation of electronic device 1200 are stored. Processor 1201, ROM 1202 and RAM 1203 are connected to each other via bus 1204. Processor 1201 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 1202 and / or RAM 1203. It should be noted that the program can also be stored in one or more memories other than ROM 1202 and RAM 1203. Processor 1201 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0141] According to an embodiment of the present disclosure, the electronic device 1200 may further include an input / output (I / O) interface 1205, which is also connected to the bus 1204. The electronic device 1200 may further include one or more of the following components connected to the I / O interface 1205: an input portion 1206 including a keyboard, a mouse, etc.; an output portion 1207 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1208 including a hard disk, etc.; and a communication portion 1209 including a network interface card such as a LAN card, a modem, etc. The communication portion 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to the I / O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1210 as needed, so that a computer program read therefrom is installed into the storage portion 1208 as needed.

[0142] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1209, and / or installed from the removable medium 1211. When the computer program is executed by the processor 1201, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0143] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0144] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0145] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 1202 and / or the RAM 1203 described above and / or one or more memories other than the ROM 1202 and the RAM 1203 .

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments and / or claims of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

Claims

1. A wavelet threshold denoising method, comprising: Obtain the signal to be denoised and the ideal signal; Initializing denoising parameters, and denoising the signal to be denoised using the initialized denoising parameters to obtain a first noise signal; Calculating a mean square error between the signal to be denoised and the ideal signal as a first fitness; Calculating a mean square error between the first noise signal and the ideal signal as a second fitness; Comparing the first fitness with the second fitness, if the second fitness is less than the first fitness, updating denoising parameters, and denoising the signal to be denoised using the updated denoising parameters to obtain a second noise signal; Calculating a mean square error between the second noise signal and the ideal signal as a third fitness; Comparing the third fitness with the second fitness, if the third fitness is less than the second fitness, continuing to update the denoising parameters to perform denoising and fitness calculation, and iterating until a minimum fitness is obtained; De-noising the signal to be de-noised using the de-noising parameter corresponding to the minimum fitness; Wherein, the updating of denoising parameters includes respectively updating the denoising parameters in a global search mode or a local search mode; the denoising parameters include wavelet basis functions, decomposition and reconstruction layers, threshold estimation criteria, threshold function form and threshold scaling mode; The method further comprises: A correspondence between all optional types of the denoising parameters and the search range of the global search method or the local search method is established, wherein the search range is indicated by a number.

2. The wavelet threshold denoising method according to claim 1, wherein: The denoising parameters include wavelet basis function, decomposition and reconstruction layer number, threshold estimation criterion, threshold function form and threshold scaling method; In global search mode, according to Update the denoising parameters, where Indicates Among the five parameters in the first iteration update The current value of the parameter. Indicates the first The global optimal value of the parameters, is the search step length.

3. The wavelet threshold denoising method according to claim 1, wherein: The denoising parameters include wavelet basis function, decomposition and reconstruction layer number, threshold estimation criterion, threshold function form and threshold scaling method; In the local search mode, according to Update the denoising parameters, where Indicates Among the five parameters in the first iteration update The current value of the parameter. and Indicates The iteration update Any two local parameters are different from The value of is a proportional coefficient with an interval of (0, 1) and uniform distribution.

4. The wavelet threshold denoising method according to claim 1, wherein: The denoising parameters include wavelet basis function, decomposition and reconstruction layer number, threshold estimation criterion, threshold function form and threshold scaling method; Denoising the signal to be denoised by using the denoising parameter includes: Performing wavelet transform on the denoised signal using wavelet basis function; Decomposing the signal and noise in the signal to be denoised into different scales according to the number of decomposition and reconstruction layers; According to the threshold estimation criterion and the threshold scaling method, the threshold of the wavelet coefficients at each scale is determined; The wavelet coefficients at each scale are set to zero, reduced or retained using the threshold function form; The wavelet basis function and the number of decomposition and reconstruction layers are used to perform inverse wavelet transform on the processed wavelet coefficients at each scale to obtain a denoised signal.

5. The wavelet threshold denoising method according to claim 1, further comprising: Set conversion probability; A global search method or a local search method is selected according to the conversion probability.

6. A wavelet threshold denoising device, comprising: An acquisition module, used to acquire the signal to be denoised and the ideal signal; An initialization module, used to initialize denoising parameters, and denoise the signal to be denoised using the initialized denoising parameters to obtain a first noise signal; A calculation module, used for calculating a mean square error between the signal to be denoised and the ideal signal as a first fitness; Calculating a mean square error between the first noise signal and the ideal signal as a second fitness; An updating module, used for comparing the first fitness and the second fitness, and updating a denoising parameter if the second fitness is less than the first fitness; A denoising module, configured to denoise the signal to be denoised using the updated denoising parameters to obtain a second noise signal; The calculation module is further used to calculate a mean square error between the second noise signal and the ideal signal as a third fitness; The updating module is further used to compare the third fitness and the second fitness, and if the third fitness is less than the second fitness, continue to update the denoising parameters; wherein, the calculation module, the update module and the denoising module are iterated until the minimum fitness is obtained, and the denoising parameter corresponding to the minimum fitness is used to denoise the signal to be denoised; The updating module is further used to update the denoising parameters respectively in a global search mode or a local search mode; the denoising parameters include wavelet basis function, decomposition and reconstruction layer number, threshold estimation criterion, threshold function form and threshold scaling mode; The updating module is further used to establish a correspondence between all optional types of the denoising parameters and the search range of the global search method or the local search method, wherein the search range is indicated by a number.

7. An electronic device comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 5.

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