CN115097484A - double-Gamma estimation-based single photon laser radar fog-penetration imaging method - Google Patents
double-Gamma estimation-based single photon laser radar fog-penetration imaging method Download PDFInfo
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Abstract
The invention relates to a single photon laser radar fog-penetrating imaging method based on double Gamma estimation. The invention relates to the technical field of daytime outdoor foggy day imaging. The invention compensates the pile-up effect generated by non-signal photons by using an observation model based on polynomial distribution, and eliminates the non-signal photons by adopting two times of Gamma estimation, thereby finally separating the signal photons under the extremely low SBR condition from the non-signal photons (scattering and noise photons). According to the method, the first Gamma fitting is carried out on the histogram detected by each pixel, so that the correction of the histogram data is realized; compensating the accumulation effect generated by non-signal photons by adopting an observation model based on polynomial distribution to realize the calculation of echo photons; and separating signal photons from non-signal photons by adopting second-time Gamma fitting, and reconstructing a target depth image based on the signal photons separated from each pixel.
Description
Technical Field
The invention relates to the technical field of outdoor fog-penetrating imaging of a single-photon laser radar in daytime, and discloses a single-photon laser radar fog-penetrating imaging method based on double Gamma estimation.
Background
In the outdoor remote detection process, the three-dimensional imaging result of the photon counting radar is often limited by environmental conditions, especially cloud and fog in the natural environment. The cloud has strong absorption and scattering capabilities for the laser, resulting in significant attenuation of the laser energy over relatively short transmission distances. Moreover, the original propagation direction of light is easily changed due to multiple scattering of photons when the photons are transmitted in the cloud and mist. Scattering of the cloud and interference of background light cause the signal to background ratio (SBR) of the target to be very low. These factors ultimately lead to a reduction in the effective imaging distance, increased imaging degradation, and reduced imaging resolution of the radar system. How to overcome the scattering influence of cloud and fog and improve the imaging quality in the cloud and fog environment is still an important scientific problem to be solved urgently by photon counting radars. Therefore, developing an algorithm for separating target signals from non-signal photons (scattering and noise photons) meets the requirements of outdoor low visibility remote target depth image detection and short-time imaging, lays a foundation for target detection and identification of novel applications such as laser radar unmanned driving and unmanned aerial vehicles, and the like, and is very necessary.
The prior scheme is introduced:
conventional Peak method (Peak selection algorithm, PSA): and selecting a peak point of the signal in each pixel point as a distance position of the target.
Single Parameter Estimation Algorithm (SPEA): and reconstructing a depth image by adopting a single parameter estimation construction algorithm (SPEA) according to the actually measured fog attenuation coefficient and the Gamma distribution model of the smog.
Full parameter estimation (alem estimation algorithm, APEA): and (3) approximately considering the target signal as noise, and directly estimating the photon distribution histogram by adopting maximum likelihood estimation so as to reconstruct the depth image.
Double-parameter estimation (Dual-parameter estimation algorithm): and obtaining a scale parameter of a Gamma distribution function according to part of smoke signals estimated by continuous wavelet transform, and then obtaining a shape parameter by utilizing maximum likelihood estimation.
The problems of the existing scheme are as follows:
the researches are only developed aiming at indoor artificial smoke, and the influence of background photons on an algorithm reconstruction result is not considered;
compared with the actual outdoor remote target detection scene, the simulated smoke is only distributed in a local area within the detection range and is not accordant with the actual detection scene;
simulated smoke differs from the particle characteristics of actual fog, and it is not sufficient to verify the effectiveness of the algorithm using only laboratory experiments.
Disclosure of Invention
In view of the above drawbacks or needs for improvement in the prior art, the present invention provides an outdoor foggy day imaging algorithm suitable for an area array photon counting radar, which can separate signal photons from non-signal photons (scattered and noise photons) under extremely low SBR conditions. The present invention compensates for the pile-up effect of non-signal photon generation by using an observation model based on polynomial distribution and employs two Gamma estimations to eliminate non-signal photons.
The invention provides a single photon laser radar fog-penetrating imaging method based on double Gamma estimation, which provides the following technical scheme:
a single photon laser radar fog-penetration imaging method based on double Gamma estimation comprises the following steps:
step 1: performing first Gamma fitting on the histogram detected by each pixel to realize the correction of the histogram data;
step 2: compensating the accumulation effect generated by non-signal photons by adopting an observation model based on polynomial distribution to realize the calculation of echo photons;
and step 3: and separating signal photons from non-signal photons by adopting second-time Gamma fitting, and reconstructing a target depth image based on the signal photons separated from each pixel.
Preferably, the step 1 specifically comprises:
photons detected by the photon counting radar in a foggy environment mainly comprise smoke scattered photons lambda fog Target reflected photon λ target Background photon λ b And system noise, each photon count being from a poisson distribution P (.) distribution, represented by:
h t P[λ(t)]＝P[λ target (t)+λ fog (t)+λ b +b d ] (1)
λ target (t)＝ηαf(t-2R/c) (2)
h is observed histogram distribution, lambda is average photon number, eta represents quantum efficiency of detector, alpha represents reflection photon number of target, and is calculated by laser radar equation, f represents system pulse response function, R represents target distance value, c is light speed, b is light speed d Represents a dark count of the detector;
the photons are scattered for multiple times when passing through the scattering medium, the scattered photons satisfy Gamma distribution on the time domain, and the time domain distribution of the scattered photons is represented by the following formula:
k is the maximum scattering frequency, r is related to the backscattering coefficient of smoke, and beta is the average scattering frequency reaching the focal plane of the detector within each time Bin;
and (3) modeling the observed histogram by adopting polynomial distribution, wherein the probability P (h | lambda) of a single pixel histogram h observed in the daytime outdoor foggy environment meets the relation:
wherein, T is the maximum Bin number, N is the total pulse number, and the likelihood function is solved for the above formula:
and solving through maximum likelihood to obtain the number of photons of the ith time Bin as:
wherein, λ is the echo photon rate function under the condition of single pulse, and the target echo photon under the condition of single pulse is λ target ＝λ-λ fog -λ b -b d 。
Preferably, pixel-by-pixel denoising pretreatment is performed on histogram data h1, histogram distribution after Poisson triggering is Gamma distribution, Gamma parameter estimation is performed on an original histogram, contour information of smoke scattering photons is estimated, then estimated residual error Δ h is calculated to be h1-y1, signal photons are contained in the residual error Δ h, an initial position of a target is searched by adopting a logarithm matching filter algorithm, a signal window epsilon is selected by taking the position as a central point, the window width is 2 system response function pulse widths, and photons in the window epsilon in the Δ h are reserved as the initial signal photons; and setting a photon number threshold value as xi, and when | delta h | is larger than xi outside a signal window, replacing the photon number on the corresponding time bin by a numerical value with the size of a smooth filtering window being 2FWHM to finally obtain corrected histogram data h 2.
Preferably, the step 2 specifically comprises:
for the corrected data h2, obtaining a photon rate function lambda reaching the detection focal plane by adopting a maximum likelihood function solving formula (6), and obtaining a noise parameter by adopting a median estimation algorithm so as to obtain an echo signal h3 only containing smoke scattered photons and signal reflection photons;
estimating smoke scattering echoes meeting the Gamma distribution by adopting a maximum likelihood estimation algorithm again to obtain an average scattering frequency beta and a maximum scattering frequency K;
and obtaining the lambda fog through least square optimization, and further separating the signal photon from the scattered photon to obtain the lambda target h 3-lambda fog.
Preferably, step 2 is based on calculating the scattered photons based on the N pulses.
Preferably, the step 3 specifically comprises: and for the separated signal photons, performing pixel-by-pixel processing by adopting a logarithm matching filter algorithm, performing histogram statistics on the reconstructed depth image, performing noise removal according to histogram distribution, and supplementing the defect points of the denoised depth image by adopting a median filter algorithm.
Preferably, for the echo photon λ fog estimation of the fog, besides the least square optimization, a genetic algorithm, a simulated annealing algorithm, a particle swarm optimization algorithm and a differential evolution algorithm can be adopted for optimization of various parameters.
A dual Gamma estimated outdoor foggy day imaging system, the system comprising:
the correction module carries out first Gamma fitting on the histogram detected by each pixel to realize the correction of the histogram data;
the signal compensation module compensates the accumulation effect generated by non-signal photons by adopting an observation model based on polynomial distribution to realize the calculation of echo photons;
and the reconstruction module is used for realizing the separation of signal photons and non-signal photons by adopting the second Gamma fitting and realizing the reconstruction of the target depth image based on the signal photons separated from each pixel.
A computer-readable storage medium having stored thereon a computer program for execution by a processor for implementing a dual Gamma estimation based single photon lidar fog-penetrating imaging method.
A computer device comprising a memory and a processor, the memory having stored therein a computer program that, when executed by the processor, performs a single photon lidar fog-penetrating imaging method based on dual Gamma estimation.
The invention has the following beneficial effects:
the present invention can separate signal photons from non-signal photons (scattered and noise photons) under extremely low SBR conditions. The algorithm compensates for the pile-up effect of non-signal photon generation by using an observation model based on a polynomial distribution and employs two Gamma estimations to eliminate non-signal photons.
The target depth image reconstruction algorithm in the outdoor fog environment adopts a three-step strategy, as shown in fig. 1. For area array photon counting radar data, the method is mainly used for extracting signals based on a pixel-by-pixel processing algorithm. Firstly, performing first Gamma fitting on the histogram detected by each pixel to realize the correction of the histogram data. Secondly, an observation model based on polynomial distribution is adopted to compensate the accumulation effect generated by non-signal photons, and the calculation of echo photons is realized. And thirdly, separating the signal photons from the non-signal photons by adopting second-time Gamma fitting. And finally, reconstructing the target depth image based on the signal photons separated from each pixel.
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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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of an outdoor fog-day depth imaging algorithm of a photon counting radar based on double Gamma estimation;
FIG. 2 is a diagram of histogram data rectification for a single pixel, with visibility of 1.7km, target distance of 1.4km, range gate delay of 9000ns, and each time Bin of 1.25 ns;
fig. 3 is a view of a scene and a depth image reconstruction result under different conditions, (a) a camera image of a detection scene in a foggy weather environment mainly includes four targets at different distances a, B, C, and D. (b) And (5) reconstructing a result by logarithmic matching filtering of the target A in a foggy environment. (c) Camera image of object a without fog. (d) Reconstructing a result of the logarithm matching filtering of the target A in the absence of fog;
FIG. 4 is a comparison graph of the target depth image reconstruction results at different imaging frame numbers;
FIG. 5 is a histogram count value for the lowest SBR value that can be processed by the different algorithms, with a 20k imaging frame number. The first action is histogram counting distribution, and the second action is accumulation compensated photon number distribution;
FIG. 6 shows the depth image reconstruction results of different distance targets, and the number of imaging frames is 20 k. The first behavior is the fog-free image corresponding to the object B in fig. 3(a), and the reconstructed results of different algorithms when fog is present. The second behavior is the fog-free image corresponding to the object C in fig. 3(a), and the reconstructed result of different algorithms when fog is present.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The present invention will be described in detail with reference to specific examples.
The first embodiment is as follows:
as shown in fig. 1 to 6, the specific optimized technical solution adopted to solve the above technical problems of the present invention is: the invention relates to a single photon laser radar fog-penetrating imaging method based on double Gamma estimation.
The invention provides a single photon laser radar fog-penetrating imaging method based on double Gamma estimation, which comprises the following steps:
step 1: performing first Gamma fitting on the histogram detected by each pixel to realize the correction of the histogram data;
step 2: compensating the accumulation effect generated by non-signal photons by adopting an observation model based on polynomial distribution to realize the calculation of echo photons;
and 3, step 3: and separating signal photons from non-signal photons by adopting second-time Gamma fitting, and reconstructing a target depth image based on the signal photons separated from each pixel.
The invention provides an outdoor foggy day imaging algorithm suitable for an area array photon counting radar, which can separate signal photons from non-signal photons (scattered photons and noise photons) under an extremely low SBR condition. The algorithm compensates for the pile-up effect of non-signal photon generation by using an observation model based on a polynomial distribution and employs two Gamma estimations to eliminate non-signal photons.
The target depth image reconstruction algorithm in the outdoor fog environment adopts a three-step strategy, as shown in fig. 1. For area array photon counting radar data, the invention mainly extracts signals based on a pixel-by-pixel processing algorithm. Firstly, performing first Gamma fitting on the histogram detected by each pixel to realize the correction of the histogram data. Secondly, an observation model based on polynomial distribution is adopted to compensate the accumulation effect generated by non-signal photons, and the calculation of echo photons is realized. And thirdly, separating signal photons from non-signal photons by adopting second-time Gamma fitting. And finally, reconstructing the target depth image based on the signal photons separated from each pixel.
The second embodiment is as follows:
the difference between the second embodiment and the first embodiment is only that:
the step 1 specifically comprises the following steps:
photons detected by the photon counting radar in a foggy environment mainly comprise smoke scattered photons lambda fog Target reflected photon λ target Background photon λ b And system noise, each photon count being from a poisson distribution P (.) distribution, represented by:
h t P[λ(t)]＝P[λ target (t)+λ fog (t)+λ b +b d ] (1)
λ target (t)＝ηαf(t-2R/c) (2)
h is observed histogram distribution, lambda is average photon number, eta represents quantum efficiency of detector, alpha represents reflection photon number of target, and is calculated by laser radar equation, f represents system pulse response function, R represents target distance value, c is light speed, b is light speed d Represents a dark count of the detector;
the photons are scattered for multiple times when passing through the scattering medium, the scattered photons satisfy Gamma distribution on the time domain, and the time domain distribution of the scattered photons is represented by the following formula:
wherein K is the maximum scattering number of times, r is related to the backscattering coefficient of smoke, and beta is the average scattering number of times reaching the focal plane of the detector within each time Bin;
for photon counting radar, the probability of detection of the tth time bin is related to the probability of detection of the previous time bin due to the presence of detector dead time. For a radar system with long dead time, the single photon detector can only respond to a photon counting signal once in the range gate, namely a single-trigger mode. When there are always strong scattered and background photons within the range gate, a SPAD (single-photon amplitude diode) only records the first returned photon in each transmit pulse period, and therefore does not store the photons arriving thereafter. This results in a systematic deviation between the observed histogram and the true echo photon rate function. In effect, this deviation reduces the signal photon count for longer arrival times in the range gate. The effect of this photon pile-up at the front of the range gate is called the pile-up effect.
To compensate for the pile-up effect, the present invention uses a polynomial distribution to model the histogram of the present invention's observations.
And (3) modeling the observed histogram by adopting polynomial distribution, wherein the probability P (h | lambda) of a single pixel histogram h observed in the daytime outdoor foggy environment meets the relation:
wherein, T is the maximum Bin number, N is the total pulse number, and the likelihood function is solved for the above formula:
and solving through maximum likelihood to obtain the number of photons of the ith time Bin as:
wherein λ is a single pulse stripEcho photon rate function under condition, target echo photon is lambda under single pulse condition target ＝λ-λ fog -λ b -b d 。
The main challenge of lidar to achieve three-dimensional imaging of targets through smoke is that the strong backscattering of smoke particles causes the observed histogram background noise to be high and non-uniform. When laser is transmitted in a smoke medium, multiple scattering collisions occur between photons and smoke particles;
the invention compensates the pile-up effect generated by non-signal photons by using an observation model based on polynomial distribution, and eliminates the non-signal photons by adopting two times of Gamma estimation, thereby finally separating the signal photons under the extremely low SBR condition from the non-signal photons (scattering and noise photons).
The third concrete embodiment:
the difference between the third embodiment and the second embodiment is only that:
carrying out pixel-by-pixel denoising pretreatment on histogram data h1, wherein the histogram distribution after Poisson triggering is Gamma distribution, carrying out Gamma parameter estimation on an original histogram, estimating the profile information of smoke scattering photons, then calculating and estimating residual error delta h as h1-y1, wherein the residual error delta h contains signal photons, searching the initial position of a target by adopting a logarithm matching filter algorithm, selecting a signal window epsilon by taking the position as a central point, the window width is 2 system response function pulse widths, and retaining the photons in the window epsilon in the delta h as the initial signal photons; and setting a photon number threshold value as xi, and when | delta h | is larger than xi outside a signal window, replacing the photon number on the corresponding time bin by a numerical value with the size of a smooth filtering window being 2FWHM to finally obtain corrected histogram data h 2.
The fourth concrete embodiment:
the difference between the fourth embodiment and the third embodiment is only that:
the step 2 specifically comprises the following steps:
for the corrected data h2, obtaining a photon rate function lambda reaching the detection focal plane by adopting a maximum likelihood function solving formula (6), and obtaining a noise parameter by adopting a median estimation algorithm so as to obtain an echo signal h3 only containing smoke scattered photons and signal reflection photons;
estimating smoke scattering echoes meeting the Gamma distribution by adopting a maximum likelihood estimation algorithm again to obtain an average scattering time beta and a maximum scattering time K;
and obtaining the lambda fog through least square optimization, and further separating the signal photon from the scattered photon to obtain the lambda target h 3-lambda fog.
The fifth concrete embodiment:
the difference between the fifth embodiment and the fourth embodiment is only that:
step 2 is to calculate the scattered photons based on the N pulses.
The sixth specific embodiment:
the difference between the sixth embodiment and the fifth embodiment is only that:
the step 3 specifically comprises the following steps: and for the separated signal photons, carrying out pixel-by-pixel processing by adopting a logarithm matching filter algorithm, carrying out histogram statistics on the reconstructed depth image, carrying out noise removal according to histogram distribution, and supplementing the defect points of the denoised depth image by adopting a median filter algorithm.
The seventh specific embodiment:
the seventh embodiment of the present application differs from the sixth embodiment only in that:
for the echo photon lambda fog estimation of fog, besides the least square method optimization, a genetic algorithm, a simulated annealing algorithm, a particle swarm optimization algorithm and a differential evolution algorithm can be adopted for optimization of various parameters.
The eighth specific embodiment:
the eighth embodiment of the present application differs from the seventh embodiment only in that:
the invention provides an outdoor fog day imaging system with double Gamma estimation, which comprises:
the correction module performs first Gamma fitting on the histogram detected by each pixel to realize the correction of the histogram data;
the signal compensation module compensates the accumulation effect generated by non-signal photons by adopting an observation model based on polynomial distribution to realize the calculation of echo photons;
and the reconstruction module is used for realizing the separation of signal photons and non-signal photons by adopting the second Gamma fitting and realizing the reconstruction of the target depth image based on the signal photons separated from each pixel.
The specific embodiment is nine:
the difference between the ninth embodiment and the eighth embodiment is only that:
the present invention provides a computer readable storage medium having stored thereon a computer program for execution by a processor for implementing a single photon lidar fog-penetrating imaging method based on dual Gamma estimation.
The specific embodiment ten:
the difference between the tenth embodiment and the ninth embodiment is only that:
the invention provides computer equipment which comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes a single photon laser radar fog-penetrating imaging method based on double Gamma estimation.
The first specific embodiment:
the difference between the eleventh embodiment and the tenth embodiment is only that:
the invention provides an outdoor foggy day imaging algorithm based on double Gamma estimation
The first step is data correction, i.e. the first Gamma estimation. Since the system of the present invention is directed to an external field remote target, the presence of noise photons in the echo signal results in the presence of outliers in the echo signal. As can be seen from equation (6), the value of each bin of the histogram directly affects the estimation result of the photon rate function. To reduce the estimation error, the observed histogram data h1 needs to be subjected to a pixel-by-pixel denoising preprocessing first. As can be seen from the simulation results in fig. 1, the histogram distribution after poisson triggering is also Gamma distribution (including scattered photons and noise photons). Therefore, the present invention directly performs Gamma parameter estimation on the original histogram, and the estimation result y1 is the blue line in fig. 2. The algorithm can well estimate the profile information of smoke scattered photons. The estimated residual Δ h — h1-y1 is then calculated. Because the residual Δ h contains signal photons, the initial position of the target is searched by using a traditional log-matched filtering algorithm. The position is taken as a central point to select a signal window epsilon, and the window width is 2 system response function pulse widths. The photons in Δ h within the window ε are retained as initial signal photons (green curve in FIG. 2). Setting the threshold value of the number of photons as xi, and if | delta h | is > xi outside the signal window, replacing the number of photons on the corresponding time bin with a value after smooth filtering (the window size is 2 FWHM). The final rectified histogram data h2 is shown as a red bar in fig. 2.
The second step is the separation of the signal photons from the scattered photons, i.e. the second Gamma estimation. And (4) solving a formula (6) by using a maximum likelihood function for the corrected data h2 to obtain a photon rate function lambda reaching the detection focal plane. This process is also known as compensation pile-up (compensation). Since the background photons can be approximately regarded as a fixed value, a median estimation algorithm can be adopted to obtain noise parameters, and then an echo signal h3 only containing smoke scattered photons and signal reflection photons is obtained. And then, estimating the smoke scattering echoes meeting the Gamma distribution by adopting a maximum likelihood estimation algorithm again to obtain an average scattering frequency beta and a maximum scattering frequency K. Finally, λ fog is obtained through least square optimization, and then λ target ═ h3- λ fog is separated from the scattered photons. The scattered photons are calculated based on N pulses throughout the calculation.
And the third step is target depth image reconstruction. And for the separated signal photons, performing pixel-by-pixel processing by adopting a logarithm matching filtering algorithm. Due to the fact that visibility is low, the atmosphere has strong attenuation on laser, and finally a large amount of noise exists in the reconstructed target depth image. Therefore, the invention carries out histogram statistics on the reconstructed depth image and carries out noise removal according to the histogram distribution. And finally, supplementing the defect points of the denoised depth graph by adopting a median filtering algorithm.
In order to check the target depth image reconstruction capability of the algorithm under the actual foggy day condition, especially when the visibility is low or the acquisition time of the system is very short, the method carries out depth image reconstruction on the target A in the image 3 (a). And compared with the traditional Coates algorithm capable of realizing the pile-up inhibition and the All parameter estimation method (APEA) capable of realizing the fog-penetrating imaging indoors, the result is shown in fig. 4. The imaging frame number of the system is consistent with the number of laser emission pulses. When the imaging frame number is 20k, the acquisition time of the image is 1 s. In this case, the algorithm proposed by the present invention can recover a large amount of contour information of the object, particularly in the lower region of the object. As the number of imaging frames decreases, the recovery capability of all algorithms decreases. When the number of imaging frames is 5k, the acquisition time at this time is 0.25 s. Compared with other two algorithms, the algorithm provided by the invention can recover more target contours, but the integrity of the image is poor, and the reconstruction effect in some local areas is not as good as that of the APEA algorithm.
The invention adopts SBR to describe the quality of the foggy day imaging environment, and further compares the reconstruction capability of the four algorithms under different SBR conditions. The present invention defines SBR as the number of signal photons (i.e. the back reflection of the target) divided by the number of noise photons (i.e. ambient light, dark counts and scattered photons) within the entire range gate. When the imaging frame number is 20k, the histogram corresponding to the minimum SBR and the accumulation compensated photon number distribution for the reconstruction of the target a by the four algorithms are shown in fig. 5. As expected, the algorithm proposed by the present invention can extract the signal under the minimum SBR condition compared to the other three algorithms. The SBR value was 0.003. Also, SNR and Photon Per Pixel (PPP) were used to describe the intensity of the return signal at 1s acquisition time, and the results are shown in Table 1. The present invention proposes that the algorithm have minimum SNR and PPP values of 0.474 and 75.7, respectively. It should be noted that these indices are calculated after the pile-up compensation is performed based on equation (6). Therefore, the algorithm provided by the invention has stronger extraction capability on a small number of signal photons under the condition of low SBR.
The histogram count value corresponding to the lowest SBR value that can be processed by the different algorithms of FIG. 5 has an imaging frame number of 20 k. The first action is histogram counting distribution, and the second action is accumulation compensated photon number distribution.
TABLE 1 comparison of limit indexes for reconstruction with different algorithms
In view of the fact that the increase of the acquisition time can improve the fog penetration detection capability of the system, the acquisition time is set to be 1s, namely the imaging frame number is 20k, and the target B and the target C in the image 3(a) are detected. And the four algorithms are used for respectively reconstructing depth images of the two targets, and the reconstruction result is shown in fig. 6. Wherein the distance of the target B is 0.8km, and the distance of the target C is 0.5 km. Since the distance between the two targets is small relative to the visibility of 1.7km, the attenuation of the laser light when it reaches the target surface is weak. Therefore, compared with the processing result of the target A, the results of reconstructing the targets B and C by the four algorithms are complete. The closer the distance, the better the target reconstruction result. However, no matter whether the target B or C is, the algorithm of the invention has better target recovery capability and more complete recovery of the target edge contour. Therefore, when the target distance is smaller than the visibility, the target depth image in the foggy environment can be better reconstructed by the algorithm provided by the invention.
For the echo photon lambda fog estimation of fog, besides the least square method optimization, various parameter optimization algorithms such as a genetic algorithm, a simulated annealing algorithm, a particle swarm optimization algorithm, a differential evolution algorithm and the like can be adopted.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise. Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention. The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory. It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments. In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The above is only a preferred embodiment of the double-Gamma estimation-based single photon laser radar fog-penetrating imaging method, and the protection range of the double-Gamma estimation-based single photon laser radar fog-penetrating imaging method is not limited to the above embodiments, and all technical solutions belonging to the idea belong to the protection range of the invention. It should be noted that modifications and variations that do not depart from the gist of the invention are intended to be within the scope of the invention.
Claims (10)
1. A single photon laser radar fog penetration imaging method based on double Gamma estimation is characterized in that: the method comprises the following steps:
step 1: performing first Gamma fitting on the histogram detected by each pixel to realize the correction of the histogram data;
step 2: compensating the accumulation effect generated by non-signal photons by adopting an observation model based on polynomial distribution to realize the calculation of echo photons;
and step 3: and separating signal photons from non-signal photons by adopting second-time Gamma fitting, and reconstructing a target depth image based on the signal photons separated from each pixel.
2. The single photon laser radar fog-penetrating imaging method based on double Gamma estimation as claimed in claim 1, wherein: the step 1 specifically comprises the following steps:
photons detected by the photon counting radar in a foggy environment mainly comprise smoke scattered photons lambda fog Target reflected photon λ target Background photon λ b And system noise, each photon count being from a poisson distribution P (.) distribution, represented by:
h t P[λ(t)]＝P[λ target (t)+λ fog (t)+λ b +b d ] (1)
λ target (t)＝ηαf(t-2R/c) (2)
wherein h is the observed histogram distribution, λ is the average photon number, η represents the quantum efficiency of the detector, α represents the reflected photon number of the target, and is calculated by the lidar equation, and f represents the system impulse responseFunction, R represents the target distance value, c is the speed of light, b d Represents a dark count of the detector;
the photons are scattered for multiple times when passing through the scattering medium, the scattered photons satisfy Gamma distribution on the time domain, and the time domain distribution of the scattered photons is represented by the following formula:
k is the maximum scattering frequency, r is related to the backscattering coefficient of smoke, and beta is the average scattering frequency reaching the focal plane of the detector within each time Bin;
and (3) modeling the observed histogram by adopting polynomial distribution, wherein the probability P (h | lambda) of a single pixel histogram h observed in the daytime outdoor foggy environment meets the relation:
wherein, T is the maximum Bin number, N is the total pulse number, and the likelihood function is solved for the above formula:
and solving through maximum likelihood to obtain the number of photons of the ith time Bin as:
wherein, λ is the echo photon rate function under the condition of single pulse, and the target echo photon under the condition of single pulse is λ target ＝λ-λ fog -λ b -b d 。
3. The single photon laser radar fog-penetrating imaging method based on double Gamma estimation as claimed in claim 2, wherein: carrying out pixel-by-pixel denoising pretreatment on histogram data h1, wherein histogram distribution triggered by Poisson is Gamma distribution, carrying out Gamma parameter estimation on an original histogram, estimating contour information of smoke scattered photons, then calculating an estimated residual error delta h as h1-y1, wherein the residual error delta h contains signal photons, searching the initial position of a target by adopting a logarithm matching filter algorithm, selecting a signal window epsilon by taking the position as a central point, the window width is 2 system response function pulse widths, and retaining photons in the window epsilon in the delta h as initial signal photons; and setting a photon number threshold value as xi, and when | delta h | is larger than xi outside a signal window, replacing the photon number on the corresponding time bin by a numerical value with the size of a smooth filtering window being 2FWHM to finally obtain corrected histogram data h 2.
4. The single photon laser radar fog-penetrating imaging method based on double Gamma estimation as claimed in claim 3, wherein: the step 2 specifically comprises the following steps:
for the corrected data h2, adopting a maximum likelihood function solving formula (6) to obtain a photon rate function lambda reaching the detection focal plane, adopting a median estimation algorithm to obtain a noise parameter, and further obtaining an echo signal h3 only containing smoke scattering photons and signal reflection photons;
estimating smoke scattering echoes meeting the Gamma distribution by adopting a maximum likelihood estimation algorithm again to obtain an average scattering frequency beta and a maximum scattering frequency K;
and obtaining the lambda fog through least square optimization, and further separating the signal photon from the scattered photon to obtain the lambda target h 3-lambda fog.
5. The single photon laser radar fog-penetrating imaging method based on double Gamma estimation as claimed in claim 4, wherein: step 2 is to calculate the scattered photons based on N pulses.
6. The single photon laser radar fog-penetrating imaging method based on double Gamma estimation as claimed in claim 5, wherein: the step 3 specifically comprises the following steps: and for the separated signal photons, carrying out pixel-by-pixel processing by adopting a logarithm matching filter algorithm, carrying out histogram statistics on the reconstructed depth image, carrying out noise removal according to histogram distribution, and supplementing the defect points of the denoised depth image by adopting a median filter algorithm.
7. The single photon laser radar fog-penetrating imaging method based on double Gamma estimation as claimed in claim 6, wherein: for the echo photon lambda fog estimation of fog, besides the least square method optimization, a genetic algorithm, a simulated annealing algorithm, a particle swarm optimization algorithm and a differential evolution algorithm can be adopted for optimization of various parameters.
8. An outdoor fog day imaging system with double Gamma estimation is characterized in that: the system comprises:
the correction module carries out first Gamma fitting on the histogram detected by each pixel to realize the correction of the histogram data;
the signal compensation module compensates the accumulation effect generated by non-signal photons by adopting an observation model based on polynomial distribution to realize the calculation of echo photons;
and the reconstruction module is used for separating signal photons from non-signal photons by adopting secondary Gamma fitting and reconstructing a target depth image based on the signal photons separated from each pixel.
9. A computer-readable storage medium, on which a computer program is stored, the program being executed by a processor for implementing a dual-Gamma estimation based single photon lidar fog-penetrating imaging method as claimed in any of claims 1 to 7.
10. A computer device comprising a memory and a processor, the memory storing a computer program, the processor executing a dual-Gamma estimation based single photon lidar fog-penetrating imaging method according to any one of claims 1-7 when the processor executes the memory stored computer program.
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