CN103983355A - Compressed spectrum imaging system and method based on panchromatic imaging - Google Patents

Compressed spectrum imaging system and method based on panchromatic imaging Download PDF

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CN103983355A
CN103983355A CN201410228328.9A CN201410228328A CN103983355A CN 103983355 A CN103983355 A CN 103983355A CN 201410228328 A CN201410228328 A CN 201410228328A CN 103983355 A CN103983355 A CN 103983355A
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spectrum
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CN103983355B (en
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石光明
李超
高大化
刘丹华
邓健
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Xidian University
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Abstract

The invention discloses a compressed spectrum imaging system and method based on panchromatic imaging. The problems that in an existing compressed spectrum imaging technology, the spectrum image information using rate is low, and the spectrum image resolution ratio is not high are mainly solved. The imaging system comprises a beam splitter module (1), a compressed spectrum observing module (2), a panchromatic observing module (3) and an image reestablishing processing module (4). An incident beam of a collected spectrum image is divided into two beams identical in information and different in direction through the beam splitter module (1), one beam passes through the compressed spectrum observing module (2), compressed encoding observing of the spectrum image is achieved, the other beam passes through the panchromatic observing module (3), and panchromatic observing of the spectrum image is achieved. The image reestablishing processing module (4) carries out simultaneous fusion on output results of the two modules, and then spectrum image reestablishing is completed. The system and method have the advantages of being high in spectrum information using rate, the obtained spectrum image is high in resolution ratio, and the system and method can be used for obtaining and reestablishing the spectrum image.

Description

Compressed spectrum imaging system and formation method based on full color imaging
Technical field
The invention belongs to technical field of image processing, particularly a kind of imaging technique of compressed spectrum, can be used for obtaining and reconstruct of spectrum picture, improves the spatial resolution of image.
Background technology
By light spectrum image-forming, can capturing optical power spectrum density, this power spectrum density is the function of wavelength X and locus (x, y).That is to say, spectrum picture is made up of the image of different spectral coverage under same field, and it comprises space dimension information and spectrum dimension information, and traditional imaging only comprises space dimension information.The spectrum dimension information of spectrum picture locus is for showing that the composition and the structure that are observed object in scene are of great importance.Impel light spectrum image-forming technology at geographical remote sensing, atmosphere environment supervision, military target is scouted, is monitored meteorological observation, the field widespread uses such as disaster prevention.Scientific research personnel is also devoted to study various spectrum imaging systems and formation method always, but still there is many problems in prior art, main manifestations is: the spatial resolution of traditional light spectrum image-forming depends on detector array density, for the cost that improves spatial resolution and increase detector array density is very huge, and high resolving power between time, space, spectrum is often difficult to get both simultaneously.How utilizing existing detector to obtain more high-resolution spectrum picture, is a problem demanding prompt solution.
The compressed sensing CS theory being proposed by people such as E.J.Candes, J.Romberg, T.Tao and D.L.Donoho for 2006 has been brought new hope for addressing the above problem.This theory is pointed out, in signal acquisition, just data is carried out to suitable compression.Than traditional signal acquisition and processing procedure, under compressive sensing theory framework, sampling rate is no longer decided by the bandwidth of signal, but be 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 a process of optimizing reconstruct.
If being sampled the length of signal X is N, sparse transform-based is Ψ.Be that the expression of signal X on Ψ is sparse.The mathematical model of compressive sensing theory requires observing matrix Φ who ties up with the incoherent M of Ψ × N of design, and wherein M < N is multiplied each other and 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 mathematical notation is:
min||Ψ TX|| 1s.t.Y＝ΦX
According to above-mentioned theory, the scholar M.E.Gehm of Duke Univ USA, the designs such as R.Johm have also proposed CASSI (Coded Aperture Snapshot Spectral Imagers) system, utilize random coded template and dispersion element, realize the observation to spectrum picture, finally reconstruct original image by compressive sensing theory.But due to the gate action of coding templet, spectrum picture is by effective information that can loss half after coding templet, cause the spatial resolution of spectrum picture of final reconstruct not high.
Summary of the invention
The object of the invention is to for existing compressed spectrum imaging space resolution lowly, propose a kind of compressed spectrum imaging system and formation method based on full color imaging, to reduce the loss of effective information, improve the spatial resolution of reconstruct spectrum picture.
Technical scheme of the present invention completes like this:
Know-why of the present invention is to use for reference M.E.Gehm, and the CASSI system that the people such as R.Johm propose has increased panchromatic observation, the imaging system that composition full color imaging and compressed spectrum imaging combine on original compressed encoding observation basis.
One. according to above-mentioned principle, the present invention is based on the compressed spectrum imaging system of full color imaging, comprising:
Observation module, image reconstruction process module, observation 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 module is divided into two, i.e. compressed spectrum observation module and panchromatic observation module, and the front end of these two observation modules is provided with splitter module; The incident beam of collected spectrum picture is divided into through splitter module the two-way light beam that information is identical, direction is different, the compressed encoding observation of spectrum picture is realized on one tunnel through compressed spectrum observation module, the panchromatic observation of spectrum picture is realized on another road through panchromatic observation module; Image reconstruction process module is carried out the compressed encoding observation of the spectrum picture of this two modules output and panchromatic observed result to complete after simultaneous fusion the reconstruct of spectrum picture.
As preferably, described compressed spectrum observation module, comprises first lens group, coding templet, dispersion element and first surface array detector; Coding templet is positioned at the rear end of first lens group, realize the coding to spectrum picture, dispersion element is positioned at the rear end of coding templet, for the spectrum dimension information of translation spectrum picture, realize the dispersion of spectrum picture, first surface array detector is positioned at the rear end of dispersion element, for observed image, obtains coding image information afterwards.
As preferably, described panchromatic observation module, comprise the second lens combination and the second planar array detector, the second planar array detector is positioned at the second lens combination rear end, for observed image, obtain full-colour image information, this full-colour image information comprises the spectral information of first surface array detector record and the spectral information of loss.
Two. according to above-mentioned principle, the present invention is based on the compressed spectrum formation method of full color imaging, 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, wherein 0≤m≤M-1,0≤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:
f 11 ( m , n , k ) = 1 2 f 0 ( m , n , k ) ,
f 21 ( m , n , k ) = 1 2 f 0 ( m , n , k ) ;
(1d) utilize coding function T (m, n) to encode to first via spectral information, draw through the spectral information f after coding 12(m, n, k) is:
f 12 ( m , n , k ) = f 11 ( m , n , k ) T ( m , n ) = 1 2 f 0 ( m , n , k ) T ( m , n ) ,
Wherein, T (m, n) gets 0 or 1 randomly;
(1e) by k pixel of information translation of k spectral coverage in the spectral information after first via coding, move to m-k by k the capable information of spectral coverage m capable, draw dispersion spectral information f afterwards 13(m, n, k) is:
f 13 ( m , n , k ) = f 12 ( m - k , n , k ) = f 11 ( m - k , n , k ) T ( m - k , n ) = 1 2 f 0 ( m - k , n , k ) T ( m - k , n ) ;
(1f) spectral information on the first via and the second tunnel is exposed simultaneously, obtain the observed result y of the first via 1the observed result y on (m, n) and the second tunnel 2(m, n):
y 1 ( m , n ) = &Sigma; k f 13 ( m , n , k ) = 1 2 &Sigma; k = 0 L - 1 f 0 ( m - k , n , k ) T ( m - k , n ) ,
y 2 ( m , n ) = &Sigma; k f 21 ( m , n , k ) = 1 2 &Sigma; k = 0 L - 1 f 0 ( m , n , k ) ,
This two-way observed result is designated as:
Y＝Hf，
Wherein Y={y 1(m, n), y 2(m, n) } be observed image matrix, H is linear operator, represents the observation model of system, f is original spectrum image;
(2) spectrum picture reconstruction step:
(2a) observed image matrix Y is delivered to image reconstruction processor;
(2b) setting sparse base Ψ is wavelet basis or DCT base or Fourier basis, and make spectrum picture is sparse under sparse base Ψ;
(2c) image reconstruction processor, according to observed image matrix Y and sparse base Ψ, utilizes nonlinear optimization method to reconstruct original spectrum image f.
The present invention compared with prior art has the following advantages
First: the present invention, than traditional single channel compressed spectrum imaging technique, has increased full color imaging, can record all spectral informations, overcome the low shortcoming of spectral information utilization factor in existing imaging system;
Second: the present invention takes full advantage of the high spatial resolution of full color imaging, make the present invention have advantages of that reconstruction accuracy is high;
The the 3rd: the present invention has utilized the sparse property of spectrum picture, realize spectrum picture reconstruct by solving nonlinear optimal problem, the present invention can be obtained simultaneously have the spectrum picture of resolution between high spatial resolution and high spectrum.
Brief description of the drawings
Fig. 1 is system chart of the present invention;
Fig. 2 is the structured flowchart of compressed spectrum observation module in the present invention;
Fig. 3 is the structured flowchart of panchromatic observation module in the present invention;
Fig. 4 is the coding templet structural drawing in the present invention;
Fig. 5 is formation method process flow diagram of the present invention;
Fig. 6 is the reconstruction result that imaging system of the present invention and the CASSI of Duke University system are observed balloons spectrum picture;
Fig. 7 is the reconstruction result that imaging system of the present invention and the CASSI of Duke University system are observed egyptian_statue spectrum picture.
Embodiment
Below in conjunction with accompanying drawing and example, the present invention is described in detail:
With reference to Fig. 1, the compressed spectrum imaging system based on full color imaging of the present invention, comprises splitter module 1, compressed spectrum observation module 2, panchromatic observation module 3 and image reconstruction process module 4.Wherein: splitter module 1 is positioned at the front end of compressed spectrum observation module 2 and panchromatic observation module 3; The incident beam of collected spectrum picture is divided into through splitter module 1 the two-way light beam that information is identical, direction is different, the compressed encoding observation of spectrum picture is realized on one tunnel through compressed spectrum observation module 2, the panchromatic observation of spectrum picture is realized on another road through panchromatic observation module 3; Image reconstruction process module 4 is carried out the compressed encoding observation of the spectrum picture of this two modules output and panchromatic observed result to complete after simultaneous fusion the reconstruct of spectrum picture.
With reference to Fig. 2, the compressed spectrum observation module 2 in the present invention, comprises first lens group 21, coding templet 22, dispersion element 23 and first surface array detector 24.Wherein: coding templet 22 is positioned at the rear end of first lens group 21, realize the coding to spectrum picture; Dispersion element 23 is positioned at the rear end of coding templet 22, for the spectrum dimension information of translation spectrum picture, realizes the dispersion of spectrum picture; First surface array detector 24 is positioned at the rear end of dispersion element 23, for observed image, obtains coding image information afterwards.
With reference to Fig. 3, the panchromatic observation module 3 in the present invention, comprises the second lens combination 31 and the second planar array detector 32.Wherein: the second planar array detector 32 is positioned at the second lens combination 31 rear ends, for observed image, obtains full-colour image information, this full-colour image information comprises spectral information that first surface array detector 24 records and the spectral information of loss.
With reference to Fig. 4, the coding templet 22 in the present invention, the rectangle plane plate being formed by 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, light tight grid is encoded to 0 to image; Whether printing opacity is random setting to each grid of coding templet 22, realizes the random coded of each position information to image by this coding templet.
With reference to Fig. 5, the present invention is based on the compressed spectrum formation method of full color imaging, comprise that spectrum picture observation and spectrum picture reconstruct two walk greatly.
One. spectrum picture observation:
Step 1, initialization original spectrum information, establishes original spectrum information matrix f 0size be M × N × L, the spectral information of establishing any point is f 0(m, n, k), the spatial resolution that wherein M × N is spectral information, the spectral resolution that L is spectral information, the spectral coverage number of spectral information is L; M and n representation space dimension coordinate, k represents spectrum dimension coordinate, wherein 0≤m≤M-1,0≤n≤N-1,0≤k≤L-1.
Step 2, is divided into two-way by original spectrum information 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:
f 11 ( m , n , k ) = 1 2 f 0 ( m , n , k ) ,
f 21 ( m , n , k ) = 1 2 f 0 ( m , n , k ) .
Step 3, establishing coding function is T (m, n), with this coding function to first via spectral information f 11(m, n, k) encodes, and show that the first via is through the spectral information f after coding 12(m, n, k):
f 12 ( m , n , k ) = f 11 ( m , n , k ) T ( m , n ) = 1 2 f 0 ( m , n , k ) T ( m , n ) ,
Wherein, T (m, n) gets 0 or 1 randomly.
Step 4, by the spectral information f after first via coding 12(m, n, k) translation, moves to m-k by k the capable information of spectral coverage m capable, draws the spectral information f after translation 13(m, n, k) is:
f 13 ( m , n , k ) = f 12 ( m - k , n , k ) = f 11 ( m - k , n , k ) T ( m - k , n ) = 1 2 f 0 ( m - k , n , k ) T ( m - k , n ) .
Step 5, obtains observed image matrix.
(5a) respectively to the spectral information f after first via translation 13the spectral information f on (m, n, k) and the second tunnel 21(m, n, k) exposes, and adds up by the spectral information of each each spectral coverage of road, draws the observed result y of the first via 1the observed result y on (m, n) and the second tunnel 2(m, n):
y 1 ( m , n ) = &Sigma; k f 13 ( m , n , k ) = 1 2 &Sigma; k = 0 L - 1 f 0 ( m - k , n , k ) T ( m - k , n ) ,
y 2 ( m , n ) = &Sigma; k f 21 ( m , n , k ) = 1 2 &Sigma; k = 0 L - 1 f 0 ( m , n , k ) ,
(5b) this two-way observed result is designated as:
Y＝Hf，
Wherein Y={y 1(m, n), y 2(m, n) } be observed image matrix, H is Observation Operators, represents the observation model of system, f is original spectrum information.
Two. spectrum picture reconstruct:
Step 6, is sent to image reconstruction processor by observed image matrix Y.
Step 7, setting sparse base Ψ is wavelet basis or DCT base or Fourier basis, make original spectrum information f is sparse under sparse base Ψ, i.e. the projection coefficient Ψ of original spectrum information f under sparse base Ψ tin f, most numerical value is less than a certain specific threshold, and this threshold value need to be set by experiment, the threshold value difference that different sparse transform domains are corresponding, and it is wavelet basis that this example is set sparse base Ψ, setting threshold is adaptive threshold.
Step 8, image reconstruction processor, according to observed result Y and sparse base Ψ, is utilized nonlinear optimization method reconstruct original spectrum information f.
(8a) set optimization aim function be min (|| Ψ tf|| 1), wherein T representing matrix transposition, || || 1represent projection coefficient Ψ tf gets l 1norm, min () represents to get l 1the minimum value of norm;
(8b) using observed image matrix Y=Hf as constraint condition;
(8c) simultaneous optimization aim function and constraint condition, draws and meets constraint condition Y=Hf, and make || Ψ tf|| 1minimum f, is original spectrum information f.
Effect of the present invention can further illustrate by following emulation
1. simulated conditions
The hardware test platform of this experiment is: Intel Core i7CPU, dominant frequency 3.40GHz, internal memory 8GB; Software emulation platform is: windows764 bit manipulation system and Matlab2013b; Test pattern is: the disclosed spectrum picture in Columbia University, and spatial resolution is (512,512), between spectrum, resolution is 31.
2. emulation content and interpretation of result
For verifying validity of the present invention, two emulation experiments are implemented, two emulation experiments adopt different spectroscopic data cubes as original spectrum image, then utilize two step iterative algorithms to carry out spectrum picture reconstruct, calculate again the Y-PSNR PSNR of reconstruct spectrum picture according to reconstruction result, and compare with the reconstruction result of the CASSI of Duke University system.
Emulation 1, using the balloons figure of Columbia University as original spectrum image, carry out simulation observation by the CASSI of Duke University system and system of the present invention, and utilize respectively two step iterative algorithms to be reconstructed observed result, result is as shown in figure (6), wherein, figure (6a) is original spectrum image, i.e. balloons figure; Figure (6b) is for utilizing the reconstruction result after the CASSI of Duke University systematic observation; Figure (6c) is for utilizing the reconstruction result after systematic observation of the present invention.Under each reconstructed image, mark the PSNR of this wave band reconstruction result, because wave band number is more, thus only to wave band 1, wave band 5, wave band 22, wave band 31 is shown.
Emulation 2, using the egyptian_statue figure of Columbia University as original spectrum image, carry out simulation observation by CASSI system and the system of the present invention of Duke University, and utilize respectively two step iterative algorithms to be reconstructed observed result, result is as shown in figure (7), wherein, figure (7a) is original spectrum image, i.e. egyptian_statue figure; Figure (7b) is for utilizing the reconstruction result after the CASSI of Duke University systematic observation; Figure (7c) is for utilizing the reconstruction result after systematic observation of the present invention.Under each reconstructed image, mark the PSNR of this wave band reconstruction result, because wave band number is more, thus only to wave band 1, wave band 5, wave band 22, wave band 31 is shown.
Can find out from the experimental result of emulation, the spectrum picture obtaining with the present invention, details is more clear, profile is more complete, has had large increase than the CASSI system of Duke University; As shown in table (1), can find out from the PSNR of reconstructed image, the PSNR of reconstructed image of the present invention has the raising of 7-10dB than the PSNR of the CASSI of Duke University system reconfiguration image, on average in 8.5dB left and right.This two aspect has all fully confirmed premium properties of the present invention.
Table 1.PSNR contrast
PSNR/dB Fig. 6 Fig. 7
CASSI 31.4079 34.1876
The present invention 38.6715 43.9457

Claims (5)

1. the compressed spectrum imaging system based on full color imaging, comprise observation module, image reconstruction process module, observation module is observed spectrum picture, obtains observed image, image reconstruction process module is reconstructed observed image, obtain original spectrum image, it is characterized in that, observation module is divided into two, be compressed spectrum observation module (2) and panchromatic observation module (3), the front end of these two observation modules is provided with splitter module (1); The incident beam of collected spectrum picture is divided into through splitter module (1) the two-way light beam that information is identical, direction is different, the compressed encoding observation of spectrum picture is realized on one tunnel through compressed spectrum observation module (2), the panchromatic observation of spectrum picture is realized on another road through panchromatic observation module (3); Image reconstruction process module (4) is carried out the compressed encoding observation of the spectrum picture of this two modules output and panchromatic observed result to complete after simultaneous fusion the reconstruct of spectrum picture.
2. the compressed spectrum imaging system based on full color imaging according to claim 1, it is characterized in that, described compressed spectrum observation module (2), comprises first lens group (21), coding templet (22), dispersion element (23) and first surface array detector (24); Coding templet (22) is positioned at the rear end of first lens group (21), realize the coding to spectrum picture, dispersion element (23) is positioned at the rear end of coding templet (22), for the spectrum dimension information of translation spectrum picture, realize the dispersion of spectrum picture, first surface array detector (24) is positioned at the rear end of dispersion element (23), for observed image, obtains coding image information afterwards.
3. the compressed spectrum imaging system based on full color imaging according to claim 1, it is characterized in that, described panchromatic observation module (3), comprise the second lens combination (31) and the second planar array detector (32), the second planar array detector (32) is positioned at the second lens combination (31) rear end, for observed image, obtain full-colour image information, this full-colour image information comprises the spectral information of first surface array detector (24) record and the spectral information of loss.
4. the compressed spectrum formation method based on full color imaging, 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, wherein 0≤m≤M-1,0≤n≤N-1,0≤k≤L-1;
(1c) original spectrum 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:
f 11 ( m , n , k ) = 1 2 f 0 ( m , n , k ) ,
f 21 ( m , n , k ) = 1 2 f 0 ( m , n , k ) ;
(1d) utilize coding function T (m, n) to encode to first via spectral information, draw through the spectral information f after coding 12(m, n, k) is:
f 12 ( m , n , k ) = f 11 ( m , n , k ) T ( m , n ) = 1 2 f 0 ( m , n , k ) T ( m , n ) ,
Wherein, T (m, n) gets 0 or 1 randomly;
(1e) by k pixel of information translation of k spectral coverage in the spectral information after first via coding, move to m-k by k the capable information of spectral coverage m capable, draw translation spectral information f afterwards 13(m, n, k) is:
f 13 ( m , n , k ) = f 12 ( m - k , n , k ) = f 11 ( m - k , n , k ) T ( m - k , n ) = 1 2 f 0 ( m - k , n , k ) T ( m - k , n ) ;
(1f) spectral information on the first via and the second tunnel is exposed simultaneously, obtain the observed result y of the first via 1the observed result y on (m, n) and the second tunnel 2(m, n):
y 1 ( m , n ) = &Sigma; k f 13 ( m , n , k ) = 1 2 &Sigma; k = 0 L - 1 f 0 ( m - k , n , k ) T ( m - k , n ) ,
y 2 ( m , n ) = &Sigma; k f 21 ( m , n , k ) = 1 2 &Sigma; k = 0 L - 1 f 0 ( m , n , k ) ,
This two-way observed result is designated as:
Y＝Hf，
Wherein Y={y 1(m, n), y 2(m, n) } be observed image matrix, H is Observation Operators, represents the observation model of system, f is original spectrum information;
(2) spectrum picture reconstruction step:
(2a) observed image matrix Y is delivered to image reconstruction processor;
(2b) setting sparse base Ψ is wavelet basis or DCT base or Fourier basis, and make spectral information is sparse under sparse base Ψ;
(2c) image reconstruction processor, according to observed image matrix Y and sparse base Ψ, utilizes nonlinear optimization method to reconstruct original spectrum information f.
5. the compressed spectrum formation method based on full color imaging according to claim 4, is characterized in that, what step (2c) was described utilizes nonlinear optimization method reconstituting initial image, carries out as follows:
(2c1) set optimization aim function be min (|| Ψ tf|| 1), wherein T representing matrix transposition, || || 1represent projection coefficient Ψ tf gets l 1norm, min () represents to get l 1the minimum value of norm;
(2c2) using observed image matrix Y=Hf as constraint condition;
(2c3) simultaneous optimization aim function and constraint condition, draws and meets constraint condition Y=Hf, and make || Ψ tf|| 1minimum f, is original spectrum information f.
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