CN102706449B - Two-channel remote sensing light spectrum imaging system based on compressed sensing and imaging method - Google Patents
Two-channel remote sensing light spectrum imaging system based on compressed sensing and imaging method Download PDFInfo
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Abstract
The invention discloses a two-channel remote sensing light spectrum imaging system based on compressed sensing, and an imaging method, and mainly solves the problems, in the prior art, that the utilization ratio of light spectrum information is low, the difficulty in manufacturing technique of a detector is high, high spatial resolution and high spectral resolution of spectrogram image can not be obtained at the same time. The imaging system comprises a beamsplitter module (1), a first observing passage module (2), a second observing passage module (3), and an image reconstructing and processing module (4). Original spectrogram images are divided into two paths of light beams which are the same in information and different in directions, the first observing passage module (2) and the second observing passage module (3) achieve complementary coding observation to the light spectrum image, observed results are output to the image reconstructing and processing module (4), a reconstruction model of the spectrogram image is built, and the original spectrogram images are reconstructed by using a nonlinear optimized method. The invention has the advantages that the utilization ratio of the light spectrum information is high, the manufacturing technique is simple, and the calculation complex rate is low, thereby being used for acquiring and reconstructing remote sensing spectrogram images.
Description
Technical field
The invention belongs to technical field of image processing, further relate to a kind of optical spectrum encoded imaging system and the formation method based on compressed sensing in remote sensing field, can be used for realizing obtaining and reconstruct of Remote Spectra image.
Background technology
Spectrum picture is defined on the basis in traditional two-dimensional space territory has increased Spectral dimension according to the three-dimensional data cube forming, and that is to say, spectrum picture is comprised of the different spectral coverage image under same field.Traditional panchromatic and coloured image can not meet people's application demand already far away, and all kinds of Remote Spectra imaging techniques are by broad development.Light spectrum image-forming technology utilizes several or tens wave bands simultaneously to target imaging, can realize synchronously obtaining object space information, spectral information.Because it is distinctive, have the advantage of imaging and spectrographic detection concurrently, be widely used in land ocean geography remote sensing, atmosphere environment supervision, military target is scouted, is monitored meteorological observation, a plurality of dual-use fields such as disaster prevention.
Because Remote Spectra imaging technique all has wide application potential in civil and military field, scientific research personnel is devoted to study various spectrum imaging systems and formation method always, but prior art meets with development bottleneck, is mainly manifested in following two aspects:
First aspect, the spatial resolution of spectrum picture can be improved by reducing the instantaneous field of view angle of remote sensor; Spectral resolution can improve with the bandwidth that reduces each wave band by increasing wave band quantity.But under the certain condition of incident light energy, the contradiction between the narrow wave band imaging of high-resolution spectra and low narrow-band radiated energy receive causes the high spatial resolution of spectrum picture and spectral resolution not to obtain simultaneously.
Second aspect, spectrum picture is a kind of 3 d image data, its data volume is very huge.Particularly, when the spectral resolution of image is improved, its data volume can sharply increase.In order to reduce the pressure of data transmission, prior art adopts the mode of image compression encoding to represent scene information with less data bit number always.Through data compression, a large amount of non-important data are abandoned, and the process of this high-speed sampling recompression causes the complexity of system to increase and wasted a large amount of sampling resources.
So very naturally drawn a problem: can utilize other transformation spaces to describe signal, set up new signal description and the theoretical frame of processing, make in the situation that guarantee information is not lost, use the speed sampled signal far below nyquist sampling theorem requirement, can restoring signal completely again simultaneously, by the sampling of the paired information of sample transition of signal.
Since 2006, signal process field a kind of new compressive sensing theory that has been born, has attracted related researcher's concern greatly.This theory is pointed out, in signal acquisition, just data are suitably compressed, than traditional signal acquisition and processing procedure, under compressive sensing theory framework, sampling rate is no longer decided by the bandwidth of signal, but being decided by structure and the content of information in signal, this makes the sampling of sensor and assesses the cost greatly to reduce, and signal rejuvenation is one and optimizes the process of calculating.The length of the signal X that note is sampled is N, set sparse base Ψ, it is sparse making signal X on Ψ, the mathematical model of compressive sensing theory is observing matrix Φ who ties up with the incoherent M of Ψ * N of design, M < N wherein, is multiplied each other and is obtained the observation data Y of lower dimension by Φ and X:
Y＝ΦX
By solving l
1optimization problem under norm is carried out reconstruct original signal X, and its process is:
min||Ψ
TX||
1?s.t.Y＝ΦX，
Wherein, Ψ is sparse base.
As everyone knows, spectrum picture signal has compressibility, as long as the degree of rarefication of selecting suitable sparse transform-based just can guarantee.First can with the incoherent observing matrix of transform-based, the high dimensional signal of conversion gained be projected on a lower dimensional space with one, realize effective compression sampling of spectral information, so just can under certain spectral resolution condition, reduce the difficulty that realizes of camera, or under existence conditions, significantly put forward spectral resolution; Then by solving-optimizing problem, just can from these a small amount of projections, with high probability, reconstruct original spectrum image, the enough information of reconstruction signal that can prove such the inclusive projection.
According to above-mentioned theory, the scholar M.E.Gehm of Duke Univ USA, R.Johm, D.J.Brady, R.M.Willet and T.J.Schualz are at paper " Single-shot compressive spectral imaging with a dual-disperser architecture " OPTICS EXPRESS, Vol.15, No.21, pp.14013-14027, in 2007, propose to utilize random coded template and two dispersion elements, the observation of realization to spectrum picture, finally by compressive sensing theory, reconstruct original image, the deficiency of this method is only to utilize single channel system to observe, spectrum picture is losing the effective information of half by meeting after coding templet, so just reduced the spectral information utilization factor of image, and then reduced the reconstruction accuracy of image.
Summary of the invention
The object of the invention is to shortcoming and development bottleneck for above-mentioned prior art, a kind of binary channels Remote Spectra imaging system and formation method based on compressed sensing proposed, realize the complementary encoding of spectrum picture, thereby improve spectral information utilization factor and the reconstruction accuracy of image.
For achieving the above object, binary channels Remote Spectra imaging system of the present invention, comprise observation channel module, image reconstruction process module, observation channel module is observed spectrum picture, obtain observed image, image reconstruction process module is reconstructed observed image, obtain original spectrum image, it is characterized in that, observation channel module is divided into two, and the front end at these two observation passages is provided with splitter module, it is identical that the incident beam of collected spectrum picture is divided into two-way information through splitter module, the light beam that direction is different, this two-way light beam observes channel module realize the binary channels complementary encoding observation of spectrum picture through the first observation channel module and second respectively, observed result is exported to the reconstruct that image reconstruction process module is carried out spectrum picture.
Described the first observation channel module, comprise first lens group, the first dispersion element, the first coding templet, the second dispersion element, first surface array detector, this the first dispersion element is positioned at the rear end of first lens group, and at first lens, form on the focal plane of picture, spectrum dimension information for translation spectrum picture, realize the dispersion of spectrum picture, this the first coding templet is positioned at the rear end of the first dispersion element, for realizing the coding to spectrum picture, this the second dispersion element is positioned at the rear end of the first coding templet, spectrum dimension information for reverse translation spectrum picture, to eliminate the chromatic dispersion effects of being introduced by the first dispersion element, realize the alignment again of spectrum dimension information, this first surface array detector is positioned at the second dispersion element rear end, for observed image, obtain coding image information afterwards.
Described the second observation channel module, comprise the second lens combination, the 3rd dispersion element, the second coding templet, the 4th dispersion element, the second planar array detector, this the second dispersion element is positioned at the rear end of the second lens combination, and on the focal plane of the second lens combination imaging, spectrum dimension information for translation spectrum picture, realize the dispersion of spectrum picture, this the second coding templet is positioned at the rear end of the 3rd dispersion element, for realizing the coding to spectrum picture, the 4th dispersion element is positioned at the rear end of the second coding templet, spectrum dimension information for reverse translation spectrum picture, to eliminate the chromatic dispersion effects of being introduced by the 3rd dispersion element, realize the alignment again of spectrum dimension information, this second planar array detector is positioned at the 4th dispersion element rear end, for observed image, obtain coding image information afterwards.
The first described coding templet and the second coding templet, the rectangle plane plate being formed by printing opacity and lighttight grid, printing opacity grid is encoded to 1 to image, and light tight grid is encoded to 0 to image; Whether printing opacity is random setting to each grid of the first coding templet, realizes the random coded to each position information of image; The light transmission state of each grid of the second coding templet is contrary with the light transmission state of the corresponding grid of the first coding templet, realizes the complementary encoding to each position information of image.
For achieving the above object, binary channels Remote Spectra formation method of the present invention, comprises the steps:
(1) spectrum picture observation procedure:
(1a) establish original spectrum information matrix f
0size be M * N * L, wherein M * N is spectral information spatial resolution, resolution between the spectrum that L is spectral information;
(1b) spectral information of establishing any point is f
0(m, n, k), wherein m and n representation space dimension coordinate, k represents spectrum dimension coordinate, 0≤m≤M-1 wherein, 0≤n≤N-1,0≤k≤L-1;
(1c) spectral information is divided into two-way in the ratio of 1: 1, wherein the contained information f of the first via
11the information f that (m, n, k) and the second road are contained
21(m, n, k) is identical, that is:
(1d), by k pixel of information translation of k spectral coverage in two-way spectral information, draw dispersion spectral information f afterwards
12(m, n, k) and f
22(m, n, k) is respectively:
(1f) two-way spectral information is encoded, coding function is respectively T
1(m, n) and T
2(m, n), draws through the spectral information f after coding
13(m, n, k) and f
23(m, n, k) is respectively:
Wherein, T
1(m, n) gets 0 or 1 randomly,
To realize T
1(m, n) and T
2coding between (m, n) is complementary;
(1g), by the reverse translation k pixel of the information of k spectral coverage of two-way spectral information, the information of the same space position different spectral coverage of again aliging, draws the spectral information f after reverse translation
14(m, n, k) and f
24(m, n, k) is respectively:
(1h) two-way spectral information is exposed, obtaining observed result is y
1(m, n) and y
2(m, n), wherein
Be designated as:
Y＝Hf；
Y={y wherein
i(m, n) }, i=1,2 is observed image matrix, and H is linear operator, represents the observation model of system, and f is part original spectrum information matrix;
(2) spectrum picture reconstruction step:
(2a) observed image matrix Y is delivered to image reconstruction processor;
(2b) set ΨWeiDCT territory, sparse territory or wavelet field or Fourier domain, make spectrum picture is sparse under Ψ;
(2c) image reconstruction processor, according to observed result Y and sparse territory Ψ, is utilized nonlinear optimization method reconstituting initial image f.
The present invention compared with prior art has the following advantages:
First: the present invention has adopted twin-channel observation module, realized the complementation observation of spectrum picture, overcome in existing imaging system the low and low shortcoming of reconstruction accuracy of spectral information utilization factor, made the high and high advantage of reconstruction accuracy of tool spectral information utilization factor of the present invention;
Second: the light transmission state of the coding templet that the present invention adopts is random setting, realized the random coded to spectrum picture, with respect to the mode of utilizing high density detecting device first to expose and recompress in traditional imaging system, the present invention utilizes low-density detector to expose, and imaging compression synchronously completes, make cost of the present invention low, without compression artefacts, and system complexity is low;
The the 3rd: the present invention has utilized the sparse property of spectrum picture, by solving nonlinear optimal problem, realizes Image Reconstruction, the present invention can be obtained simultaneously have the spectrum picture of resolution between high spatial resolution and high spectrum.
Accompanying drawing explanation
Fig. 1 is the structured flowchart that the present invention is based on the binary channels Remote Spectra imaging system of coding perception;
Fig. 2 is the structured flowchart of the present invention's the first observation channel module and the second observation channel module;
Fig. 3 is the first coding templet and the second coding templet figure using in the present invention;
Fig. 4 is the process flow diagram that the present invention is based on the binary channels Remote Spectra formation method of coding perception.
Embodiment
With reference to Fig. 1, the binary channels Remote Spectra imaging system based on compressed sensing of the present invention, comprises that splitter module 1, the first observation channel module 2, the second observation leads to module 3 and image reconstruction process module 4.Wherein splitter module 1 is positioned at the front end of the first observation passage 2 and the second observation passage 3, the first observation passage 2 is identical with the structure of the second observation passage 3, as shown in Figure 2, Fig. 2 (a) has provided the structure of the first observation passage 2, Fig. 2 (b) has provided the structure of the second observation passage 3, and two input ends of image reconstruction process module 4 are connected with the output terminal of the second observation passage with the first observation passage respectively.Splitter module 1 is divided into by the incident beam of original spectrum image the light beam that two-way information is identical, direction is different, this two-way light beam is respectively through the first observation channel module 2 and the second observation channel module 3, realize the binary channels complementary encoding observation of spectrum picture, observed result sends image reconstruction process module 4 to, and image reconstruction process module 4 is carried out the reconstruct of spectrum picture by nonlinear optimization method.
With reference to Fig. 2 (a), the first observation channel module 2 comprises: first lens group 21, the first dispersion element 22, the first coding templet 23, the second dispersion element 24 and first surface array detector 25.Wherein, the first dispersion element 22 is positioned at the rear end of first lens group 21, and on the focal plane of first lens group 21 imagings, the spectrum dimension information for translation spectrum picture, realizes the dispersion of spectrum picture; The structure of the first coding templet 23 is as shown in Fig. 3 (a), and it is positioned at the rear end of the first dispersion element 22, for realizing the coding to spectrum picture; The second dispersion element 24 is positioned at the rear end of the first coding templet 23, spectrum dimension information for reverse translation spectrum picture, its placement direction is contrary with the placement direction of the first dispersion element 22, to eliminate the chromatic dispersion effects of being introduced by the first dispersion element, realizes the alignment again of spectrum dimension information; First surface array detector 25 is positioned at the second dispersion element 24 rear ends, for observed image, obtains coding image information afterwards.
With reference to Fig. 2 (b) the second observation channel module 3, comprise: the second lens combination 31, the 3rd dispersion element 32, the second coding templet 33, the 4th dispersion element 34, the second planar array detector 35.Wherein, the second dispersion element 32 is positioned at the rear end of the second lens combination 31, and on the focal plane of the second lens combination 31 imagings, the spectrum dimension information for translation spectrum picture, realizes the dispersion of spectrum picture; As shown in Figure 3 (b), it is positioned at the rear end of the 3rd dispersion element 32 to the second coding templet 33, for realizing the coding to spectrum picture; The 4th dispersion element 34 is positioned at the rear end of the second coding templet 33, its placement direction is contrary with the placement direction of the 3rd dispersion element 32, spectrum dimension information for reverse translation spectrum picture, to eliminate the chromatic dispersion effects of being introduced by the 3rd dispersion element, realizes the alignment again of spectrum dimension information; The second planar array detector 35 is positioned at the 4th dispersion element rear end, for observed image, obtains coding image information afterwards.
With reference to Fig. 3 (a) and Fig. 3 (b), the rectangle plane plate that the first coding templet 32 and the second coding templet 33 are comprised of printing opacity and lighttight grid, each grid size is identical, and with image slices vegetarian refreshments equal and opposite in direction, printing opacity grid is encoded to 1 to image, and light tight grid is encoded to 0 to image; Whether printing opacity is random setting to each grid of the first coding templet 23, realizes the random coded to each position information of image; The light transmission state of each grid of the second coding templet 33 is contrary with the light transmission state of the corresponding grid of the first coding templet, realizes the complementary encoding to each position information of image.
With reference to Fig. 4, for achieving the above object, the binary channels Remote Spectra formation method based on compressed sensing of the present invention, comprises spectrum picture observation and spectrum picture reconstruction step.
One, spectrum picture observation:
Step 1, initialization original spectrum information
If original spectrum information matrix f
0size be M * N * L, wherein M * N is spectral information spatial resolution, the spectral resolution that L is spectral information, the spectral coverage number of spectral information is L;
Step 2, the spectral information of establishing any point is f
0(m, n, k), wherein m and n representation space dimension coordinate, k represents spectrum dimension coordinate, 0≤m≤M-1 wherein, 0≤n≤N-1,0≤k≤L-1;
Step 3, spectral information along separate routes
Spectral information is divided into two-way in the ratio of 1: 1, wherein the contained spectral information f of the first via
11the spectral information f that (m, n, k) and the second road are contained
21(m, n, k) is identical, and is equal to
original spectrum information doubly, that is:
Step 4, spectral information translation
By first via spectral information f
11(m, n, k) and the second road spectral information f
21(m, n, k) carries out respectively linear translation, is about to k pixel of information translation of k spectral coverage, draws dispersion two-way spectral information f afterwards
12(m, n, k) and f
22(m, n, k) is respectively:
Step 5, spectral information coding
Set first via coding function and the second road coding function and be respectively T
1(m, n) and T
2(m, n), respectively to the first via spectral information f after dispersion
12(m, n, k) and the second road spectral information f
22(m, n, k) encodes, and draws coding two-way spectral information f afterwards
13(m, n, k) and f
23(m, n, k) is respectively:
Wherein, T
1(m, n) gets 0 or 1 randomly,
To realize T
1(m, n) and T
2coding between (m, n) is complementary.
Step 6, the reverse translation of spectral information
By the first via spectral information f after coding
13(m, n, k) and the second road spectral information f
23(m, n, k) carries out reverse translation, is about to the reverse translation k pixel of information of k spectral coverage, and the different spectral coverage information of the same space position can be alignd again, draws the two-way spectral information f after reverse translation
14(m, n, k) and f
24(m, n, k) is respectively:
Step 7, to the first via spectral information f after reverse translation
14(m, n, k) and the second road spectral information f
24(m, n, k) exposes respectively, obtains two-way observed result y
1(m, n) and y
2(m, n) is respectively:
Step 8, by first via observed result y
1(m, n) and the second road observed result y
2(m, n) merges into observed image matrix Y, i.e. Y={y
i(m, n) }, i=1 wherein, 2, the Systems with Linear Observation model of initialization system is H, can draw:
Y＝Hf；
Wherein f is original spectrum information matrix.
Two, spectrum picture reconstruct:
Step 1, is sent to image reconstruction processor by observed image matrix Y.
Step 2, setting sparse transform domain Ψ is discrete sine transform territory or wavelet transformed domain or Fourier transform, make original spectrum image f is sparse on sparse transform domain Ψ, makes the projection coefficient Ψ of original spectrum image f under sparse transform domain Ψ
tin f, most numerical value is less than a certain specific threshold, and this threshold value need to be set by experiment, and the threshold value that different sparse transform domains are corresponding is different, and it is discrete cosine transform domain that this example is set sparse transform domain Ψ, and setting threshold is 1, but is not limited to this value.
Step 3, image reconstruction processor, according to observed result Y and sparse transform domain Ψ, is utilized nonlinear optimization method reconstruct original spectrum image f.
(3a) set optimization aim function be min (|| Ψ
tf||
1), T representing matrix transposition wherein, || ||
1expression is got l to projection coefficient
1norm, min () represents to get l
1the minimum value of norm;
(3b) setting constraint condition is Hf=Y, and wherein Y is observed image matrix, the observation model that H is system, and f is original spectrum image;
(3c), according to optimization aim function and constraint condition, reconstruct original spectrum image f.
Claims (7)
1. the binary channels Remote Spectra imaging system based on compressed sensing, comprise observation channel module, image reconstruction process module, observation channel module is observed spectrum picture, obtain observed image, image reconstruction process module is reconstructed observed image, obtains original spectrum image, it is characterized in that:
Observation channel module is divided into two, the first observation channel module (2) and second is observed channel module (3), in the first observation channel module (2), be provided with the first coding templet (23), in the second observation channel module (3), be provided with the second coding templet (33): the rectangle plane plate that this first coding templet (23) and the second coding templet (33) are comprised of printing opacity and lighttight grid, each grid size is identical, and with image slices vegetarian refreshments equal and opposite in direction, printing opacity grid is encoded to 1 to image, and light tight grid is encoded to 0 to image;
The front end of described two observation passages is provided with splitter module (1), the incident beam of collected spectrum picture is divided into through splitter module (1) light beam that two-way information is identical, direction is different, this two-way light beam is realized respectively the binary channels complementary encoding observation of spectrum picture through the first coding templet (23) and the second coding templet (33), whether printing opacity is random setting to each grid of the first coding templet (23), realizes the random coded to each position information of image; The light transmission state of each grid of the second coding templet (33) is contrary with the light transmission state of the corresponding grid of the first coding templet, realizes the complementary encoding to each position information of image;
Complementary encoding observed result is exported to image reconstruction process module (4) and is carried out the reconstruct of spectrum picture.
2. the binary channels Remote Spectra imaging system based on compressed sensing according to claim 1, it is characterized in that, described the first observation channel module (2), comprise first lens group (21), the first dispersion element (22), the first coding templet (23), the second dispersion element (24), first surface array detector (25), this the first dispersion element (22) is positioned at the rear end of first lens group (21), and on the focal plane of first lens group (21) imaging, spectrum dimension information for translation spectrum picture, realize the dispersion of spectrum picture, this the first coding templet (23) is positioned at the rear end of the first dispersion element (22), for realizing the coding to spectrum picture, this the second dispersion element (24) is positioned at the rear end of the first coding templet (23), spectrum dimension information for reverse translation spectrum picture, to eliminate the chromatic dispersion effects of being introduced by the first dispersion element (22), realize the alignment again of spectrum dimension information, this first surface array detector (25) is positioned at the second dispersion element (24) rear end, for observed image, obtain coding image information afterwards.
3. the binary channels Remote Spectra imaging system based on compressed sensing according to claim 2, it is characterized in that, described the second observation channel module (3), comprise the second lens combination (31), the 3rd dispersion element (32), the second coding templet (33), the 4th dispersion element (34), the second planar array detector (35), the 3rd dispersion element (32) is positioned at the rear end of the second lens combination (31), and on the focal plane of the second lens combination (31) imaging, spectrum dimension information for translation spectrum picture, realize the dispersion of spectrum picture, this the second coding templet (33) is positioned at the rear end of the 3rd dispersion element (32), for realizing the coding to spectrum picture, the 4th dispersion element (34) is positioned at the rear end of the second coding templet (33), spectrum dimension information for reverse translation spectrum picture, to eliminate the chromatic dispersion effects of being introduced by the 3rd dispersion element (32), realize the alignment again of spectrum dimension information, this second planar array detector (35) is positioned at the 4th dispersion element (34) rear end, for observed image, obtain coding image information afterwards.
4. the binary channels Remote Spectra imaging system based on compressed sensing according to claim 3, it is characterized in that, described the second dispersion element (24) is contrary with the placement direction of the first dispersion element (22), and the 3rd dispersion element (32) is contrary with the placement direction of the 4th dispersion element (34).
5. the binary channels Remote Spectra imaging system based on compressed sensing according to claim 1, it is characterized in that the first coding templet (23) is identical with light tight grid size with the printing opacity grid in the second coding templet (33), and the equal and opposite in direction of the size of each grid and image slices vegetarian refreshments.
6. the binary channels Remote Spectra formation method based on compressed sensing, comprising:
(1) spectrum picture observation procedure:
(1a) establish original spectrum information matrix f
0size be M * N * L, wherein M * N is spectral information spatial resolution, the spectral resolution that L is spectral information;
(1b) spectral information of establishing any point is f
0(m, n, k), wherein m and n representation space dimension coordinate, k represents spectrum dimension coordinate, 0≤m≤M-1 wherein, 0≤n≤N-1,0≤k≤L-1;
(1c) spectral information is divided into two-way in the ratio of 1:1, wherein the contained information f of the first via
11the information f that (m, n, k) and the second road are contained
21(m, n, k) is identical, that is:
(1d), by k pixel of the information translation of k spectral coverage in two-way spectral information, draw dispersion spectral information f afterwards
12(m, n, k) and f
22(m, n, k) is respectively:
(1f) two-way spectral information is encoded, coding function is respectively T
1(m, n) and T
2(m, n), draws through the spectral information f after coding
13(m, n, k) and f
23(m, n, k) is respectively:
Wherein, T
1(m, n) gets 0 or 1 randomly,
To realize T
1(m, n) and T
2coding between (m, n) is complementary;
(1g), by the reverse translation k pixel of the information of k spectral coverage of two-way spectral information, the information of the same space position different spectral coverage of again aliging, draws the spectral information f after reverse translation
14(m, n, k) and f
24(m, n, k) is respectively:
(1h) two-way spectral information is exposed, obtaining observed result is y
1(m, n) and y
2(m, n), wherein
Be designated as:
Y＝Hf；
Y={y wherein
i(m, n) }, i=1,2 is observed image matrix, and H is linear operator, represents the observation model of system, and f is original spectrum information matrix;
(2) spectrum picture reconstruction step
(2a) observed image matrix Y is delivered to image reconstruction processor;
(2b) setting sparse territory Ψ is discrete cosine territory or wavelet field or Fourier domain, and make spectrum picture is sparse under sparse territory Ψ;
(2c) image reconstruction processor, according to observed result Y and sparse territory Ψ, is utilized nonlinear optimization method reconstruct original spectrum information matrix f.
7. the binary channels Remote Spectra formation method based on compressed sensing according to claim 6, is characterized in that, step (2c) is described utilizes nonlinear optimization method reconstruct original spectrum information matrix f, carries out as follows:
First, set optimization aim function be min (|| Ψ
tf||
1), T representing matrix transposition wherein, || ||
1expression is got l to projection coefficient
1norm, min () represents to get l
1the minimum value of norm;
Then, setting constraint condition is Hf=Y, and wherein Y is observed image matrix, the observation model that H is system, and f is original spectrum information matrix;
Finally, according to optimization aim function and constraint condition, reconstruct original spectrum image.
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| CN106441577B (en) * | 2016-09-27 | 2018-01-09 | 北京理工大学 | Collaboration coding Hyperspectral imager and image reconstructing method based on accidental projection |
| CN106998474B (en) * | 2017-03-29 | 2019-10-18 | 南京大学 | A kind of spectrum multichannel mixing compression transmitting method |
| CN107205103B (en) * | 2017-04-14 | 2020-02-14 | 华东师范大学 | Ultra-high speed compression photographic device based on compressed sensing and stripe camera principle |
| CN109444056B (en) * | 2018-10-30 | 2024-03-01 | 浙江大学 | Binocular imaging type underwater spectral reflectivity in-situ measurement device and measurement method |
| CN109682476B (en) * | 2019-02-01 | 2020-06-19 | 北京理工大学 | Method for performing compressed hyperspectral imaging by using self-adaptive coding aperture |
| CN112903103A (en) * | 2021-01-22 | 2021-06-04 | 西安电子科技大学 | Computed spectrum imaging system and method based on DMD and complementary all-pass |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7315371B2 (en) * | 2004-01-23 | 2008-01-01 | P&P Optica Inc. | Multi-channel spectrum analyzer |
| CN101893552A (en) * | 2010-07-06 | 2010-11-24 | 西安电子科技大学 | Hyperspectral imager and imaging method based on compressive sensing |
-
2012
- 2012-05-24 CN CN201210172731.5A patent/CN102706449B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7315371B2 (en) * | 2004-01-23 | 2008-01-01 | P&P Optica Inc. | Multi-channel spectrum analyzer |
| CN101893552A (en) * | 2010-07-06 | 2010-11-24 | 西安电子科技大学 | Hyperspectral imager and imaging method based on compressive sensing |
Non-Patent Citations (2)
| Title |
|---|
| 基于压缩感知的双通道SAR地面运动目标检测方法研究;王伟伟等;《电子与信息学报》;20120331;第34卷(第3期);第588-592页 * |
| 王伟伟等.基于压缩感知的双通道SAR地面运动目标检测方法研究.《电子与信息学报》.2012,第34卷(第3期),第588-592页. |
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