CN110430340B - Method and system for reducing noise of pulse array signal - Google Patents
Method and system for reducing noise of pulse array signal Download PDFInfo
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Abstract
The invention discloses a noise reduction method of a pulse array signal, which comprises the following steps: modeling the distribution characteristics of the pulse array signals in the space neighborhood and the timestamp neighborhood of the pulse to be denoised, establishing a distribution characteristic model of the pulse array signals, and extracting the space-time distribution characteristics of the pulse array signals; constructing a space-time filter according to the noise distribution characteristics in the pulse array signals, and filtering the extracted features; and restoring the filtered pulse space-time distribution characteristics into pulse array signals according to the release characteristic model. The invention fully considers the space-time distribution characteristics of noise in the pulse array signal, models the transmissibility of the noise in the time domain and the randomness of the noise in the space domain, and constructs a space-time filter to efficiently denoise the pulse signal by combining the physical significance represented by the pulse, thereby having high efficiency and good noise reduction effect.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a method and a system for reducing noise of a pulse array signal.
Background
Conventional vision sensors typically sample a scene completely in units of frames at a preset fixed frequency. The sampling based on the fixed frame rate cannot reflect the dynamic change of a scene, and is easy to oversample or undersample a current scene, so that the problems of large video data redundancy, low time domain resolution, easy blurring under high-speed motion and the like are caused. The novel camera for collecting pulse array signals enters the visual field of people, and comprises a Sensor for sending pulse signals based on illumination intensity changes, such as a Dynamic Vision Sensor (DVS), an Asynchronous Time-based image Sensor (ATIS), a Dynamic Active Pixel Vision Sensor (Dynamic and Active Pixel Vision Sensor, DAVIS) and the like, and a Sensor for sending signals based on the accumulated intensity of illumination intensity, such as a light intensity accumulation Sensor and the like. The sensor of the camera collects information of optical signals in a certain area within a certain time, and has the advantages of high dynamic range, high time resolution and the like.
Noise is an important issue in the field of signal processing. The pulse array signals have extremely high time resolution, the time correlation among the signals is strong, and the noise influence has the characteristics of transmissibility in a time domain and randomness in a space domain. Different from the traditional image and signal denoising, the pulse array signal is denoised, the characteristics and the physical significance of the pulse signal need to be fully considered, and a large amount of pulse data is efficiently processed.
Disclosure of Invention
It is an object of the present disclosure to provide a new solution for noise reduction of pulse array signals. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to a first aspect of embodiments of the present invention, there is provided a method for reducing noise of a pulse array signal, including: modeling the distribution characteristics of the pulse array signals in the space neighborhood and the timestamp neighborhood of the pulse to be denoised, establishing a distribution characteristic model of the pulse array signals, and extracting the space-time distribution characteristics of the pulse array signals;
constructing a space-time filter according to the noise distribution characteristics in the pulse array signals, and filtering the extracted features;
and restoring the filtered pulse space-time distribution characteristics into pulse array signals according to the release characteristic model.
Furthermore, the pulse to be denoised is a specific pulse in a time-space domain, and is a segment of pulse signal from the emission of a certain impulse response to the appearance of the next impulse response on a certain pixel.
Further, the constructing a spatio-temporal filter according to the noise distribution characteristics in the pulse array signal, and filtering the extracted features includes:
setting pulse array signals of pulses to be denoised in a space neighborhood and a timestamp neighborhood as a first search area of the pulse array signals;
analyzing the pulse array signals in the first search area to obtain pulse issuing information of a plurality of pulse sequences, and obtaining each pulse representation characteristic or converting the pulse issuing information into a transform domain to extract characteristics according to the pulse issuing information;
converting a pulse array signal contained in the first search area into pulse intensity, calculating the similarity between a specific pulse and other pulses in the first search area in a pulse intensity domain, setting a first threshold, and reserving the pulses with the similarity larger than the first threshold in the first search area, and marking as a second search area;
setting a second threshold value for the second search area, and judging whether the pulse number in the second search area is greater than the second threshold value; if so, estimating the pulse to be denoised by using the pulse in the second search area, and replacing the original intensity with the estimated value; and if not, subtracting the second search area from the first search area to obtain a third search area, estimating the pulse to be denoised by using the pulse in the third search area, and replacing the original intensity with the estimated value.
Further, each pulse representation characteristic is obtained according to the pulse sending information, and the method comprises the following steps: the pulse strength is determined from the pulse firing interval, or from the inverse of the pulse firing interval, or from the logarithm of the pulse firing interval.
Further, the calculating the similarity between the specific pulse and other pulses in the first search area includes: and calculating the variance of the pulse intensity in the search area, determining a confidence interval according to the variance and the intensity of the pulse to be denoised, and determining the similarity according to the distribution condition of the pulse in the confidence interval.
Furthermore, the first threshold and the second threshold are dynamically set according to the neighborhood pulse relationship so as to adapt to different pulse data.
Further, the first threshold is different from the second threshold.
Further, the estimating the pulse to be denoised by using the pulse in the second search region includes but is not limited to: weighted average method, neighborhood filtering method.
Further, the estimation of the pulse to be denoised is performed by a filter, and the weight or convolution kernel of the space-time filter gives a larger value to the pulse closer to the pulse to be denoised.
Further, according to the release characteristic model, restoring the filtered pulse space-time distribution characteristics into a pulse array signal, including: and restoring the adjusted intensity value of the specific pulse into a pulse array signal according to the pulse time sequence relation, wherein the method is the inverse operation of the operation step of obtaining the representation characteristics of each pulse according to the pulse distribution information.
According to a second aspect of the embodiments of the present invention, there is provided a noise reduction system for a pulse array signal, including:
the modeling extraction module is used for modeling the distribution characteristics of the pulse array signals in the space neighborhood and the timestamp neighborhood of the pulse to be denoised, establishing a distribution characteristic model of the pulse array signals and extracting the space-time distribution characteristics of the pulse array signals;
the filtering module is used for constructing a space-time filter according to the noise distribution characteristics in the pulse array signals and filtering the extracted characteristics;
and the restoring module is used for restoring the filtered pulse space-time distribution characteristics into pulse array signals according to the distribution characteristic model.
According to a third aspect of the embodiments of the present invention, there is provided a noise reduction system for a pulse array signal, for implementing the above method, the system including: the system comprises a neighborhood searching module, a pulse analysis and conversion module, a similarity estimation module and a pulse denoising and recombination module which are connected in sequence;
the neighborhood searching module is used for searching neighborhood pulse array data of the target pulse to obtain a target searching area;
the pulse analysis and conversion module is used for analyzing pulse data to obtain pulse distribution information of each pulse sequence corresponding to each pixel search area and converting the pulse distribution information into pulse intensity;
the similarity estimation module is used for calculating the intensity similarity between each pulse and a target pulse in the search area;
the pulse denoising and recombining module is used for denoising the target pulse and recombining the denoised pulse data into a pulse array signal.
According to a fourth aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method described above.
According to a fifth aspect of embodiments of the present invention, there is provided a non-transitory computer readable storage medium having stored thereon a computer program, the program being executed by a processor to implement the above-mentioned method.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the method and the system for reducing the noise of the pulse array signals, the pulse array signals in a certain range in the space neighborhood and the timestamp neighborhood of a specific pulse to be denoised in the time-space domain are converted into pulse intensity information; calculating the similarity between the pulse to be denoised and the neighborhood pulse, and filtering the intensity of the pulse to be denoised by using the neighborhood pulse; recovering the adjusted intensity value of the specific pulse into a pulse array signal according to the pulse time sequence relation; the invention fully considers the space-time distribution characteristics of noise in the pulse array signal, models the transmissibility of the noise in the time domain and the randomness of the noise in the space domain, and constructs a space-time filter to efficiently denoise the pulse signal by combining the physical significance represented by the pulse, thereby having high efficiency and good noise reduction effect.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for reducing noise of a pulse array signal according to an embodiment of the disclosure;
FIG. 2 is a schematic diagram illustrating a method for reducing noise of a pulse array signal according to another embodiment of the present disclosure;
FIG. 3 is a schematic diagram of spatial-temporal neighborhood selection of a noise reduction method for a pulse array signal according to another embodiment of the present disclosure;
FIG. 4 is a diagram illustrating an example of relationship between pulse signals and pulse intensities in a method for reducing noise of a pulse array signal according to another embodiment of the present disclosure;
fig. 5 is a block diagram of a noise reduction system for a pulse array signal according to another embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As shown in fig. 1, an embodiment of the present disclosure provides a method for reducing noise of a pulse array signal, including:
step S1: modeling the distribution characteristics of the pulse array signals in the space neighborhood and the timestamp neighborhood of the pulse to be denoised, establishing a distribution characteristic model of the pulse array signals, and extracting the space-time distribution characteristics of the pulse array signals.
The pulse to be denoised is a specific pulse in a time-space domain; the specific pulse in the time-space domain is defined as a pulse signal from the emission of a certain impulse response to the appearance of the next impulse response at a certain pixel.
The space neighborhood and the time stamp neighborhood are in four neighborhoods, eight neighborhoods or other relations, and the time stamp neighborhood takes the pulse as a unit and can take a larger neighborhood radius relative to the space neighborhood.
Step S2: constructing a space-time filter according to the noise distribution characteristics in the pulse array signals, and filtering the extracted features; step S2 specifically includes:
step S21: setting a pulse array signal in a certain range in a space neighborhood and a timestamp neighborhood as a first search area of a specific pulse to be denoised in a time-space domain; the spatial neighborhood may be a square with a smaller size, such as 2 x 2 or 4 x 4, or a circle with a radius of 1.5 or 2, or a larger neighborhood; the time stamp neighborhood is generally larger than the space neighborhood range so as to adapt to the high time resolution of the pulse array signal;
step S22: analyzing the pulse array signals in the first search area to obtain pulse issuing information of a plurality of pulse sequences, and obtaining each pulse representation characteristic or converting the pulse issuing information into a transform domain to extract characteristics according to the pulse issuing information;
the method for obtaining each pulse representation characteristic according to the pulse sending information may be: determining the pulse intensity according to the pulse emitting time interval, or determining the pulse intensity according to the reciprocal of the pulse emitting time interval, or determining the pulse intensity according to the logarithm of the pulse emitting time interval; the time stamp neighborhood takes the pulse as a unit, and can take a larger neighborhood radius relative to the space neighborhood;
step S23: for a first search area, converting a pulse array signal contained in the first search area into pulse intensity, calculating the similarity between a specific pulse and other pulses in the first search area in the pulse intensity domain, setting a first threshold, and keeping the pulse with the similarity larger than the first threshold in the first search area as a second search area;
step S24: setting a second threshold value for the second search area, and judging whether the pulse number in the second search area is greater than the second threshold value;
if the number of the pulses in the second search area is larger than a second threshold value, estimating the pulses to be denoised by using the pulses in the second search area, and replacing the original intensity of the pulses with the estimated value;
and if the pulse number in the second search area is smaller than a second threshold value, subtracting the second search area from the first search area to obtain a third search area, estimating the pulse to be denoised by using the pulse in the third search area, and replacing the original intensity of the pulse to be denoised by using the estimated value.
The calculating the similarity between the specific pulse and other pulses in the first search area comprises the following steps: and calculating the variance of the pulse intensity in the search area, determining a confidence interval according to the variance and the intensity of the pulse to be denoised, and determining the similarity according to the distribution condition of the pulse in the confidence interval.
The first threshold and the second threshold are dynamically set according to the neighborhood pulse relationship so as to adapt to different pulse data. The first threshold is different from the second threshold.
The pulse to be denoised is estimated by using the pulse in the second search region, and the estimation method includes but is not limited to a weighted average method, a neighborhood filtering method and the like.
The pulse to be denoised is estimated through a filter, and the weight or convolution kernel of the filter gives a larger value to the pulse closer to the pulse to be denoised.
Step S3: restoring the filtered pulse space-time distribution characteristics into pulse array signals according to the release characteristic model of the pulse array signals established in the step S1; namely: and restoring the adjusted intensity value of the specific pulse into a pulse array signal according to the pulse time sequence relation. The method for restoring the adjusted intensity value of the specific pulse to the pulse array signal according to the pulse timing relationship is the inverse operation of the operation step of obtaining the representation characteristics of each pulse according to the pulse distribution information in step S22.
A system for noise reduction of a pulse array signal, comprising:
the modeling extraction module is used for modeling the distribution characteristics of the pulse array signals in the space neighborhood and the timestamp neighborhood of the pulse to be denoised, establishing a distribution characteristic model of the pulse array signals and extracting the space-time distribution characteristics of the pulse array signals;
the filtering module is used for constructing a space-time filter according to the noise distribution characteristics in the pulse array signals and filtering the extracted characteristics;
and the restoring module is used for restoring the filtered pulse space-time distribution characteristics into pulse array signals according to the distribution characteristic model.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the noise reduction method described above.
A non-transitory computer readable storage medium having stored thereon a computer program which is executed by a processor to implement the noise reduction method described above.
According to an embodiment of the present application, a method for reducing noise of a pulse array signal is provided, as shown in fig. 2, including:
according to the pulse position to be denoised, the pulse array signals in a certain range on the spatial neighborhood and the timestamp neighborhood are used as a first search area, and as shown in fig. 3, eight neighborhoods or a circle with the radius of 1.5 are selected as a spatial neighborhood SpatialRange; assuming that the corresponding time interval of the pulse to be denoised is t, selecting 5 x t as a timestamp neighborhood TimeRange; a first search area is determined.
Converting the pulse array signal in the first search area to pulse intensity, as shown in fig. 4;
calculating the sum and the square sum of all pulse intensities in the first search area, and calculating a mean value;
calculating the integral variance and standard deviation of the pulse in the first search area;
assuming that the number of pulses in the first search region is M, the standard deviation σ of the pulse intensity information, and the scaling factor k, the first threshold T1 is set to k × σ. And assuming that the intensity of the pulse to be denoised is P, calculating an upper limit and a lower limit of a confidence interval according to the average value, and setting the confidence interval as [ P-k sigma, P + k sigma ].
And traversing the pulses in the first search area again, and counting the number of pulses in the confidence interval.
Setting a second threshold value T2 to be M/10, if the number of pulses meeting the condition is less than T2, excluding the current pulse and all pulses in the confidence interval, and taking the average value of the intensities of other pulses in the first search area S1 as a denoised result P':
where num (S) is the number of pulses in region S. a ∩ b represents the overlap of region a and region b.
Indicating regions that do not belong to S.
If the number of pulses meeting the condition is greater than T2, taking all pulses meeting the condition in the first search area, marking as a second search area, and calculating the average intensity value of the pulses in the second search area S2 as the denoising result P' of the target pulse:
it should be noted that the way of solving the final result can also be better in some cases by setting non-uniform weights according to the distance from the target pulse.
After the current pulse denoising is finished, denoising the pulse at the next position by using the method until all positions in the pulse array signal are traversed.
As shown in fig. 5, a system for reducing noise of a pulse array signal is used to implement the noise reduction method according to this embodiment, and the system includes: the system comprises a neighborhood searching module, a pulse analysis and conversion module, a similarity estimation module and a pulse denoising and recombination module which are connected in sequence;
the neighborhood searching module is used for searching neighborhood pulse array data of the target pulse to obtain a target searching area;
the pulse analysis and conversion module is used for analyzing pulse data to obtain pulse distribution information of each pulse sequence corresponding to each pixel search area and converting the pulse distribution information into pulse intensity;
the similarity estimation module is used for calculating the intensity similarity between each pulse and a target pulse in the search area;
the pulse denoising and recombining module is used for denoising the target pulse and recombining the denoised pulse data into a pulse array signal.
The term "module" is not intended to be limited to a particular physical form. Depending on the particular application, a module may be implemented as hardware, firmware, software, and/or combinations thereof. Furthermore, different modules may share common components or even be implemented by the same component. There may or may not be clear boundaries between the various modules.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the creation apparatus of a virtual machine according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
The above-mentioned embodiments only express the embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (12)
1. A method for reducing noise of a pulse array signal, comprising:
modeling the distribution characteristics of the pulse array signals in the space neighborhood and the timestamp neighborhood of the pulse to be denoised, establishing a distribution characteristic model of the pulse array signals, and extracting the space-time distribution characteristics of the pulse array signals;
constructing a space-time filter according to the noise distribution characteristics in the pulse array signals, and filtering the extracted features;
restoring the filtered pulse space-time distribution characteristics into pulse array signals according to the distribution characteristic model;
the method for constructing a space-time filter according to the noise distribution characteristics in the pulse array signal and filtering the extracted features comprises the following steps:
setting pulse array signals of pulses to be denoised in a space neighborhood and a timestamp neighborhood as a first search area of the pulse array signals;
analyzing the pulse array signals in the first search area to obtain pulse issuing information of a plurality of pulse sequences, and obtaining each pulse representation characteristic or converting the pulse issuing information into a transform domain to extract characteristics according to the pulse issuing information;
converting a pulse array signal contained in the first search area into pulse intensity, calculating the similarity between a pulse to be denoised and other pulses in the first search area in the pulse intensity domain, setting a first threshold value, reserving the pulses with the similarity larger than the first threshold value in the first search area, and marking as a second search area;
setting a second threshold value for the second search area, and judging whether the pulse number in the second search area is greater than the second threshold value; if so, estimating the pulse to be denoised by using the pulse in the second search area, and replacing the original intensity with the estimated value; if not, subtracting the second search area from the first search area to obtain a third search area, estimating the pulse to be denoised by using the pulse in the third search area, and replacing the original intensity with the estimated value;
according to the release characteristic model, restoring the filtered pulse space-time distribution characteristics into a pulse array signal, which comprises the following steps: and restoring the adjusted intensity value of the pulse to be denoised into a pulse array signal according to the pulse time sequence relation.
2. The method of claim 1, wherein the pulse to be denoised is a specific pulse in the time-space domain, and is a pulse signal from the emission of one impulse response to the occurrence of the next impulse response at a specific pixel.
3. The method of claim 1, wherein obtaining the respective pulse representation characteristics from the pulse delivery information comprises: the pulse strength is determined from the pulse firing interval, or from the inverse of the pulse firing interval, or from the logarithm of the pulse firing interval.
4. The method of claim 1, wherein the calculating the similarity between the pulse to be denoised and other pulses in the first search region comprises: and calculating the variance of the pulse intensity in the search area, determining a confidence interval according to the variance and the intensity of the pulse to be denoised, and determining the similarity according to the distribution condition of the pulse in the confidence interval.
5. The method of claim 1, wherein the first threshold and the second threshold are dynamically set according to a neighborhood pulse relationship to accommodate different pulse data.
6. The method of claim 1, wherein the first threshold is different from the second threshold.
7. The method of claim 1, wherein estimating the pulse to be denoised using the pulse in the second search region comprises but is not limited to: weighted average method, neighborhood filtering method.
8. The method of claim 1, wherein estimating the pulse to be denoised is estimating by a filter whose weight or convolution kernel assigns a larger value to pulses closer to the pulse to be denoised.
9. A system for noise reduction of a pulse array signal, comprising:
the modeling extraction module is used for modeling the distribution characteristics of the pulse array signals in the space neighborhood and the timestamp neighborhood of the pulse to be denoised, establishing a distribution characteristic model of the pulse array signals and extracting the space-time distribution characteristics of the pulse array signals;
the filtering module is used for constructing a space-time filter according to the noise distribution characteristics in the pulse array signals and filtering the extracted characteristics;
the restoring module is used for restoring the filtered pulse space-time distribution characteristics into pulse array signals according to the distribution characteristic model;
the filtering module comprises:
the device comprises a first unit, a second unit and a third unit, wherein the first unit is used for setting pulse array signals of pulses to be denoised in a space neighborhood and a timestamp neighborhood as a first search area of the pulse array signals;
the second unit is used for analyzing the pulse array signals in the first search area to obtain pulse distribution information of a plurality of pulse sequences, and obtaining each pulse representation feature or converting the pulse distribution information into a transform domain extraction feature according to the pulse distribution information;
the third unit is used for converting the pulse array signals contained in the first search area into pulse intensity, calculating the similarity between the pulse to be denoised and other pulses in the first search area in the pulse intensity domain, setting a first threshold value, reserving the pulse with the similarity larger than the first threshold value in the first search area, and marking as a second search area;
a fourth unit, configured to set a second threshold for the second search area, and determine whether the number of pulses in the second search area is greater than the second threshold; if so, estimating the pulse to be denoised by using the pulse in the second search area, and replacing the original intensity with the estimated value; if not, subtracting the second search area from the first search area to obtain a third search area, estimating the pulse to be denoised by using the pulse in the third search area, and replacing the original intensity with the estimated value;
the reduction module comprises: and the unit is used for recovering the adjusted intensity value of the pulse to be denoised into a pulse array signal according to the pulse time sequence relation.
10. A system for noise reduction of a pulse array signal, for implementing the method of any one of claims 1-8, the system comprising: the system comprises a neighborhood searching module, a pulse analysis and conversion module, a similarity estimation module and a pulse denoising and recombination module which are connected in sequence;
the neighborhood searching module is used for searching neighborhood pulse array data of the target pulse to obtain a target searching area;
the pulse analysis and conversion module is used for analyzing pulse data to obtain pulse distribution information of each pulse sequence corresponding to each pixel search area and converting the pulse distribution information into pulse intensity;
the similarity estimation module is used for calculating the intensity similarity between each pulse and a target pulse in the search area;
the pulse denoising and recombining module is used for denoising the target pulse and recombining the denoised pulse data into a pulse array signal.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of any one of claims 1-8.
12. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor to implement the method according to any one of claims 1-8.
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| CN107004255A (en) * | 2014-09-23 | 2017-08-01 | 英特尔公司 | The non-local mean image denoising retained with details of mixing is driven using self-similarity |
| CN107147856A (en) * | 2017-03-30 | 2017-09-08 | 深圳大学 | A kind of pixel cell and its denoising method, dynamic visual sensor, imaging device |
| CN109068024A (en) * | 2018-06-29 | 2018-12-21 | 北京大学 | The method that a kind of pair of space-time signal is filtered |
| CN109101884A (en) * | 2018-07-10 | 2018-12-28 | 北京大学 | A kind of pulse array prediction technique |
| CN109902594A (en) * | 2019-01-31 | 2019-06-18 | 国网江苏省电力有限公司泰州供电分公司 | A kind of pulse signal denoising method based on small echo |
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| CN107004255A (en) * | 2014-09-23 | 2017-08-01 | 英特尔公司 | The non-local mean image denoising retained with details of mixing is driven using self-similarity |
| CN107147856A (en) * | 2017-03-30 | 2017-09-08 | 深圳大学 | A kind of pixel cell and its denoising method, dynamic visual sensor, imaging device |
| CN109068024A (en) * | 2018-06-29 | 2018-12-21 | 北京大学 | The method that a kind of pair of space-time signal is filtered |
| CN109101884A (en) * | 2018-07-10 | 2018-12-28 | 北京大学 | A kind of pulse array prediction technique |
| CN109902594A (en) * | 2019-01-31 | 2019-06-18 | 国网江苏省电力有限公司泰州供电分公司 | A kind of pulse signal denoising method based on small echo |
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