CN113709325A - Single-pixel imaging method based on Hadamard frequency domain transformation matrix threshold filtering - Google Patents
Single-pixel imaging method based on Hadamard frequency domain transformation matrix threshold filtering Download PDFInfo
- Publication number
- CN113709325A CN113709325A CN202110817757.XA CN202110817757A CN113709325A CN 113709325 A CN113709325 A CN 113709325A CN 202110817757 A CN202110817757 A CN 202110817757A CN 113709325 A CN113709325 A CN 113709325A
- Authority
- CN
- China
- Prior art keywords
- matrix
- frequency domain
- hadamard
- light source
- threshold filtering
- 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
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
- H04N23/81—Camera processing pipelines; Components thereof for suppressing or minimising disturbance in the image signal generation
-
- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G02B26/00—Optical devices or arrangements for the control of light using movable or deformable optical elements
- G02B26/08—Optical devices or arrangements for the control of light using movable or deformable optical elements for controlling the direction of light
- G02B26/0816—Optical devices or arrangements for the control of light using movable or deformable optical elements for controlling the direction of light by means of one or more reflecting elements
- G02B26/0833—Optical devices or arrangements for the control of light using movable or deformable optical elements for controlling the direction of light by means of one or more reflecting elements the reflecting element being a micromechanical device, e.g. a MEMS mirror, DMD
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/64—Computer-aided capture of images, e.g. transfer from script file into camera, check of taken image quality, advice or proposal for image composition or decision on when to take image
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Optics & Photonics (AREA)
- Image Processing (AREA)
Abstract
A single-pixel imaging method based on Hadamard frequency domain transform matrix threshold filtering. The method obtains a transform matrix sequence of a frequency domain of a Hadamard modulation matrix sequence by performing discrete cosine transform on the Hadamard modulation matrix sequence. Low-pass filtering the transformed matrix sequence in the frequency domain, and inversely transforming the matrix sequence back to the space domain. And then, modulating and sampling the target in a single-pixel imaging system by using the new matrix, and finally, reconstructing a target image at an ultralow sampling rate by using a compressed sensing algorithm. The method is based on the traditional passive single-pixel imaging system, has a simple structure, is easy to operate, and can recover a clear image of a measured object at a sampling rate lower than 10%. Compared with other modulation methods such as Hadamard and the like, the method has the advantage that the required sampling rate is lower under the condition of the same imaging quality and object sparsity. Therefore, the method is expected to have wide application value in the fields of remote sensing imaging and the like.
Description
Technical Field
The invention relates to the technical field of single-pixel imaging, in particular to a single-pixel imaging method based on Hadamard frequency domain transform matrix threshold filtering.
Background
The traditional single-pixel imaging method utilizes a Hadamard matrix or a random matrix as a measurement matrix, and has the disadvantages of poor imaging quality and low signal-to-noise ratio under a low sampling rate. The single-pixel imaging method based on Hadamard matrix optimization sorting can effectively solve the problem, but the situation that the target object is difficult to reconstruct under the ultra-low sampling rate still exists, and the imaging efficiency is seriously reduced.
Disclosure of Invention
The invention aims to solve the technical problem of providing a single-pixel imaging method based on Hadamard frequency domain transform matrix threshold filtering, which utilizes high-frequency and low-frequency components with clear frequency domains to sample only in low-frequency information, thereby effectively improving the imaging efficiency.
In order to achieve the purpose, the invention adopts the following technical scheme:
a single-pixel imaging method based on Hadamard frequency domain transform matrix threshold filtering comprises the following steps:
1. pretreatment:
1.1, generating a group of orthogonal Hadamard matrixes H by a computer, and transforming the H matrixes from a space domain to a frequency domain by utilizing discrete cosine transform, Fourier transform or wavelet transform, wherein the transformed matrixes are H1;
1.2 matching the matrix H in the frequency domain1Threshold filtering is carried out, high-frequency coefficients are abandoned, low-frequency coefficients are reserved, inverse discrete cosine transform is utilized to return to a space domain, and a matrix after transformation is H2;
1.3 finding H in step 1.22Minimum value a, maximum value b of the matrix let H2The matrix is added with | a | and divided by | a | + | b |, the transformed matrix is
1.4, mixing H obtained in the step 1.33Rounding each matrix element of the matrix, and reserving two decimal places to obtain a matrix H4Then adding H4Loading the matrix into a digital micromirror device;
2. imaging processing:
2.1, covering the light spots on an object by a light source through a collimation and beam expansion system;
2.2, light reflected by the object is imaged on the digital micromirror device through the imaging lens, and the total light intensity S reflected by the object modulated by the digital micromirror device is collected by the bucket detector through the collecting lens;
2.3 with H2Modulating the matrix to reconstruct the object, processing the data collected by the barrel detector, namely the total light intensity S, wherein the processed data is
S1=S*(a|+|b|)-|a|*T(x,y),
Where T (x, y) is the reflection function of the object and S is1Transmitting the data to a computer;
2.4, utilization data S1Sum matrix H2And performing coincidence operation to reconstruct a target image in the coincidence measurement system.
The imaging system used by the single-pixel imaging method based on Hadamard frequency domain transform matrix threshold filtering comprises a light source, a target object, an imaging lens, a digital micromirror device, a collecting lens, a bucket detector and a coincidence measurement system; the light source covers the light spot on the object; the light reflected by the object is imaged on the digital micromirror device through the imaging lens; the bucket detector collects the total light intensity reflected by the object modulated by the digital micromirror device through a collecting lens; said H2Matrix sum S1The data is transmitted to a coincidence measurement system.
The light source is a helium neon laser light source, a thermal light source, a natural light source or an artificial pseudo-thermal light source.
The bucket detector is used for collecting total light intensity, and the total light intensity is S ═ I (x ^ I)1)dx1Wherein I (x)1) Is represented by x1The intensity of the light at.
In the step 2.4, the algorithm used for the coincidence operation is a compressed sensing algorithm or a traditional second-order correlation algorithm.
The beneficial effects created by the invention are as follows: the method comprises the steps that a computer generates an orthogonal Hadamard matrix, the orthogonal Hadamard matrix is converted into a frequency domain through discrete cosine transform, high-frequency coefficients are filtered out and then inversely converted into a space domain, finally, the matrix element value of the orthogonal Hadamard matrix is converted into a value between 0 and 1 and loaded to a digital micro-mirror device to be used for modulating a target object, a bucket detector detects the total light intensity reflected by the target and transmits the total light intensity to the computer, the computer conducts image reconstruction by using a new modulation matrix which is filtered through a frequency domain threshold based on the Hadamard modulation matrix, and the algorithm used for reconstruction is a TVAL3 algorithm, a compression sensing algorithm and can also be a traditional association algorithm or other compression sensing algorithms; according to the high-frequency and low-frequency components with clear frequency domains, sampling is only carried out at the low-frequency information position, and the imaging efficiency is effectively improved.
Drawings
FIG. 1: is a functional block diagram of an imaging system used in the creation of the present invention;
1. a light source; 2. a target object; 3. an imaging lens; 4. a digital micromirror device; 5. a collection lens; 6. a bucket detector; 7. in line with the measurement system.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments.
Example 1: a single-pixel imaging method based on Hadamard frequency domain transform matrix threshold filtering comprises the following steps:
1. pretreatment:
1.1, generating a group of orthogonal Hadamard matrixes H by a computer, and transforming the H matrixes from a space domain to a frequency domain by utilizing discrete cosine transform, Fourier transform or wavelet transform, wherein the transformed matrixes are H1. This can be achieved by using discrete cosine transform, fourier transform or wavelet transform, which works best when discrete cosine transform is used.
1.2 matching the matrix H in the frequency domain1Threshold filtering is carried out, high-frequency coefficients are abandoned, low-frequency coefficients are reserved, inverse discrete cosine transform is utilized to return to a space domain, and a matrix after transformation is H2. And low-frequency information is left, and the high-frequency information is abandoned, so that the contour of the target can be reconstructed more easily, and the reconstruction of the image of the object 2 under the low sampling rate is facilitated.
1.3 finding H in step 1.22Minimum value a, maximum value b of the matrix let H2The matrix is added with | a | and divided by | a | + | b |, the transformed matrix isH2The elements of the matrix have a large number of negative values and the digital micromirror device 4 cannot read, let H2The matrix is added with | a | and divided by | a | + | b | to become H3Matrix, can be H2The value of the matrix element is between 0 and 1, so that the converted H is convenient3The matrix is loaded onto a digital micromirror device 4, where the modulation device can also be other spatial light modulators.
1.4, mixing H obtained in the step 1.33Rounding each matrix element of the matrix, and reserving two decimal places to obtain a matrix H4Then adding H4The matrix is loaded into the digital micromirror device 4. H3The matrix has 15 decimal places, and 2 decimal places are reserved for being read by the digital micro-mirror device 4, and the modulation device can also be a spatial light modulator.
2. Imaging processing:
2.1, covering the light spots on the object 2 by the light source 1 through a collimation and beam expansion system. The light source 1 may be a helium neon laser source, a thermal light source, a natural light source, or an artificial pseudo thermal light source.
2.2, the light reflected by the object 2 is imaged on the digital micromirror device 4 through the imaging lens 3, and the total light intensity S reflected by the object modulated by the digital micromirror device 4 is collected by the bucket detector 6 through the collecting lens 5. The instrument for collecting the total light intensity is a barrel detector 6 without spatial resolution capability or an area array camera with spatial resolution capability, and the total collected light intensity is S ═ I (x ═ I)1)dx1Wherein I (x)1) Is represented by x1The intensity of the light at.
2.3 with H2Modulating the matrix to reconstruct the object 2, processing the data collected by the barrel detector 6, namely the total light intensity S, and obtaining the processed data
S1=S*(|a|+|b|)-|a|*T(x,y),
Where T (x, y) is the reflection function of the object 2 and S is1And transmitting to the computer.
2.4, utilization data S1Sum matrix H2And performing coincidence operation to reconstruct a target image in the coincidence measurement system 7. The algorithm used by the coincidence operation is a compressed sensing algorithm and can also be a traditional second-order correlation algorithm.
Specifically, when the algorithm used is the TVAL3 algorithm, the solving method is as follows:
tMN×1=argmin||t'MN×1||1s.t.||YK×1-φK×MNψt'MN×1||2<ε,
in the formula, phiK×MNIs a measurement matrix; noise is a random Noise term of the environment and the detector electric signals;
ψ=[ψ1,ψ1,ψ1…,ψMN]is a specific sparse radical. Such as Noiselet, Dct, etc.; m and N are the pixel numbers of rows and columns of the image to be restored; the total number of measurements; l |. electrically ventilated margin1And |. non conducting phosphor2Respectively represent l1Norm sum l2A norm; with epsilon being the amplitude of the noiseA boundary.
The imaging system used in the above-described imaging method is shown in fig. 1. The device comprises a light source 1, a target object 2, an imaging lens 3, a digital micromirror device 4, a collecting lens 5, a barrel detector 6 and a coincidence measurement system 7; the light source 1 covers the light spot on the object 2; the light reflected by the object 2 is imaged on a digital micromirror device 4 through an imaging lens 3; the bucket detector 6 collects the total light intensity reflected by the object modulated by the digital micromirror device 4 through the collecting lens 5; said H2Matrix sum S1The data is transmitted to the coincidence measurement system 7.
Thus, it should be understood by those skilled in the art that while an exemplary embodiment of the present invention has been illustrated and described in detail herein, many other variations and modifications can be made, which are consistent with the principles of the invention, from the disclosure herein, without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (6)
1. A single-pixel imaging method based on Hadamard frequency domain transform matrix threshold filtering is characterized by comprising the following steps:
1. pretreatment:
1.1, generating a group of orthogonal Hadamard matrixes H by a computer, and transforming the H matrixes from a space domain to a frequency domain by utilizing discrete cosine transform, Fourier transform or wavelet transform, wherein the transformed matrixes are H1;
1.2 matching the matrix H in the frequency domain1Threshold filtering is carried out, high-frequency coefficients are abandoned, low-frequency coefficients are reserved, inverse discrete cosine transform is utilized to return to a space domain, and a matrix after transformation is H2;
1.3 finding H in step 1.22Minimum value a, maximum value b of the matrix let H2The matrix is added with | a | and divided by | a | + | b |, the transformed matrix is
1.4, will stepH obtained in step 1.33Rounding each matrix element of the matrix, and reserving two decimal places to obtain a matrix H4Then adding H4Loading the matrix into a digital micromirror device (4);
2. imaging processing:
2.1, covering the light spots on the object (2) by the light source (1) through a collimation and beam expansion system;
2.2, light reflected by the object (2) is imaged on the digital micromirror device (4) through the imaging lens (3), and the total light intensity S reflected by the object modulated by the digital micromirror device (4) is collected by the bucket detector (6) through the collecting lens (5);
2.3 with H2Modulating the matrix to reconstruct the object (2), processing the data collected by the barrel detector (6), namely the total light intensity S, and obtaining the processed data
S1=S*(|a|+|b|)-|a|*T(x,y),
Wherein T (x, y) is the reflection function of the object (2) and S is1Transmitting the data to a computer;
2.4, utilization data S1Sum matrix H2And performing coincidence operation to reconstruct a target image in the coincidence measurement system (7).
2. The imaging system for use in the Hadamard frequency domain transform matrix threshold filtering based single pixel imaging method as claimed in claim 1, characterized by comprising a light source (1), a target object (2), an imaging lens (3), a digital micromirror device (4), a collecting lens (5), a bucket detector (6) and a coincidence measurement system (7); the light source (1) covers the light spot on the object (2); the light reflected by the object (2) is imaged on the digital micromirror device (4) through the imaging lens (3); the bucket detector (6) collects the total light intensity reflected by the object modulated by the digital micromirror device (4) through the collecting lens (5); said H2Matrix sum S1The data are transmitted to a coincidence measurement system (7).
4. The Hadamard frequency domain transform matrix threshold filtering based single-pixel imaging method of claim 1, wherein: the light source (1) is a helium neon laser light source, a thermal light source, a natural light source or an artificial pseudo-thermal light source.
5. The method of claim 1 for single-pixel imaging based on Hadamard frequency domain transform matrix threshold filtering, characterized in that: the barrel detector (6) is used for collecting total light intensity, and the total light intensity is S ═ I (x ═ I)1)dx1Wherein I (x)1) Is represented by x1The intensity of the light at.
6. The Hadamard frequency domain transform matrix threshold filtering based single-pixel imaging method of claim 1, wherein: in the step 2.4, the algorithm used for the coincidence operation is a compressed sensing algorithm or a traditional second-order correlation algorithm.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110817757.XA CN113709325B (en) | 2021-07-20 | 2021-07-20 | Single-pixel imaging method based on Hadamard frequency domain transformation matrix threshold filtering |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110817757.XA CN113709325B (en) | 2021-07-20 | 2021-07-20 | Single-pixel imaging method based on Hadamard frequency domain transformation matrix threshold filtering |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113709325A true CN113709325A (en) | 2021-11-26 |
CN113709325B CN113709325B (en) | 2023-09-15 |
Family
ID=78649046
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110817757.XA Active CN113709325B (en) | 2021-07-20 | 2021-07-20 | Single-pixel imaging method based on Hadamard frequency domain transformation matrix threshold filtering |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113709325B (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115372991A (en) * | 2022-09-07 | 2022-11-22 | 辽宁大学 | Spectrum super-resolution Shan Xiangsu imaging method based on compressed sensing |
CN117091700A (en) * | 2023-08-31 | 2023-11-21 | 山东大学 | System for realizing hyperspectral imaging by using non-array detector |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104796609A (en) * | 2015-04-17 | 2015-07-22 | 南京理工大学 | Large-visual-field high-resolution microscopic imaging method based on optimal Hadamard codes |
CN108024037A (en) * | 2017-11-28 | 2018-05-11 | 华中科技大学 | Hadamard matrixes perceive imaging system and its imaging method |
CN109035282A (en) * | 2018-08-02 | 2018-12-18 | 吉林工程技术师范学院 | The thresholding method of Hadamard coded modulation relevance imaging |
CN112789856A (en) * | 2018-10-08 | 2021-05-11 | 高通股份有限公司 | Transform domain filtering based quantization artifact suppression in video encoding/decoding |
-
2021
- 2021-07-20 CN CN202110817757.XA patent/CN113709325B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104796609A (en) * | 2015-04-17 | 2015-07-22 | 南京理工大学 | Large-visual-field high-resolution microscopic imaging method based on optimal Hadamard codes |
CN108024037A (en) * | 2017-11-28 | 2018-05-11 | 华中科技大学 | Hadamard matrixes perceive imaging system and its imaging method |
CN109035282A (en) * | 2018-08-02 | 2018-12-18 | 吉林工程技术师范学院 | The thresholding method of Hadamard coded modulation relevance imaging |
CN112789856A (en) * | 2018-10-08 | 2021-05-11 | 高通股份有限公司 | Transform domain filtering based quantization artifact suppression in video encoding/decoding |
Non-Patent Citations (1)
Title |
---|
MARCO F. DUARTE等: "Single-Pixel Imaging via Compressive Sampling", IEEE SIGNAL PROCESSING MAGAZINE * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115372991A (en) * | 2022-09-07 | 2022-11-22 | 辽宁大学 | Spectrum super-resolution Shan Xiangsu imaging method based on compressed sensing |
CN117091700A (en) * | 2023-08-31 | 2023-11-21 | 山东大学 | System for realizing hyperspectral imaging by using non-array detector |
CN117091700B (en) * | 2023-08-31 | 2024-02-06 | 山东大学 | System for realizing hyperspectral imaging by using non-array detector |
Also Published As
Publication number | Publication date |
---|---|
CN113709325B (en) | 2023-09-15 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rousset et al. | Adaptive basis scan by wavelet prediction for single-pixel imaging | |
CN113709325A (en) | Single-pixel imaging method based on Hadamard frequency domain transformation matrix threshold filtering | |
US20030068097A1 (en) | Adaptive mean estimation and normalization of data | |
CN114387164A (en) | Terahertz single-pixel super-resolution imaging method and system | |
Healy et al. | Compression at the physical interface | |
CN112203068A (en) | Single-pixel imaging method, system, device and medium | |
CN115824412A (en) | Full-polarization spectrum imaging device and detection method | |
CN114859377B (en) | Method and equipment for capturing single-pixel imaging of moving target in real time | |
CN112153254B (en) | Two-step phase-shift single-pixel imaging method based on base map | |
CN111915697B (en) | One-step harmonic single-pixel imaging method | |
Conde et al. | Low-light image enhancement for multiaperture and multitap systems | |
Pan | Uniform full-information image matching using complex conjugate wavelet pyramids | |
CN112616050B (en) | Compression imaging classification method and system | |
Zhao et al. | Effects of lossy compression on lesion detection: predictions of the nonprewhitening matched filter | |
Fraser et al. | Principles of tomography in image data compression | |
Elaveini et al. | Hybrid Transform Based Compressive Sensing of Image with Better Quality Using Denoising Convolution Neural Network | |
Bobin et al. | Compressed sensing in astronomy and remote sensing: a data fusion perspective | |
Thakkar et al. | Single pixel measurements through fourier phase shifting in imaging system | |
Rousset et al. | Adaptive acquisitions in biomedical optical imaging based on single pixel camera: Comparison with compressive sensing | |
Ives | On the compression of synthetic aperture radar imagery | |
CN117876837A (en) | Near infrared single-pixel imaging method and system based on depth expansion network | |
Marks et al. | Computational photography and compressive holography | |
CN114694026A (en) | Hyperspectral rapid target detection method based on compressed spectrum imaging | |
Raffoul et al. | Computationally efficient and high fidelity log-based demosaicking for degree of linear polarization | |
CN113936126A (en) | Image reconstruction method and image reconstruction device based on single-pixel imaging |
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 | ||
GR01 | Patent grant |