CN115900582A - Meter-level planar element surface shape detection device and method for eliminating intermediate-frequency coherent noise - Google Patents
Meter-level planar element surface shape detection device and method for eliminating intermediate-frequency coherent noise Download PDFInfo
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Abstract
The invention discloses a meter-level plane element surface shape detection device and method for eliminating intermediate frequency coherent noise, wherein the detection method comprises the following steps: (1) The clamping angle of the large-caliber element is adjusted through an electric five-dimensional adjusting frame, so that an interference pattern which is clear in focusing and good in fringe contrast is obtained; (2) The wavelength of the tunable laser is changed by adjusting the voltage of a laser controller, so that the wavelength phase shift is realized, and a group of phase shift interferograms are acquired by a measuring camera in a time-sharing manner; (3) Sequentially carrying out aperture selection, surface shape demodulation by a random step size phase-shifting algorithm, unwrapping phase by a neural network method and inclination removal by aberration fitting on the phase-shifting interferogram to obtain a surface shape image phi (x, y), and carrying out system error removal on the surface shape image phi (x, y); (4) The convolution neural network is utilized to process and eliminate the intermediate frequency coherent noise to obtain phi dn (x, y). By using the invention, the medium-frequency surface shape of the meter-level planar element can be effectively measured with high precision.
Description
Technical Field
The invention belongs to the technical field of optical precision detection, and particularly relates to a meter-level plane element surface shape detection device and method for eliminating intermediate frequency coherent noise.
Background
In an inertial confinement nuclear fusion (ICF) system, strict requirements are imposed on the output quality of laser, and the residual error of optical element manufacture directly affects the output quality of the laser, especially the manufacturing error of the intermediate frequency band (caliber 1/12-1/160) of the optical element, reduces the brightness of a central bright spot, widens the bright spot width, is also an important reason for causing nonlinear self-focusing in a high-power laser system, and seriously affects the performance of the system.
The large-caliber optical element with the caliber of more than 1m is called a meter-level optical element, and has high production cost, large caliber and very high requirement on the medium-frequency manufacturing precision of the face shape. To ensure that the mid-frequency profile of the optical element can meet the requirements of the ICF device, it is necessary to measure it with high precision throughout the manufacturing process. The method of interferometric detection is usually used to perform high-precision measurement, but in interferometric detection, many system factors affect the precision of interferometric measurement, and the presence of coherent noise has a large influence on the result of interferometric measurement.
Most of interference detection systems adopt a laser light source with good coherence as an illumination light source, and when the laser light source passes through a plurality of optical elements, a large amount of coherent fringe noise exists in an interference image due to defects or multiple reflections in the elements. The majority of coherent noise is Newton's ring or target heart shape, the frequency range is in the first frequency band (PSD 1) of power spectrum density, the period is 1/12-1/160 of the full aperture, and the coherent noise overlaps with the frequency band which needs high-precision detection.
Although the conventional frequency domain filtering method can weaken the coherent noise circular rings, the information of the full-band key surface shape is lost.
Therefore, an effective and high-precision interference detection algorithm is required to remove the intermediate frequency coherent noise, and the extreme detection requirement of the large-aperture optical element is met.
Disclosure of Invention
The invention provides a meter-level planar element surface shape detection device and method for eliminating intermediate frequency coherent noise, which can realize high-precision measurement of the intermediate frequency surface shape of a meter-level planar element.
A meter-level plane element surface shape detection device for eliminating intermediate frequency coherent noise comprises an interference detection system and a computer processing module;
the interference detection system is arranged on the air-flotation shock-insulation optical platform and comprises a tunable laser, a polarizer, a beam expanding lens, a spatial filter, a reflecting mirror, a beam splitting prism, an imaging lens, a measuring camera, a small-caliber collimating objective lens group, a small-caliber negative lens, a first turning reflecting mirror, a second turning reflecting mirror, a large-caliber collimating objective lens, a transmission standard flat crystal TF and a reflection standard flat crystal RF;
the laser emitted by the tunable laser passes through the polarizer to obtain linearly polarized light, and then the linearly polarized light sequentially passes through the beam expanding lens, the spatial filter, the reflecting mirror, the beam splitting prism and the small-caliber collimating objective lens group to emit parallel light; after the parallel light is emitted, the parallel light passes through the small-caliber negative lens, the first turning reflector and the second turning reflector to realize twice turning, and then passes through the large-caliber collimating objective lens to emit expanded parallel light; when the expanded parallel light passes through the transmission standard flat crystal TF, a part of light is reflected and condensed to the beam splitter prism to be used as interference reference light; the other part of light is transmitted through a mirror to be measured as measuring light and then reflected by a reflection standard flat crystal RF, and the original path returns to the beam splitter prism to be used as interference detection light; the interference reference light and the interference detection light are reflected by the light splitting prism and then pass through the imaging lens, and interference occurs at the image surface of the measuring camera to obtain an interference image;
the computer processing module comprises a hardware control module, an image acquisition module and an interference pattern data analysis processing module; the hardware control module is connected with a controller of the tunable laser, and the wavelength of the tunable laser is changed by adjusting the voltage of the controller of the tunable laser, so that the wavelength phase shift is realized; the image acquisition module is connected with the measuring camera, and after a group of wavelength phase-shifting interferograms are obtained, data are transmitted to the interferogram data analysis processing module for analysis.
Further, the wavelength λ of the tunable laser 0 =632.8nm, and the wavelength phase shift is realized by adjusting the voltage of a controller of the tunable laser to change the wavelength for interference phase modulation; wherein the phase change amount
And the wavelength variation DeltaLambda satisfies
h is the interference cavity length.
A meter-level plane element surface shape detection method for eliminating intermediate frequency coherent noise uses the meter-level plane element surface shape detection device, and comprises the following specific processes:
step 3, sequentially carrying out aperture selection, surface shape demodulation by a random step size phase shift algorithm, unwrapping phase by a neural network method and inclination removal by aberration fitting on the phase shift interferogram to obtain a surface shape image phi (x, y), and carrying out system error removal on the surface shape image phi (x, y);
step 4, further processing by using a neural network, and eliminating intermediate frequency coherent noise to obtain a final surface shape phi dn (x,y)。
In the step (3), in the process of carrying out aberration fitting and inclination removal, a Zernike polynomial is adopted for the regular circular aperture to carry out aberration fitting and inclination removal, and Schmidt orthogonalization is adopted for the irregular aperture to carry out aberration fitting and inclination removal.
The specific process of the step (4) is as follows:
(4-1) constructing a simulation data set;
(4-2) dividing the data set into a training set, a verification set and a test set; selecting a PDU convolutional neural network, training the convolutional neural network by using a training set, verifying the network learning effect by using a verification set, and evaluating the accuracy of the trained and verified network by using a test set;
(4-3) preprocessing the surface shape chart phi (x, y) to obtain a low-frequency surface shape phi l (x, y) and intermediate frequency surface profile phi m (x, y) and high frequency profile φ h (x,y)；
(4-4) intermediate frequency surface shape phi after pretreatment m (x, y) inputting the trained PDU convolutional neural network to obtain a coherent noise removed surface shape result phi' mt (x, y) and separation noise n (x, y);
(4-5) surface shape result phi 'of coherent noise removal by separation noise n (x, y)' mt (x, y) performing correction treatment to obtain the medium-frequency surface shape phi m Final noise removal result of (x, y)' m (x,y)；
(4-6) shaping the low-frequency surface phi l (x, y) and the treated mid-frequency surface phi' m (x, y) are added to obtain the final surface shape phi dn (x,y)＝φ l (x,y)+φ' m (x,y)。
The specific process of the step (4-1) is as follows:
(4-1-1) generating a profile W (x, y) containing high, middle and low frequency bands by using a random profile generating function;
(4-1-2) carrying out PSD1 error function filtering on the power spectral density first frequency band of the surface shape W (x, y) to obtain an intermediate frequency truth value surface shape W r (x, y), the error function filter is:
in the formula, f 1 And f 2 Is a set PSD1 frequency range;
(4-1-3) randomly generating a noise center point N with coordinates of (x) 0 ,y 0 ) And if the radius of the ring coherent noise is R, the phase of the ring coherent noise is:
in the formula, r is the number of pixels reaching a noise central point N, and dx is the actual size of each pixel point;
(4-1-4) simulation of intermediate frequency truth surfaceShape W r Adding (x, y) and coherent noise phase n to obtain simulated intermediate frequency noise-containing surface shape
Where k is the number of coherent noise rings.
In the step (4-2), the proportion of the training set, the verification set and the test set is 3.
In the step (4-3), the surface map phi (x, y) is preprocessed in the following steps:
four-image topology is carried out on the surface map phi (x, y) to obtain phi 1 (x, y) such that the face-shaped edges are spatially continuous; for topological back surface shape phi 1 (x, y) performing Fourier transform to obtain a spatial frequency spectrum F (x, y); the spatial frequency spectrum F (x, y) is divided into frequency domains according to PSD 1: low frequency F l (x, y) intermediate frequency F m (x, y) and high frequency F h (x, y), respectively and sequentially carrying out inverse Fourier transform and topological solving operation to obtain the surface shapes of different frequency bands: low frequency phi l (x, y), intermediate frequency φ m (x, y) and a high frequency phi h (x,y)。
In the step (4-5), the correction process is phi' m (x,y)＝φ' mt (x, y) + tn (x, y), where t is a correction coefficient and satisfies RMS (phi' m (x, y)) takes the minimum value.
Compared with other phase denoising methods, the method has the following advantages:
1. the invention can eliminate the medium-frequency coherent noise of the surface shape of the meter-level planar element and effectively reduce the PSD1 value of the surface shape of the meter-level planar element.
2. The invention only processes the middle frequency band of the surface shape and does not lose the original low-frequency and high-frequency information of the surface shape.
3. The neural network processing result of the invention contains the separated noise, and can be used for carrying out secondary correction on the result of the first output network, thereby further eliminating the intermediate frequency coherent noise.
Drawings
FIG. 1 is a schematic diagram of a meter-level planar element surface shape detection device for eliminating intermediate frequency coherent noise according to the present invention;
FIG. 2 is a diagram of a neural network architecture in an embodiment of the present invention;
FIG. 3 is a surface map obtained after the processing in steps 1-3 in the example of the present invention;
FIG. 4 is a denoising surface map obtained after the processing of step 4 in FIG. 3;
FIG. 5 is a plot of the mid-frequency (PSD 1 band) profile of FIG. 3;
FIG. 6 is a profile of the intermediate frequency (PSD 1 band) obtained after neural network processing in FIG. 5;
fig. 7 shows the separated noise obtained after neural network processing in fig. 5.
Detailed Description
The invention will be described in further detail below with reference to the drawings and examples, which are intended to facilitate the understanding of the invention without limiting it in any way.
As shown in fig. 1, a device for detecting the surface shape of a meter-level planar element for eliminating intermediate frequency coherent noise includes an interference detection system and a computer processing module.
The whole interference detection system is fixed on an air-flotation shock-insulation optical platform 1 and comprises a tunable laser 2, a polarizer 3, a beam expander 4, a spatial filter 5, a reflector 6, a beam splitter prism 7, an imaging lens 8, a measuring camera 9, a small-caliber collimating objective lens group 10, a small-caliber negative lens 11, a first turning reflector 12, a second turning reflector 13, a large-caliber collimating objective lens 14, a transmission standard flat crystal TF15 and a reflection standard flat crystal RF17;
the laser emitted by the tunable laser 2 passes through the polarizer 3 to obtain linearly polarized light, and then sequentially passes through the beam expander 4, the spatial filter 5, the reflector 6, the beam splitter prism 7 and the small-caliber collimating objective lens group 10 to emit parallel light; after the parallel light is emitted, the parallel light passes through the small-caliber negative lens 11, the first turning reflector 12 and the second turning reflector 13 to realize twice turning, and then passes through the large-caliber collimating objective 14 to emit expanded parallel light; when the expanded parallel light passes through the transmission standard flat crystal TF15, a part of light is reflected and condensed to the beam splitter prism 7 to be used as interference reference light; the other part of light is transmitted through a mirror 16 to be measured as measuring light and then reflected by a reflection standard flat crystal RF17, and the original path returns to the beam splitter prism 7 to be used as interference detection light; the interference reference light and the interference detection light are reflected by the beam splitter prism 7 and then pass through the imaging lens 8, and interference occurs at the image surface of the measuring camera 9, so that an interference pattern is obtained.
In this embodiment, the small-aperture collimating objective lens group 10 is 4 inches, the small-aperture negative lens 11 is 4 inches, and the large-aperture collimating objective lens 14 is 32 inches.
The computer processing module comprises a hardware control module, an image acquisition module and an interference pattern data analysis processing module. The hardware control module is connected with the tunable laser 2, and the wavelength of the tunable laser 2 is changed by adjusting the voltage of the tunable laser 2, so that the wavelength phase shift is realized. The image acquisition module is connected with the measuring camera 9, and after a group of wavelength phase-shifting interferograms are obtained, data are transmitted to the interferogram data analysis processing module for analysis.
The meter-level plane element surface shape detection for eliminating the intermediate frequency coherent noise by using the device comprises the following steps:
And 2, changing the wavelength of the tunable laser 2 by adjusting the voltage of the tunable laser 2 so as to realize wavelength phase shift, and acquiring a group of phase-shift interferograms in a time-sharing manner by using the measuring camera 9.
The length of the interference cavity is 800mm according to the formula
The calculated phase shift of pi/2 needs to change the wavelength by delta lambda =6.26 × 10 4 nm, and the number of collected interferograms is 5.
And 3, selecting an aperture for the phase-shifting interferogram, including a complete interferogram as far as possible, demodulating the surface shape by using a random step size phase-shifting algorithm, and iteratively calculating the step size and the phase of the interferogram by using a plurality of phase-shifting interferograms based on a least square method through the phase of each pixel point on each interferogram by using an Advanced Iterative Algorithm (AIA). After the demodulation surface shape is obtained, the phase position is unwrapped by using a neural network method. And adopting Zernike polynomials to perform aberration fitting to remove inclination for the regular circular aperture, and adopting Schmidt orthogonalization to perform aberration fitting to remove inclination for the irregular aperture to obtain a surface profile phi (x, y). And removing the systematic error of the surface map phi (x, y) by using the calibrated cavity error.
Step 4, processing by using a neural network to eliminate intermediate frequency coherent noise to obtain phi dn (x, y). The specific process is as follows:
(4-1) constructing a simulation data set.
(4-2) dividing the data set into a training set, a verification set and a test set; selecting a PDU (Phase Denoise Unet) convolutional neural network, training the convolutional neural network by using a training set, verifying the learning effect of the network by using a verification set, and evaluating the accuracy of the trained and verified network by using a test set.
Wherein, the structure of the PDU convolutional neural network is shown in figure 2,
the body of the network consists of six layers of U-Net, with transitions between each layer made by DenseNet. The interference pattern is zoomed for a plurality of times by the U-Net, and the number of extracted features is increased along with the continuous deepening of the layer number of the U-Net, thereby being beneficial to extracting the features of the phase. Meanwhile, the depth and convolution time of the network are expanded between the U-Net layers through the DenseNet, so that the network can extract the characteristics of higher layers and abstraction. In addition, jump connection operation is largely used in U-Net and DenseNet, so that gradient transmission is ensured, stability of the network is improved, and training time is shortened.
And (4) training by using the simulation data set in the step (4-1) to obtain the weight of the PDU convolutional neural network.
(4-3) preprocessing the surface shape chart phi (x, y) to obtain a low-frequency surface shape phi l (x, y) and mid-frequency surface profile phi m (x, y) and high frequency profile φ h (x,y)。
(4-4) intermediate frequency surface shape phi after pretreatment m (x, y) inputting the trained PDU convolutional neural network to obtain a coherent noise removed surface shape result phi' mt (x, y) and separation noise n (x, y).
(4-5) separating the noise n (x, y) pairsSurface shape result phi 'of coherent noise removal' mt (x, y) carrying out correction treatment to obtain the medium-frequency surface shape phi m (x, y) Final De-noising result φ' m (x,y)。
(4-6) shaping the low-frequency surface phi l (x, y) and the treated mid-frequency surface phi' m (x, y) are added to obtain the final surface shape phi dn (x,y)＝φ l (x,y)+φ' m (x,y)。
FIGS. 3-7 show the results of the invention applied to the surface shape detection of the mid-frequency coherent noise-eliminating meter-class planar element. Wherein FIG. 3 is the surface profile φ (x, y) obtained in step 3, and FIG. 4 is the surface profile φ (x, y) obtained in step 4 by eliminating the intermediate frequency coherent noise by processing with neural network dm (x, y), FIG. 5 is a surface plot φ (x, y) of intermediate frequency φ m (x, y), FIG. 6 is the intermediate frequency result φ 'of neural network processing' m (x, y), and fig. 7 shows the separation noise n (x, y). Wherein the PV value of the surface shape phi (x, y) is 382.85nm, the RMS value is 72.21nm, and the equivalent PSD1 value is phi m RMS value of (x, y) was 4.77nm. Surface shape phi after neural network treatment dm (x, y) has a PV value of 355.90nm, an RMS value of 75.18nm and an equivalent PSD1 value of phi' m RMS value of (x, y) was 2.81nm. Through the processing of the neural network, the equivalent PSD1 value of the experimental surface shape can be reduced from 4.77nm to 2.81nm, and the intermediate frequency coherent noise can be effectively separated.
The embodiments described above are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions and equivalents made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. A meter-level plane element surface shape detection device for eliminating intermediate frequency coherent noise is characterized by comprising an interference detection system and a computer processing module;
the interference detection system is arranged on an air-flotation shock-insulation optical platform (1) and comprises a tunable laser (2), a polarizer (3), a beam expander (4), a spatial filter (5), a reflector (6), a beam splitter prism (7), an imaging lens (8), a measuring camera (9), a small-caliber collimating objective lens group (10), a small-caliber negative lens (11), a first turning reflector (12), a second turning reflector (13), a large-caliber collimating objective lens (14), a transmission standard flat crystal TF (15) and a reflection standard flat crystal RF (17);
the laser emitted by the tunable laser (2) passes through the polarizer (3) to obtain linearly polarized light, and then sequentially passes through the beam expander (4), the spatial filter (5), the reflector (6), the beam splitter prism (7) and the small-caliber collimating objective lens group (10) to emit parallel light; after the parallel light is emitted, the parallel light passes through the small-caliber negative lens (11), the first turning reflector (12) and the second turning reflector (13) to realize twice turning, and then passes through the large-caliber collimating objective (14) to emit expanded parallel light; when the expanded parallel light passes through the transmission standard flat crystal TF (15), part of light is reflected and condensed to the beam splitter prism (7) to be used as interference reference light; the other part of light is transmitted through a mirror (16) to be measured as measuring light and then reflected by a reflection standard flat crystal RF (17), and the other part of light returns to the beam splitting prism (7) as interference detection light; the interference reference light and the interference detection light are reflected by a beam splitter prism (7), then pass through an imaging lens (8), and interfere at the image surface of a measurement camera (9) to obtain an interference image;
the computer processing module comprises a hardware control module, an image acquisition module and an interference pattern data analysis processing module; the hardware control module is connected with a controller of the tunable laser (2), and the wavelength of the tunable laser (2) is changed by adjusting the voltage of the controller of the tunable laser (2), so that the wavelength phase shift is realized; the image acquisition module is connected with the measuring camera (9), and after a group of wavelength phase-shifting interferograms are obtained, data are transmitted to the interferogram data analysis processing module for analysis.
2. The mid-frequency coherent noise cancellation meter-level planar element surface shape detection device according to claim 1, wherein the wavelength λ of the tunable laser (2) is 0 The wavelength is changed by adjusting the voltage of a controller of the tunable laser (2) to carry out interference phase modulation, so that the wavelength phase shift is realized; wherein the phase change amount
And the wavelength variation DeltaLambda satisfies
h is the interference cavity length.
3. A meter-level plane element surface shape detection method for eliminating intermediate frequency coherent noise is characterized in that the meter-level plane element surface shape detection device of claim 1 or 2 is used, and the specific process is as follows:
step 1, adjusting the clamping angle of a large-aperture collimating objective (14), a transmission standard flat crystal TF (15), a lens to be measured (16) and a reflection standard flat crystal RF (17) through an electric five-dimensional adjusting frame to obtain an interference pattern with clear focusing and good fringe contrast;
step 2, changing the wavelength of the tunable laser (2) by adjusting the voltage of a controller of the tunable laser (2), thereby realizing wavelength phase shift, and acquiring a group of phase shift interferograms in a time-sharing manner by using a measuring camera (9);
step 3, sequentially carrying out aperture selection, surface shape demodulation by a random step size phase shift algorithm, unwrapping phase by a neural network method and inclination removal by aberration fitting on the phase shift interferogram to obtain a surface shape image phi (x, y), and carrying out system error removal on the surface shape image phi (x, y);
step 4, further processing is carried out by utilizing a convolution neural network, and medium-frequency coherent noise is eliminated to obtain a final surface shape phi dn (x,y)。
4. The meter-level planar element surface shape detection method for eliminating intermediate frequency coherent noise according to claim 3, wherein in the step (3), in the process of performing aberration fitting and inclination removal, a Zernike polynomial is adopted for a regular circular aperture to perform aberration fitting and inclination removal, and a Schmidt orthogonalization is adopted for an irregular aperture to perform aberration fitting and inclination removal.
5. The meter-level planar element surface shape detection method for eliminating intermediate frequency coherent noise according to claim 3, wherein the specific process of the step (4) is as follows:
(4-1) constructing a simulation data set;
(4-2) dividing the data set into a training set, a verification set and a test set; selecting a PDU convolutional neural network, training the convolutional neural network by using a training set, verifying the network learning effect by using a verification set, and evaluating the accuracy of the trained and verified network by using a test set;
(4-3) preprocessing the surface shape chart phi (x, y) to obtain a low-frequency surface shape phi l (x, y) and mid-frequency surface profile phi m (x, y) and high frequency profile φ h (x,y)；
(4-4) intermediate frequency surface shape phi after pretreatment m (x, y) inputting the trained PDU convolutional neural network to obtain a coherent noise removed surface shape result phi' mt (x, y) and separation noise n (x, y);
(4-5) surface shape result φ 'of coherent noise removal by separation noise n (x, y)' mt (x, y) performing correction treatment to obtain the medium-frequency surface shape phi m (x, y) Final De-noising result φ' m (x,y)；
(4-6) shaping the low-frequency surface phi l (x, y) and the treated mid-frequency surface phi' m (x, y) are added to obtain the final surface shape phi dn (x,y)＝φ l (x,y)+φ' m (x,y)。
6. The meter-level planar element surface shape detection method for eliminating intermediate frequency coherent noise according to claim 5, wherein the specific process of step (4-1) is as follows:
(4-1-1) generating a profile W (x, y) containing high, middle and low frequency bands by using a random profile generating function;
(4-1-2) carrying out power spectral density first frequency band PSD1 error function filtering on the surface shape W (x, y) to obtain an intermediate frequency true value surface shape W r (x, y), the error function filter is:
in the formula (f) 1 And f 2 For a set PSD1 frequencyA rate range;
(4-1-3) randomly generating a noise center point N with coordinates of (x) 0 ,y 0 ) And if the radius of the ring coherent noise is R, the phase of the ring coherent noise is:
in the formula, r is the number of pixels reaching a noise central point N, and dx is the actual size of each pixel point;
(4-1-4) simulating the intermediate frequency true surface shape W r Adding (x, y) and coherent noise phase n to obtain simulated intermediate frequency noise-containing surface shape
Where k is the number of coherent noise rings.
7. The meter-level planar element surface shape detection method for eliminating intermediate frequency coherent noise according to claim 5, wherein in step (4-2), the ratio of the training set, the validation set and the test set is 3.
8. The meter-level planar element surface shape detection method for eliminating intermediate frequency coherent noise according to claim 5, wherein in step (4-3), the process of preprocessing the surface shape diagram phi (x, y) is as follows:
four-image topology is carried out on the surface map phi (x, y) to obtain phi 1 (x, y) such that the face-shaped edges are spatially continuous; for topological back surface shape phi 1 (x, y) performing Fourier transform to obtain a spatial frequency spectrum F (x, y); the spatial frequency spectrum F (x, y) is divided into frequency domains according to PSD 1: low frequency F l (x, y), intermediate frequency F m (x, y) and a high frequency F h (x, y), respectively and sequentially carrying out inverse Fourier transform and topological solving operation to obtain the surface shapes of different frequency bands: low frequency phi l (x, y), intermediate frequency φ m (x, y) and a high frequency phi h (x,y)。
9. The method of claim 5 for eliminating IF phasesThe method for detecting the surface shape of the meter-level planar element with dry noise is characterized in that in the step (4-5), the correction process is phi' m (x,y)＝φ' mt (x, y) + tn (x, y), where t is a correction coefficient and satisfies RMS (phi' m (x, y)) takes the minimum value.
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| CN117789041B (en) * | 2024-02-28 | 2024-05-10 | 浙江华是科技股份有限公司 | Ship defogging method and system based on atmospheric scattering priori diffusion model |
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