CN103916600B - Coding template multi-target super-resolution imaging system and method - Google Patents
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Abstract
The invention relates to a coding template multi-target super-resolution imaging system and method. The system comprises a telescope unit, an imaging lens unit, a light beam expanding collimation unit, a digital micro-array reflector unit, a converging lens unit, photoelectric detector units, a compaction algorithm module and a decoding and sparse linear algorithm module. Incident light of the telescopic unit passes through the imaging lens unit and the light beam expanding collimation unit, then the light is subjected to beam splitting through a first digital micro-array reflector, image coding is carried out through a second digital micro-array reflector, light field random space modulation is carried out through a third digital micro-array reflector, imaging is carried out through an imaging lens, convergence is carried out through the converging lens unit, the light enters the multiple photoelectric detectors, corresponding coding images are reestablished through the compaction algorithm module, low-resolution images are obtained after coding image decoding, a sparse linear equation system of each pixel gray level value of all the low-resolution images is listed, and the least square solutions of the sparse linear equation systems are super-resolution images.
Description
Technical Field
The invention relates to the field of super-resolution imaging, in particular to a coding template multi-target super-resolution imaging system and method based on compressed sensing.
Background
In the fortieth of the last century, M.J.E.Golay firstly proposed the 'template modulation' modulation idea, and based on the idea, Golay designed a multi-slit template spectrometer and recognized the effect of template modulation. Then, gilrad (gilad) proposes a grating spectrometer which takes a grating made of a fresnel zone plate as a template, and utilizes the orthogonality of fresnel wave functions to realize wavelength modulation, thereby enhancing luminous flux; the default (Mertz) proposes that the light field modulation is realized by a rotating fence method, the radiation spectrum is obtained by utilizing Fourier transform, and the advantages of multiple channels and high flux are achieved; with the further development of research, a simple template spectrometer appears, in 1968, Ibbett, Decker and Harwit research the basic characteristics of a Golay spectrometer, and an intermittent stepping template is proposed to replace a continuous rotating disc; gottlieb researches cyclic codes related to orthogonal binary digital codes, proposes that the cyclic codes can be folded into a two-dimensional array, and Sloane et al propose that Reed-Muller codes are particularly suitable for spectral measurement.
Hadamard transform spectroscopy is a new spectral modulation technique that has evolved at the end of the last century. The technology uses a Hadamard coding template to replace a slit of a traditional dispersive spectrometer, and realizes the high-flux simultaneous measurement of multiple channels and multiple spectral elements. In recent years, with the development of micro-optoelectromechanical technology, the Hadamard spectrum technology becomes one of the research hotspots, and for example, the euclidean telescope which is planned to be transmitted by the european space bureau adopts the technology to realize the observation of spatial astronomy. Compared with the traditional spectrometer, the technology has the following advantages: 1) the light flux is high, and the traditional spectral narrow slit is replaced by the digital microarray reflector; 2) the spectral resolution is high, and the device is not limited by instrument functions generated by a slit like a Fourier spectrometer; 3) the signal-to-noise ratio is high, the modulation and demodulation method effectively inhibits Beijing and interference signals, and useful spectrum signals are relatively improved; 4) the method has high flexibility, spectral information of a specific object is purposefully selected according to an observed object, interference of a background and other objects is reduced, and meanwhile, the method can also be used in the imaging field to realize super-resolution imaging.
Compressed sensing was proposed in 2004 by researchers such as e.j.cans, j.romberg, t.tao and d.l.donoho, and the like, and by the french mathematician Prony of the last century proposed a sparse signal recovery method that estimates the non-zero amplitude and corresponding frequency of a sparse trigonometric polynomial by solving eigenvalue problems; logan originally proposed sparse constraint methods based on L1 norm minimization. The compressed sensing theory developed later is to combine the L1 norm minimization sparse constraint with a random matrix to obtain the best result of sparse signal reconstruction performance, and the compressed sensing is based on the compressibility of signals and realizes the sensing of high-dimensional signals through the uncorrelated observation of low-dimensional space, low resolution and under-Nyquist sampling data. The method is widely applied to the subject fields of information theory, image processing, geoscience, optical/microwave imaging, mode recognition, wireless communication, atmospheric science, geoscience, physical astronomy and the like.
The compressive sensing theory is that sampling and compression are carried out simultaneously, prior knowledge that natural signals can be expressed under a certain sparse basis is well utilized, sub-sampling far lower than the Nyquist/Shannon sampling limit can be realized, and signal information can be reconstructed almost perfectly. The most widely applied technology is single-pixel camera technology, which can use a point detector instead of an area array detector to complete all detection tasks, if the technology is applied to sparse aperture, detection dimension must be reduced, optical noise and electrical noise brought by the area array detector are avoided, moreover, a digital micromirror device DMD is adopted, which is a passive optical element, no noise is brought to signals, a preamplifier is not needed in the aspect of the detector, in addition, the system can also realize high-speed sampling of 23kHz, which cannot be reached by the traditional area array detector, and an additional robust reconstruction algorithm must lead to more potential applications.
Disclosure of Invention
The invention aims to apply the compressive sensing theory to the field of Hadamard transform optical super-resolution imaging, and provides a compressive sensing coding template multi-target super-resolution imaging system and method.
In order to achieve the above object, the present invention provides a multi-target super-resolution imaging system for a coding template, the system comprising: the device comprises a telescope unit, an imaging lens unit, a light beam expanding and collimating unit, a digital micro-array reflector unit, a converging lens, a photoelectric detector, a compression algorithm module and a decoding and sparse linear algorithm module; wherein,
the telescope unit comprises a concave reflector (1), a convex reflector (2) and a reflector (3);
the imaging lens unit includes a first imaging lens (4-1) and a second imaging lens (4-2);
the digital microarray mirror unit comprises a first digital microarray mirror (6-1), a second digital microarray mirror (6-2) and a third digital microarray mirror (6-3);
after imaging through a first imaging lens (4-1), a multi-target image is mapped to the surface of a first digital microarray reflector (6-1) through a light beam expanding collimation lens (5), and background light of a non-target object is reflected out of a subsequent optical system by controlling the first digital microarray reflector (6-1); reflecting the background stray light to the light receiver (7); the first digital micro array reflector (6-1) is controlled to reflect the multi-target object light field to the second digital micro array reflector (6-2), the image is subjected to coding aperture coding and then is incident to the third digital micro array reflector (6-3), after the coded image is randomly optically modulated, the coded image is imaged through a second imaging lens (4-2), then is converged through a converging lens (8) and then is incident to a photoelectric detector (9), a multi-target coded image is reconstructed through a compression algorithm module (10), and then is decoded and processed through a sparse linear algorithm module (11), and decoding the coded image to form a low-resolution image, listing a sparse linear equation set for each pixel gray value of a plurality of low-resolution images obtained by all detectors through the module, and solving a least square solution to obtain a super-resolution image of the multi-target object.
Furthermore, the telescope unit comprises a concave reflector (1), a convex reflector (2) and a reflector (3); wherein the telescope is Galileo telescope, Kepler telescope, Newton telescope or Cassegrain telescope; the telescope unit is a reflection type, refraction type or return type telescope; the telescope unit is a telescope which comprises ultraviolet, visible light and infrared bands in a spectral range.
Further, the first imaging lens (4-1) images incident light of a telescope, and the second imaging lens (4-2) is used for imaging after random spatial light modulation of the coded image; the first digital micro array reflector (6-1) is used for reflecting non-target background light and stray light in the multi-target image to the light receiver (7) and reflecting the effective multi-target image to the second digital micro array reflector (6-2).
Further, the second digital micro array reflector (6-2) is used for carrying out Hadamard coding or fast Hadamard transform algorithm on the multi-target image to code the image; the Hadamard coding adopts an H matrix or an S matrix, wherein the S matrix is an N-order cyclic S matrix and a cyclic S matrix constructed based on an m sequence; (ii) a The order number of the N-order cyclic S matrix is 7, 11, 15, 19, 23 and 27, and the higher the order number is, the higher the resolution is; the digital microarray mirror unit further includes a liquid crystal spatial light modulator.
Furthermore, the third digital micro-array reflector (6-3) is used for imaging the coded image after random modulation through the second imaging lens (4-2) after random spatial light modulation is carried out on the multi-target coded image, and then inputting the imaged coded image into the converging lens (8); the converging lens unit converges an image modulated by the third digital micro-array reflecting lens (6-3) at random to one point by a converging lens (8), then the image is incident to a corresponding photoelectric detector (9), high-flux imaging is realized through the converging lens (8), and the converging lens unit is applied to weak light, ultra-weak light and single photon imaging.
Further, the photoelectric detection unit receives optical signals converged by the corresponding converging lens (8) through a photoelectric detector group (9), and then inputs the optical signals into the corresponding compression algorithm module (10), wherein the photoelectric detector group comprises M point detectors, and each point detector adopts an ultraviolet, visible light, near infrared and infrared ray array photoelectric detector or a single photon detector to detect in an optical spectrum range or ultra-high sensitivity; the single photon detector is ultraviolet, visible light, near infrared and infrared avalanche diode, solid photomultiplier or superconductive single photon detector.
Further, the compression algorithm module (10) implements compressed sensing by using any one of the following algorithms: greedy reconstruction algorithm, matching tracking algorithm MP, orthogonal matching tracking algorithm OMP, basis tracking algorithm BP, LASSO, LARS, GPSR, Bayesian estimation algorithm, magic, IST, TV, StOMP, CoSaMP, LBI, SP, l1_ ls, smp algorithm, SpaRSA algorithm, TwinST algorithm, l1_ ls0Reconstruction algorithm, l1Reconstruction algorithm, l2Reconstruction algorithm, etc., the sparse base can adopt a discrete cosine transform base, a wavelet base, a Fourier transform base, a gradient base or a gabor transform base; m coded images corresponding to the photoelectric detector group are reconstructed by using the compression algorithm module.
Furthermore, the decoding and sparse linear algorithm module (11) decodes the N encoded images to form a low-resolution image, then lists a linear equation set through each pixel gray value of a relevant area in the M images to form a sparse linear equation set, and solves a least square solution to obtain the super-resolution image of the multi-target object.
Furthermore, the third digital micro-array reflector (6-3) and the M photoelectric detector groups (9) are synchronous, each time the micro-mirror array in the third digital micro-array reflector (6-3) is turned over, each independent detector in the photoelectric detector group (9) accumulates and detects all light intensity in the turning time interval, photoelectric signal acquisition and conversion are achieved, and then the light intensity is sent to the corresponding compression algorithm module (10).
The invention also provides a multi-target super-resolution imaging method of the coding template, which comprises the following steps:
step 1), compressing sensed imaging modulation, wherein an incident imaging optical signal is transmitted to a third digital microarray reflector (6-3) after serial optical transformation, and the third digital microarray reflector (6-3) carries out light intensity modulation on reflected light of the third digital microarray reflector by loading a random matrix A;
step 2), compression sampling, wherein the photoelectric detector group (9) samples simultaneously in the time interval of each overturn of the corresponding third digital micro-array reflector (6-3), and the value converted by the photoelectric detector is used as the final measurement value y;
and 3) signal reconstruction, wherein the measured values y of the binary random measurement matrix A and the measured values y are used as input of a compression algorithm module (10), a proper sparse base is selected to enable an imaging x to be represented by a minimum number of coefficients, signal reconstruction is carried out through a compression sensing algorithm, and finally a coded image of a multi-target object is realized.
The invention has the advantages that:
the invention combines the compressed sensing theory and Hadamard transform coding aperture or template, creatively provides a sparse Hadamard transform super-resolution imaging method, has the characteristics of multiple channels, high flux, high signal-to-noise ratio, rapidness and flexibility, is suitable for the imaging mode of conventional light intensity, weak light, ultra-weak light and single photon spectrometer, and is a novel super-resolution imaging mechanism with large dynamic range. The interference of non-observation objects and background light on imaging spectra is reduced by selecting observation target objects, the signal to noise ratio of super-resolution imaging is effectively improved, and the encoding of a hadamard transformation template is realized by adopting a digital micro-array reflector technology, so that multi-channel rapid encoding imaging is realized. Therefore, the method is a novel transformation super-resolution imaging technology. By means of the remarkable advantages, the compressed sensing coding template multi-target super-resolution imaging system must replace the function of an original imaging device, and becomes an important development direction for developing the field of transformation super-resolution optical imaging.
Drawings
FIG. 1 is a schematic structural diagram of a coding template multi-target super-resolution imaging system of the present invention;
1. concave reflector of telescope unit
2. Convex reflector of telescope unit
3. Reflecting mirror
4-1, first imaging lens of imaging lens unit
4-2, second imaging lens of imaging lens unit
5. Light beam expanding and collimating lens
6-1, first digital microarray mirror of digital microarray mirror unit
6-2, second digital microarray mirror of digital microarray mirror unit
6-3, third digital microarray mirror of digital microarray mirror unit
7. Light is reflected to the light receiver
8. Converging lens
9. Photoelectric detector
10. Compression algorithm module
11. Decoding and sparse linear algorithm module
Detailed Description
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
The invention combines the compressive sensing theory and Hadamard transform optics, creatively provides sparse Hadamard transform spectroscopy, has the characteristics of multiple channels, high flux, high signal-to-noise ratio and rapidness and flexibility, is suitable for the imaging modes of conventional light intensity, weak light, ultra-weak light and single photon spectrometers, and is a novel super-resolution optical imaging mechanism with a large dynamic range.
The hyperspectral imaging system based on sparse aperture compression calculation association adopts the principle of Compressive Sensing (CS for short), and can perfectly recover the original signal by a smaller data sampling number (far below the limit of Nyquist/Shannon sampling theorem) in a random sampling mode. Firstly, selecting a proper sparse basis psi by using prior knowledge, so that a point spread function x is transformed by psi to obtain the condition that x' is the most sparse; under the condition of known measurement value vector y, measurement matrix A and sparse basis Ψ, a mathematical model y-A Ψ x ' + e is established, convex optimization is carried out through a compressed sensing algorithm to obtain x ', and then the x ' is obtainedThe inversion is x.
The above is an explanation of the compressive sensing theory algorithm, and the imaging spectrum system of the present invention will be specifically described below in conjunction with the compressive sensing principle.
FIG. 1 is a schematic structural diagram of a coding template multi-target super-resolution imaging system of the present invention, the system includes: the device comprises a telescope unit, an imaging lens unit, a light beam expanding and collimating unit, a digital micro-array reflector unit, a converging lens unit, a photoelectric detector unit, a compression algorithm module and a decoding and sparse linear algorithm module.
The telescope unit consists of a concave reflector 1, a convex reflector 2 and a reflector 3; the imaging lens unit includes a first imaging lens 4-1 and a second imaging lens 4-2; the digital microarray mirror unit includes a first digital microarray mirror 6-1, a second digital microarray mirror 6-2, and a third digital microarray mirror 6-3.
After imaging is carried out through the first imaging lens 4-1, the multi-target image is mapped to the surface of the first digital micro-array reflector 6-1 through the light beam expanding collimating lens 5, and background light of a non-target object is reflected out of a subsequent optical system by controlling the first digital micro-array reflector 6-1; reflecting the background stray light to the light receiver 7; the method comprises the steps of controlling a first digital micro-array reflector 6-1 to reflect a multi-target object light field to a second digital micro-array reflector 6-2, carrying out coding aperture coding on an image through the first digital micro-array reflector 6-1, then inputting the image to a third digital micro-array reflector 6-3, carrying out random optical modulation on the coded image, imaging through a second imaging lens 4-2, then inputting the image to a photoelectric detector 9 after converging through a converging lens 8, reconstructing the multi-target coded image through a compression algorithm module 10, decoding the coded image through a decoding and sparse linear algorithm module 11 to form a low-resolution image, listing a sparse linear equation set for each pixel gray value of a plurality of low-resolution images obtained by all detectors through the module, and solving a least square solution to obtain the multi-target object super-resolution image.
Specifically, the telescope unit consists of a concave reflector 1, a convex reflector 2 and a reflector 3; wherein the telescope unit comprises a Galileo telescope, a Kepler telescope, a Newton telescope, a Cassegrain telescope and the like; the structure can comprise a reflection type telescope, a refraction type telescope, a folding type telescope and the like; including ultraviolet, visible, infrared band telescopes, etc. in the spectral range.
Specifically, the imaging lens unit comprises a first imaging lens 4-1 and a second imaging lens 4-2, wherein the first imaging lens 4-1 is used for imaging incident light of a telescope, and the second imaging lens 4-2 is used for imaging after random spatial light modulation of a coded image; in addition, the lens also comprises a semi-convex lens in ultraviolet, visible light, infrared and other wave bands.
Specifically, the first digital microarray mirror 6-1 of the digital microarray mirror unit reflects the non-target background light and stray light in the multi-target image to the light receiver 7, and reflects the effective multi-target image to the second digital microarray mirror 6-2.
Specifically, the second digital microarray mirror 6-2 of the digital microarray mirror unit performs Hadamard coding on the multi-target image; the Hadamard coding can adopt an H matrix or an S matrix, the S matrix is the best practical coding matrix, generally adopts an N-order cyclic S matrix and a cyclic S matrix constructed based on an m sequence, and can adopt a fast Hadamard transform algorithm to code an image; in addition, the digital microarray mirror unit also comprises other optical spatial modulators such as a liquid crystal spatial light modulator and the like; the order number of the N-order cyclic S matrix may be 7, 11, 15, 19, 23, 27, etc., and the higher the order number, the higher the resolution.
Specifically, after the third digital micro-array reflector 6-3 of the digital micro-array reflector unit performs random spatial light modulation on the multi-target coded image, the randomly modulated coded image is imaged through the second imaging lens 4-2 and then input to the converging lens 8.
Specifically, the converging lens unit converges an image modulated by the third digital micro-array reflection lens 6-3 randomly by the converging lens 8 to a point, and then the image is incident to the corresponding photoelectric detector 9, and high-flux imaging is realized by the converging lens 8, so that the converging lens unit can be applied to the aspects of weak light, ultra-weak light and single photon imaging.
Specifically, the photoelectric detection unit receives an optical signal converged by the corresponding converging lens 8 through a photoelectric detector group 9, and then inputs the optical signal into the corresponding compression algorithm module 10, wherein the photoelectric detector group comprises M point detectors, and each point detector can adopt an ultraviolet, visible light, near infrared and infrared ray array photoelectric detector or a single-photon detector to detect in an optical spectrum range or in an ultrahigh sensitivity; the single photon detector can be ultraviolet, visible light, near infrared and infrared avalanche diodes, solid photomultiplier tubes, superconducting single photon detectors and the like.
Specifically, the compression algorithm module 10 implements compressed sensing by using any one of the following algorithms: greedy reconstruction algorithm, matching tracking algorithm MP, orthogonal matching tracking algorithm OMP, basis tracking algorithm BP, LASSO, LARS, GPSR, Bayesian estimation algorithm, magic, IST, TV, StOMP, CoSaMP, LBI, SP, l1_ ls, smp algorithm, SpaRSA algorithm, TwinST algorithm, l1_ ls0Reconstruction algorithm, l1Reconstruction algorithm, l2Reconstruction algorithm, etc., the sparse basis can adopt discrete cosine transform basis, wavelet basis, Fourier transform basis and gradient basisGabor transformation group, etc.; m coded images corresponding to the photoelectric detector group are reconstructed by using the compression algorithm module.
Specifically, the decoding and sparse linear algorithm module 11 forms a low-resolution image by decoding the N encoded images, then lists a linear equation set by each pixel gray value of a relevant region in the M images to form a sparse linear equation set, and solves a least square solution to obtain a super-resolution image of the multi-target object.
Specifically, the third digital micro-array mirror 6-3 and the M photo-detector groups 9 need to be synchronized, and each independent detector in the photo-detector group 9 accumulates and detects all light intensities within the turning time interval every time the micro-mirror array in the third digital micro-array mirror 6-3 is turned over, so as to realize the photoelectric signal acquisition and conversion, and then sends the photoelectric signals to the corresponding compression algorithm module 10.
The invention combines the compressed sensing theory and Hadamard transform coding aperture or template, creatively provides a sparse Hadamard transform super-resolution imaging method, has the characteristics of multiple channels, high flux, high signal-to-noise ratio, rapidness and flexibility, is suitable for the imaging mode of conventional light intensity, weak light, ultra-weak light and single photon spectrometer, and is a novel super-resolution imaging mechanism with large dynamic range. The interference of non-observation objects and background light on imaging spectra is reduced by selecting observation target objects, the signal to noise ratio of super-resolution imaging is effectively improved, and the encoding of a hadamard transformation template is realized by adopting a digital micro-array reflector technology, so that multi-channel rapid encoding imaging is realized. Therefore, the method is a novel transformation super-resolution imaging technology. By means of the remarkable advantages, the compressed sensing coding template multi-target super-resolution imaging system must replace the function of an original imaging device, and becomes an important development direction for developing the field of transformation super-resolution optical imaging.
The invention also provides a multi-target super-resolution imaging system and a method for the coding template, which comprises the following steps:
step 1), compressing the sensed imaging modulation;
the incident imaging optical signal is transmitted to a third digital micro-array reflector 6-3 after serial optical transformation, and the third digital micro-array reflector 6-3 modulates the light intensity of the reflected light of the third digital micro-array reflector by loading a random matrix A;
step 2), compressing and sampling;
the photoelectric detector group 9 samples simultaneously in the time interval of each overturn of the corresponding digital micro-array reflector 6-3, and the value converted by the photoelectric detector is taken as the final measured value y;
step 3), signal reconstruction;
the measured value y of the binary random measurement matrix A is used as the input of a compression algorithm module 10, a proper sparse base is selected to enable the imaging x to be represented by the least number of coefficients, signal reconstruction is carried out through a compression sensing algorithm, and finally the coded image of the multi-target object is achieved.
In the above technical solution, the core of the compression algorithm is a compressed sensing optimization algorithm, and a key objective function of the compressed sensing optimization algorithm is modified into:
where A is the measurement matrix of the spatial light modulator, Ψ is n × n sparse basis, generally Ψ is an orthogonal matrix, and x' ═ Ψ- 1x, x are the column vector of the original object image matrix after stretching, tau andall are constant coefficients, | ·| non-conducting phosphorpRepresents lpThe norm of the number of the first-order-of-arrival,<·>denotes the sum-and-average, aiIs a p × q matrix loaded during the ith (1 ≦ i ≦ m) modulation on the spatial light modulator, and totally modulates m times, a'iIs aiThe column vector after stretching, A is actually m (a'i)TComposed m × n matrix, ATy is an m × 1 column vector.
The above is a description of the general structure of the compressed sensing-based encoding template multi-target imaging spectroscopy system of the invention, and the specific implementation of each component thereof is further described below.
The digital microarray mirror unit can load information on a one-dimensional or two-dimensional optical data field, is a key device in modern optical fields of real-time optical information processing, adaptive optics, optical calculation and the like, and can change the amplitude or the intensity, the phase, the polarization state and the wavelength of light distribution on space or convert incoherent light into coherent light under the control of an electric drive signal or other signals which change along with time. There are many kinds of modulation, mainly digital micro-mirror devices (DMD), frosted glass, liquid crystal light valves, etc., and the modulation used here is light intensity modulation including amplitude modulation.
The DMD used in this embodiment is an array comprising thousands of micromirrors mounted on hinges (the mainstream DMD is made up of 1024 × 768 array, up to 2048 × 1152), each mirror has a size of 14 μm × 14 μm (or 16 μm × 16 μm) and can turn on/off light of one pixel, the micromirrors are suspended, and each mirror can be electrostatically tilted to about 10-12 ° to both sides (in this embodiment, 12 ° and-12 °) by electronically addressing the memory cell under each mirror with binary plane signals, and these two states are denoted as 1 and 0, corresponding to "on" and "off", respectively, and when the mirror is not in operation, they are in a "parking" state of 0 °.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A coding template multi-target super-resolution imaging system, the system comprising: the device comprises a telescope unit, an imaging lens unit, a light beam expanding and collimating unit, a digital micro-array reflector unit, a converging lens, a photoelectric detector, a compression algorithm module and a decoding and sparse linear algorithm module; wherein,
the telescope unit comprises a concave reflector (1), a convex reflector (2) and a reflector (3);
the imaging lens unit includes a first imaging lens (4-1) and a second imaging lens (4-2);
the digital microarray mirror unit comprises a first digital microarray mirror (6-1), a second digital microarray mirror (6-2) and a third digital microarray mirror (6-3);
after imaging through a first imaging lens (4-1), a multi-target image is mapped to the surface of a first digital microarray reflector (6-1) through a light beam expanding collimation lens (5), and background light of a non-target object is reflected out of a subsequent optical system by controlling the first digital microarray reflector (6-1); reflecting the background stray light to the light receiver (7); the first digital micro array reflector (6-1) is controlled to reflect the multi-target object light field to the second digital micro array reflector (6-2), the image is subjected to coding aperture coding and then is incident to the third digital micro array reflector (6-3), after the coded image is randomly optically modulated, the coded image is imaged through a second imaging lens (4-2), then is converged through a converging lens (8) and then is incident to a photoelectric detector (9), a multi-target coded image is reconstructed through a compression algorithm module (10), and then is decoded and processed through a sparse linear algorithm module (11), and decoding the coded image to form a low-resolution image, listing a sparse linear equation set for each pixel gray value of a plurality of low-resolution images obtained by all detectors through the module, and solving a least square solution to obtain a super-resolution image of the multi-target object.
2. The system according to claim 1, wherein the first imaging lens (4-1) images the telescope incident light, and the second imaging lens (4-2) is used for imaging the coded image after random spatial light modulation; the first digital micro array reflector (6-1) is used for reflecting non-target background light and stray light in the multi-target image to the light receiver (7) and reflecting the effective multi-target image to the second digital micro array reflector (6-2).
3. The system according to claim 1, wherein the second digital micro-array mirror (6-2) is used for encoding the multi-object image by Hadamard coding or fast Hadamard transform algorithm; the Hadamard coding adopts an H matrix or an S matrix, wherein the S matrix is an N-order cyclic S matrix and a cyclic S matrix constructed based on an m sequence; the order number of the N-order cyclic S matrix is 7, 11, 15, 19, 23 and 27, and the higher the order number is, the higher the resolution is; the digital microarray mirror unit further includes a liquid crystal spatial light modulator.
4. The system according to claim 1, wherein the third digital micro-array mirror (6-3) is used for imaging the coded image after random spatial light modulation of the multi-target coded image through the second imaging lens (4-2) and then inputting the coded image to the converging lens (8); the converging lens unit converges an image modulated by the third digital micro-array reflecting lens (6-3) at random to one point by a converging lens (8), then the image is incident to a corresponding photoelectric detector (9), high-flux imaging is realized through the converging lens (8), and the converging lens unit is applied to weak light, ultra-weak light and single photon imaging.
5. The system according to claim 1, wherein the photoelectric detection unit receives the optical signal converged by the corresponding converging lens (8) by a photoelectric detector group (9) and then inputs the optical signal to the corresponding compression algorithm module (10), wherein the photoelectric detector group comprises M point detectors, and each point detector adopts an ultraviolet, visible light, infrared ray array photoelectric detector or single photon detector to detect in an optical spectrum range or ultra-high sensitivity; the single photon detector is ultraviolet, visible light or infrared avalanche diode, solid photomultiplier or superconductive single photon detector.
6. The system according to claim 1, wherein the compression algorithm module (10) implements compressed sensing using any one of the following algorithms: greedy reconstruction algorithm, matching tracking algorithm MP, orthogonal matching tracking algorithm OMP, basis tracking algorithm BP, LASSO, LARS, GPSR, Bayesian estimation algorithm, magic, IST, TV, StOMP, CoSaMP, LBI, SP, l1_ ls, smp algorithm, SpaRSA algorithm, TwinST algorithm, l1_ ls0Reconstruction algorithm, l1Reconstruction algorithm, l2The reconstruction algorithm is a function of the number of the points,the sparse base can adopt a discrete cosine transform base, a wavelet base, a Fourier transform base, a gradient base or a gabor transform base; m coded images corresponding to the photoelectric detector group are reconstructed by using the compression algorithm module.
7. The system according to claim 1, wherein the decoding and sparse linear algorithm module (11) forms a low-resolution image by decoding the N encoded images, then lists a linear equation set by gray values of each pixel of a relevant region in the M images to form a sparse linear equation set, and solves a least square solution to obtain the super-resolution image of the multi-target object.
8. The system according to claim 1, characterized in that the third digital microarray mirror (6-3) is synchronized with the M sets of photodetectors (9), and each individual detector in the set of photodetectors (9) detects the total light intensity cumulatively within the turnover time interval for every turnover of the micromirror array in the third digital microarray mirror (6-3), so as to realize the photoelectric signal acquisition and conversion, and then sends the collected light intensity to the corresponding compression algorithm module (10).
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