CN114049263A - Mean filtering-based associated imaging denoising method - Google Patents
Mean filtering-based associated imaging denoising method Download PDFInfo
- Publication number
- CN114049263A CN114049263A CN202111134437.0A CN202111134437A CN114049263A CN 114049263 A CN114049263 A CN 114049263A CN 202111134437 A CN202111134437 A CN 202111134437A CN 114049263 A CN114049263 A CN 114049263A
- Authority
- CN
- China
- Prior art keywords
- hadamard
- measured
- imaging
- mean filtering
- speckle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003384 imaging method Methods 0.000 title claims abstract description 70
- 238000001914 filtration Methods 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 35
- 230000002596 correlated effect Effects 0.000 claims abstract description 14
- 230000003287 optical effect Effects 0.000 claims description 14
- 230000001678 irradiating effect Effects 0.000 claims description 8
- 230000033001 locomotion Effects 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 5
- 238000004088 simulation Methods 0.000 abstract description 10
- 238000009825 accumulation Methods 0.000 abstract 1
- 230000000875 corresponding effect Effects 0.000 abstract 1
- 238000011084 recovery Methods 0.000 description 9
- 238000005314 correlation function Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000005338 frosted glass Substances 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
Images
Classifications
-
- G06T5/70—
Abstract
The invention provides an associated imaging denoising method based on mean value filtering. According to the characteristic of mean value filtering, the invention designs the moving speckles, irradiates the speckles on the unknown object through the correlation imaging system, and obtains the image of the denoised unknown object through the accumulation of the corresponding bucket detector values and the second-order correlation between the moving speckles. The mean filtering method in image denoising is introduced into the correlated imaging, and the influence of noise on imaging quality can be effectively reduced under the condition of Gaussian white noise. Simulation results show that compared with the original associated imaging results without mean filtering, the method can effectively reduce the influence of noise on imaging and improve the imaging quality, has the advantages of simple structure and convenience in implementation, is suitable for the condition with higher requirements on the imaging quality, and has a larger application prospect.
Description
Technical Field
The invention relates to a mean value filtering-based associated imaging denoising method, and belongs to the field of associated imaging.
Background
Correlated Imaging (also known as Ghost Imaging, GI) is one of the leading edges and hot spots in the field of quantum optics in recent years. Two optical paths are adopted for correlated imaging, one optical path is called a signal optical path, and light of speckles passing through an object is received by a barrel detector without space resolution capability; the other is called a reference light path, which means that the speckles are transmitted for a certain distance and then received by a detector with spatial resolution capability. And finally, performing second-order correlation on the detection results of the two optical paths to obtain a recovered image of the object in the reference optical path. Different from the traditional imaging mode, the correlation imaging can recover the object image on the optical path without the object, and the characteristic is called non-localization. With the development of related imaging, new "ghost" imaging technologies such as thermo-optic "ghost" imaging, computational "ghost" imaging, and the like are continuously proposed. Meanwhile, with the improvement of the performance of the "ghost" imaging, various applications based on the "ghost" imaging are realized. A large number of researches show that the ghost imaging can be widely applied to the fields of military affairs, encryption, laser radar and the like.
Klyshko, the last 80 centuries, proposed quantum-associative imaging schemes based on the entanglement behavior of spontaneous parametric down-conversion photon pairs. In 1995, Pittman et al first experimentally achieved quantum correlation imaging according to Klyshko's theory. In 2002, Bennink et al experimentally realized "ghost" imaging using a classical thermal light source, which proves that "ghost" imaging can be realized using thermal light as well, and causes great bombing in the field of "ghost" imaging. In 2008, Jeffrey h. shapiro et al theoretically proposed that computational "ghost" imaging could be an alternative to traditional "ghost" imaging, and in 2009 Bromberg et al experimentally implemented computational "ghost" imaging, i.e., a single probe optical path "ghost" imaging scheme in which rotating frosted glass was replaced by a computer-controlled Spatial Light Modulator (SLM). In 2010, Ferri F proposes a differential ghost imaging scheme based on thermo-optic ghost imaging, only transmits the differential information of an object during imaging, and reduces the influence of background noise on the quality of a recovered image, thereby improving the imaging quality. In 2015, Zhang Z B et al proposed a method for obtaining the fourier spectrum of an image to achieve single-pixel imaging, which greatly improved the quality of the reconstructed image. Meanwhile, various applications based on the correlated imaging, such as the gradient correlated imaging proposed by Liu F et al in 2015, have been widely studied, and the edge information of the object can be directly obtained without obtaining the image of the object through the gradient correlated imaging.
With the technical development of "ghost" imaging, how to effectively reduce the influence of noise on imaging becomes one of research hotspots. The concept of mean filtering is introduced into the associated imaging process, so that the influence of Gaussian white noise on imaging can be effectively reduced, and an object image with higher imaging quality can be obtained.
In view of the above, it is necessary to provide a denoising method for correlated imaging based on mean filtering to solve the above problems.
Disclosure of Invention
The invention aims to provide a mean filtering-based associated imaging denoising method to reduce the influence of Gaussian white noise on image recovery.
In order to achieve the above object, the present invention provides a denoising method for associated imaging based on mean filtering, the denoising method mainly comprises the following steps:
generating a required Hadamard speckle pattern by using a computer according to the size of an object;
selecting a template of mean filtering, carrying out coordinate position movement on the generated speckles to obtain a speckle pattern containing mean filtering information, wherein the speckle pattern is called a moving speckle pattern, and loading the moving speckle pattern onto a digital micromirror array;
irradiating the digital micromirror array by a light source, and modulating by the digital micromirror array to obtain mobile Hadamard speckles required by associated imaging; sequentially irradiating the moving Hadamard speckles onto an object to be detected, and receiving an optical signal acquired from the object to be detected by using a barrel detector through transmission or reflection to obtain the numerical value of the barrel detector of each Hadamard speckle;
and step four, accumulating the values of all the Hadamard speckle bucket detectors to obtain a total value of the bucket detectors, and performing second-order correlation on the total value of the bucket detectors and the unmoved Hadamard speckle patterns to recover the image of the measured object.
As a further improvement of the invention, in the step one, the Hadamard speckle pattern is Sk(xi,yj) Where k denotes the kth hadamard speckle, (x)i,yj) Indicating the coordinate position.
As a further improvement of the present invention, the template of the mean filtering is 3 × 3, for example, to generate 9 moving speckle patterns, and the relationship between the 9 moving speckle patterns is:
wherein
The mth shifted pattern for the kth hadamard speckle pattern.
As a further improvement of the present invention, in step three, the values of the bucket detector are:
As a further improvement of the present invention, the total bucket detector values in step four are:
wherein Sk(xi,yj) For the kth Hadamard speckle pattern, R (x)i,yj) For the object to be measured, R (x)i,yj+1) For the object to be measured to be displaced by one bit, R (x), downwards in the y-directioni,yj-1) For the object to be measured to be shifted one position in the y-direction, R (x)i-1,yj) For the object to be measured to be shifted by one shift, R (x), to the right in the x directioni+1,yj) For the object to be measured to be shifted to the left by one position in the x direction, R (x)i+1,yj+1) For the object to be measured to be displaced by one position in the southwest direction, R (x)i-1,yj-1) For the object to be measured to be shifted by one bit in the northeast direction, R (x)i+1,yj-1) For the object to be measured to be shifted by one position in the northwest direction, R (x)i-1,yj+1) The object to be measured is shifted by one position in the southeast direction.
As a further improvement of the present invention, the fourth step further comprises: and the optical signal generated after the object to be detected is transmitted or reflected is converged by the lens and then received by the bucket detector.
As a further improvement of the invention, the bucket detector is a bucket detector without spatial resolution capability.
As a further improvement of the present invention, in step four, the image of the object to be measured is:
wherein, BkThe total value of the barrel detector for all the moving scattered spots of the Hadamard speckle to irradiate on the object to be measured, Sk(xi,yj) Is the k-th hadamard speckle pattern,<·>the set is statistically averaged.
The invention has the beneficial effects that: the method is different from the traditional correlation imaging method, introduces the concept of mean filtering into the correlation imaging process, can effectively reduce the influence of Gaussian white noise on imaging, and simultaneously denoises and images.
Drawings
FIG. 1 is a flowchart of a denoising method of mean filtering-based correlated imaging according to the present invention.
Fig. 2 is a graph of the simulation results of pictures "double slit" and "Ghost" using the second order correlation function and mean filtering as the recovery algorithm, consistent with the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the present invention discloses a denoising method for associated imaging based on mean filtering, which mainly comprises the following steps:
generating a required Hadamard speckle pattern by using a computer according to the size of an object;
selecting a template of mean filtering, carrying out coordinate position movement on the generated speckles to obtain a speckle pattern containing mean filtering information, wherein the speckle pattern is called a moving speckle pattern, and loading the moving speckle pattern onto a digital micromirror array;
irradiating the digital micromirror array by a light source, and modulating by the digital micromirror array to obtain mobile Hadamard speckles required by associated imaging; sequentially irradiating the moving Hadamard speckles onto an object to be detected, and receiving an optical signal acquired from the object to be detected by using a barrel detector through transmission or reflection to obtain the numerical value of the barrel detector of each Hadamard speckle;
and step four, accumulating the values of all the Hadamard speckle bucket detectors to obtain a total value of the bucket detectors, and performing second-order correlation on the total value of the bucket detectors and the unmoved Hadamard speckle patterns to recover the image of the measured object.
Specifically, the computer generates the desired speckle without shifting, and then shifts the speckle to generate the hadamard speckle S needed for simulationk(xi,yj) Where k denotes the kth hadamard speckle, (x)i,yj) Indicating the coordinate position.
Then the generated moving speckles are used for irradiating an object, after the object generates optical signals after transmission and/or reflection, the optical signals are focused by a lens and then received by a barrel detector without space resolution capability, and the numerical value of the obtained barrel detector is as follows:
wherein R (x)i,yj) Representing the distribution of the objects to be measured.
The total bucket detector values are:
the object and the speckles are in relative motion, so that the movement of the object can be regarded as the movement of the speckles.
The template of the mean filtering in the present invention is 3 × 3 template mean filtering, so that 9 moving speckles (including speckle which is moving and whose center is unknown) need to be generated, and their relationship is as follows:
after all the randomly moving speckles are illuminated on the object and the bucket detector receives all the intensity values, we can formulate the above process as:
wherein, BkThe total bucket detector value for all k-th hadamard speckles illuminated on the object to be measured.
The restored image of the object can be directly obtained by using the second-order correlation function, and the theoretical process is shown as follows:
wherein B iskThe total value of the bucket detector irradiated on the object to be measured for all the displacement speckles of the kth Hadamard speckle, Sk(xi,yj) Is the k-th hadamard speckle pattern,<·>the set is statistically averaged.
Through the theoretical process, the mean filtering is applied to the correlation imaging, the image of the object can be obtained according to the second-order correlation function, and theoretical derivation shows that the method can reduce the influence of noise on the imaging to a certain extent.
FIG. 2 is a graph of a simulation result of a denoising method for mean-value filtering-based correlated imaging. The present invention verifies the "double slit" and "Ghost" images. The specific simulation process is as follows: the method comprises the steps of firstly generating nine sets of moving Hadamard speckles by using a computer, then irradiating an unknown object by the nine sets of Hadamard speckles, then converging light transmitted by the object by using a lens, receiving the light by using a barrel detector without any spatial resolution, and finally restoring an image of the unknown object by using a second-order correlation function according to the sum of the Hadamard speckles and the barrel detector.
Fig. 2 shows a graph of simulation results of "double slit" and "Ghost", which include simulation results using the mean filtering method and simulation results without the mean filtering method. Meanwhile, in order to compare recovery results obtained when different recovery algorithms are used, a peak signal to noise ratio (PSNR) is used as an evaluation standard, and the larger the peak signal to noise ratio and the structural similarity are, the better the image recovery effect obtained by using the recovery algorithm is, and the closer the recovery result is to a real image. The simulation result is calculated, and the PSNR of a restored image obtained by mean value-free filtering is 13.09dB for a double-slit image. The PSNR of the restored image obtained by using the average filtering is 15.8 dB; for "Ghost," the PSNR of the restored image obtained without mean filtering was 13.03dB, and the PSNR of the restored image obtained using mean filtering was 15.44 dB. As can be seen from the simulation results, compared with the results without mean filtering, the recovery results of the mean filtering algorithm are clearer, i.e., the recovery results have better quality.
In summary, after the associated imaging denoising method based on mean filtering is adopted, the peak signal-to-noise ratio (PSNR) of the restored image is greatly improved compared with the restoration result without mean filtering, and the restored result is clearer. By combining the analysis, the influence of Gaussian white noise on imaging can be effectively reduced by applying the mean filtering to the associated imaging.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. A denoising method of associated imaging based on mean value filtering is characterized in that the denoising method mainly comprises the following steps:
generating a required Hadamard speckle pattern by using a computer according to the size of an object;
selecting a template of mean filtering, carrying out coordinate position movement on the generated speckles to obtain a speckle pattern containing mean filtering information, wherein the speckle pattern is called a moving speckle pattern, and loading the moving speckle pattern onto a digital micromirror array;
irradiating the digital micromirror array by a light source, and modulating by the digital micromirror array to obtain mobile Hadamard speckles required by associated imaging; sequentially irradiating the moving Hadamard speckles onto an object to be detected, and receiving an optical signal acquired from the object to be detected by using a barrel detector through transmission or reflection to obtain the numerical value of the barrel detector of each Hadamard speckle;
and step four, accumulating the values of all the Hadamard speckle bucket detectors to obtain a total bucket detector value, and performing second-order correlation on the total bucket detector value and the unmoved Hadamard speckle patterns to recover the image of the measured object.
2. The mean filtering-based denoising method for correlated imaging according to claim 1, wherein: in the step one, the Hadamard speckle pattern is Sk(xi,yj) Where k denotes the kth hadamard speckle, (x)i,yj) Indicating the coordinate position.
3. The method of claim 1, wherein the template of the mean filtering is a 3 x 3 template of the mean filtering, 9 moving speckle patterns are generated, and the relationship between the 9 moving speckle patterns is:
wherein
The mth shifted pattern for the kth hadamard speckle pattern.
4. The mean filtering-based denoising method for correlated imaging according to claim 1, wherein the bucket detector values in step three are:
5. The method of denoising mean filtering based correlation imaging according to claim 1, wherein the total bucket detector values in step four are:
wherein Sk(xi,yj) For the kth Hadamard speckle pattern, R (x)i,yj) For the object to be measured, R (x)i,yj+1) For the object to be measured to be displaced by one bit, R (x), downwards in the y-directioni,yj-1) For shifting the object to be measured by one bit in the y-direction, R (x)i-1,yj) For the object to be measured to be shifted by one shift, R (x), to the right in the x directioni+1,yj) For the object to be measured to be shifted to the left by one position in the x direction, R (x)i+1,yj+1) For the object to be measured to be displaced by one position in the southwest direction, R (x)i-1,yj-1) For the object to be measured to be shifted by one bit in the northeast direction, R (x)i+1,yj-1) For the object to be measured to be shifted by one position in the northwest direction, R (x)i-1,yj+1) The object to be measured is shifted by one position in the southeast direction.
6. The mean filtering-based denoising method for correlated imaging according to claim 1, wherein the fourth step further comprises: and the optical signal generated after the transmission or reflection of the object to be detected is converged by the lens and then received by the bucket detector.
7. The mean filtering-based denoising method for correlated imaging according to claim 6, wherein: the bucket detector is a bucket detector without space resolution capability.
8. The mean filtering-based denoising method for correlated imaging according to claim 1, wherein in step four, the image of the object to be measured is:
wherein, BkThe total value of the barrel detector irradiated on the object to be measured for the moving speckles of all the Hadamard speckles, Sk(xi,yj) Is the k-th hadamard speckle pattern,<·>the set is statistically averaged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111134437.0A CN114049263B (en) | 2021-09-27 | 2021-09-27 | Correlated imaging denoising method based on mean value filtering |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111134437.0A CN114049263B (en) | 2021-09-27 | 2021-09-27 | Correlated imaging denoising method based on mean value filtering |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114049263A true CN114049263A (en) | 2022-02-15 |
| CN114049263B CN114049263B (en) | 2024-05-03 |
Family
ID=80204844
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111134437.0A Active CN114049263B (en) | 2021-09-27 | 2021-09-27 | Correlated imaging denoising method based on mean value filtering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114049263B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150070472A1 (en) * | 2013-09-11 | 2015-03-12 | National Taiwan University | Measuring apparatus for three-dimensional profilometry and method thereof |
| CN109901190A (en) * | 2019-03-07 | 2019-06-18 | 南京邮电大学 | Relevance imaging method based on linear regression |
| CN110243398A (en) * | 2019-06-27 | 2019-09-17 | 南京邮电大学 | A kind of relevance imaging method of the phase object based on relevant detection |
-
2021
- 2021-09-27 CN CN202111134437.0A patent/CN114049263B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150070472A1 (en) * | 2013-09-11 | 2015-03-12 | National Taiwan University | Measuring apparatus for three-dimensional profilometry and method thereof |
| CN109901190A (en) * | 2019-03-07 | 2019-06-18 | 南京邮电大学 | Relevance imaging method based on linear regression |
| CN110243398A (en) * | 2019-06-27 | 2019-09-17 | 南京邮电大学 | A kind of relevance imaging method of the phase object based on relevant detection |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114049263B (en) | 2024-05-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Meda et al. | Photon-number correlation for quantum enhanced imaging and sensing | |
| CN110243398B (en) | Coherent detection-based phase object correlation imaging method | |
| US11368608B2 (en) | Compressed sensing based object imaging system and imaging method therefor | |
| CN109242808B (en) | Associated imaging method based on fractional Fourier transform | |
| CN109901190B (en) | Correlation imaging method based on linear regression | |
| CN109520619B (en) | Correlated imaging spectral camera based on non-Rayleigh speckle field and imaging method thereof | |
| Wu et al. | Resolution enhancement of long-range imaging with sparse apertures | |
| CN109115681B (en) | Stable quantum sparse imaging system and method | |
| Liu et al. | Spectral ghost imaging camera with super-Rayleigh modulator | |
| Chen et al. | Computational ghost imaging with uncertain imaging distance | |
| CN115242934A (en) | Noise phagocytosis ghost imaging with depth information | |
| Yu et al. | Hadamard-Haar (2D) dual domain ghost imaging | |
| Guan et al. | Source separation and noise reduction in single-pixel imaging | |
| Ljubenović et al. | Beam-shape effects and noise removal from thz time-domain images in reflection geometry in the 0.25–6 thz range | |
| US11412118B2 (en) | Electromagnetic wave phase/amplitude generation device, electromagnetic wave phase/amplitude generation method, and electromagnetic wave phase/amplitude generation program | |
| Chen et al. | Enhancing speckle statistics for imaging inside scattering media | |
| CN114049263B (en) | Correlated imaging denoising method based on mean value filtering | |
| Guo et al. | Ghost imaging of the low or high frequency based on the corresponding spatial-frequency of the reference pattern | |
| Zhang et al. | Fourier single-pixel imaging based on lateral inhibition for low-contrast scenes | |
| CN110716211B (en) | Iterative filtering associated imaging method | |
| Proppe et al. | 3d-2d neural nets for phase retrieval in noisy interferometric imaging | |
| Cassina et al. | Speckled-speckle field as a resource for imaging techniques | |
| Soleimani et al. | The study of computational ghost imaging in presence of atmospheric turbulence | |
| Hu et al. | Fourier ptychographic layer-based imaging of hazy environments | |
| Wang et al. | Strongly robust computational ghost imaging based on nearest neighbor filtering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |