CN108414468A - Infrared spectrum wave band feature Enhancement Method based on wavelet transformation and nonlinear transformation - Google Patents
Infrared spectrum wave band feature Enhancement Method based on wavelet transformation and nonlinear transformation Download PDFInfo
- Publication number
- CN108414468A CN108414468A CN201710070138.2A CN201710070138A CN108414468A CN 108414468 A CN108414468 A CN 108414468A CN 201710070138 A CN201710070138 A CN 201710070138A CN 108414468 A CN108414468 A CN 108414468A
- Authority
- CN
- China
- Prior art keywords
- coefficient
- wave band
- wavelet
- infrared
- transformation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000009466 transformation Effects 0.000 title claims abstract description 45
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000001228 spectrum Methods 0.000 claims abstract description 37
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 25
- 238000000701 chemical imaging Methods 0.000 claims abstract description 22
- 230000003595 spectral effect Effects 0.000 claims abstract description 15
- 238000000605 extraction Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 230000001131 transforming effect Effects 0.000 claims description 3
- 230000003321 amplification Effects 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 2
- 230000002708 enhancing effect Effects 0.000 description 13
- 238000001514 detection method Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- VMXUWOKSQNHOCA-UKTHLTGXSA-N ranitidine Chemical compound [O-][N+](=O)\C=C(/NC)NCCSCC1=CC=C(CN(C)C)O1 VMXUWOKSQNHOCA-UKTHLTGXSA-N 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000011426 transformation method Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Photometry And Measurement Of Optical Pulse Characteristics (AREA)
Abstract
The present invention relates to infrared spectrum wave band feature Enhancement Methods, and in particular to a kind of infrared spectrum wave band feature Enhancement Method based on wavelet transformation and nonlinear transformation.One kind is provided during infrared spectrum wave band feature enhances, can effectively inhibit the method that background clutter interferes in infrared spectroscopic imaging.The present invention step be：One, the correlation between infrared spectroscopic imaging adjacent band is calculated using Pearson correlation coefficient, select the adjacent band of two pairs of Pearson correlation coefficient absolute value minimums and Subspace Decomposition is carried out to infrared spectroscopic imaging as node is decomposed.Two, wavelet decomposition is carried out to three sub-spaces respectively, obtains three Coefficient Spaces.Three, nonlinear transformation is carried out to three Coefficient Spaces respectively, obtains the enhanced infrared spectrum wave band of feature.The present invention highlights spectral signature difference small between spectrum and standard spectrum to be identified, effectively background clutter can be inhibited to interfere, and is suitable for infrared multispectral or high-spectral data target acquisition application.
Description
Technical field
The present invention relates to infrared spectrum wave band feature Enhancement Methods, and in particular to based on wavelet transformation and nonlinear transformation
Infrared spectrum wave band feature Enhancement Method.
Background technology
Infrared spectrum has unique advantage to the faint template signal detection under complex background illumination condition.Due to infrared biography
Sensor inevitably introduces photosignal noise in measurement process, meanwhile, external physical environmental factors, including air
The variation of translucency, extraneous temperature and humidity conditions etc. can bring the spectrum data gathering of sensor interference, can equally bring light
The influence that spectrum information measures, leads between gathered data and real spectrum data that there are errors.Therefore target point original is being obtained
After beginning spectral sequence information, necessary related pretreatment work need to be carried out, harmful noise component(s) in original spectrum signal is removed,
It is convenient for the useful component of discriminance analysis in stick signal as far as possible simultaneously, next can accurately to extract target point spectral information
And it carries out corresponding analysis and good condition is provided.
In the development of nearly many decades, infrared detection technique achieves rapid progress, however even to this day, various infrared spies
The performance for surveying identifying system is still difficult to meet the needs of practical application, and a major reason of such case is caused to be at present
System be still difficult to adapt to the variation of various complex environments, even its curve of spectrum of same substance also has under different environment
Prodigious difference, it is high so as to cause detection system rate of failing to report and false alarm rate, it is difficult to meet actual demand.Therefore, enhance
The robustness of system is the key that it is pushed to move towards practical application.In the method that short infrared wave band uses feature enhancing, pass through
The Spectral Radiation Information of comprehensive different moments different-waveband, is analyzed and is handled to the spectrum to be identified of acquisition, and then is obtained
One can accurately be indicated in each model the general spectrum of target optical spectrum characteristic be it is urgently to be resolved hurrily with it is perfect.
When the nicety of grading to image is exigent.We are using the method for Subspace Decomposition to infrared spectrogram
As being decomposed, i.e., according to the correlation properties between infrared spectroscopic imaging different-waveband, infrared spectrum all band is adaptively divided
Solution is at smaller subspace.In each sub-spaces, have stronger correlation, the present invention related using Pearson came between wave band
Property formula calculates the correlation of adjacent band.
During infrared spectrum target identification, the feature difference between spectrum and standard spectrum to be identified seems very sometimes
It is small, if not enhancing these faint SPECTRAL DIVERSITYs using any special measures and directly being handled using identical criterion, easily draw
Erroneous judgement is played, algorithm performance is caused to decline.Therefore the present invention carries out Subspace Decomposition to infrared spectrum first, then to each height sky
Between carry out wavelet decomposition, remain the subtle changing features of infrared spectrum, finally using nonlinear transformation to decomposition obtain be
Number space carries out feature enhancing, and small feature difference, thereby realizes to whole between prominent spectrum to be identified and standard spectrum
A infrared spectrum wave band feature enhancing.The present invention can tell small spectral signature difference between different atural objects well, together
When can effectively inhibit the noise jamming of background in image.
Invention content
It is an object of the invention to propose the infrared spectrum wave band feature enhancing side based on wavelet transformation and nonlinear transformation
Method, provide it is a kind of wavelet transformation and nonlinear transformation are carried out to infrared spectrum wave band subspace, to entire infrared spectrum
The method that wave band carries out feature enhancing.It can effectively inhibit the interference of the background clutter in infrared spectroscopic imaging.
The purpose of the present invention is what is be achieved through the following technical solutions：Infrared spectrogram is calculated using Pearson correlation coefficient
As the correlation between adjacent band, the adjacent band of two pairs of Pearson correlation coefficient absolute value minimums is selected and as decomposition
Node decomposes infrared spectroscopic imaging, generates three sub-spaces.Wavelet decomposition is carried out to three sub-spaces respectively, obtains three
A Coefficient Space.Nonlinear transformation is carried out to three Coefficient Spaces respectively again, reaching enhances entire infrared spectrum wave band feature
Purpose.
The flow chart of the present invention is as follows as shown in Figure 1, be divided into three steps：
Step 1：It calculates the correlation between infrared spectroscopic imaging adjacent band and carries out Subspace Decomposition.
1）Infrared spectroscopic imaging is read in, Pearson correlation coefficient is selected to calculate the phase between infrared spectroscopic imaging adjacent band
Guan Xing.
Selection Pearson correlation coefficient calculates the correlation of infrared spectroscopic imaging adjacent band, and detailed process is such as
Under：
High spectrum imageSIn each band imageM n Data available sequence (m n (1),m n (2),…,m n (k)) indicate, wherein 1≤n
≤N,NFor the wave band number of high spectrum image.
TheiA wave band andjPearson correlation coefficient between a wave band is represented by：
,
Wherein,,,Respectively,,Desired value,Respectively,Variance.
For withNThe high spectrum image of a wave band calculates the Pearson came between all adjacent bands according to above-mentioned formula
The absolute value of related coefficient, Pearson correlation coefficient is smaller, and the correlation between wave band is smaller.
2）Select the adjacent band of two pairs of Pearson correlation coefficient absolute value minimums as disjunction node, by infrared spectrogram
As resolving into three sub-spaces, after decomposition so that the related coefficient between each bands of a spectrum inside per sub-spaces is all higher than one
Threshold value, and the variation range of related coefficient is less than a threshold value.
Step 2：Wavelet decomposition is carried out to three sub-spaces respectively, obtains the wavelet coefficient of every sub-spaces and composition three
A Coefficient Space.
The high fdrequency component of each scale space can reflect after there is wavelet transformation multiple dimensioned characteristic, image to carry out wavelet decomposition
It is thin to remain infrared spectrum by carrying out wavelet transformation to infrared spectrum subspace for the abrupt information for going out image in different directions
Micro- changing features, usual target are present in the horizontal coefficients of first layer or the second layer, and background clutter is present in back layer
In horizontal coefficients.
Three layers of wavelet decomposition are carried out to three sub-spaces respectively first, for ease of calculation and shadow can be generated to Objective extraction
Loud only horizontal component, using Haar wavelet basis, the wavelet coefficient of the horizontal component of 3 layers of extraction：f 1H、f 2H、f 3H, wherein mesh
Mark is primarily present in the 1st layer or the 2nd layer of horizontal coefficients, and background clutter is primarily present in the 3rd layer of horizontal coefficients.
It is right respectivelyf 2H、f 3HIt, will using closest interpolation methodf 2HWithf 3HBe resized tof 1HIt is identical, it obtainsf u1 2H、f u2 3H, whereinu1Indicate one times of dimension enlargement,u2Indicate 4 times of dimension enlargement.In order to expand the clutter region of background, therefore make 3
Wavelet coefficient is corresponding consistent on spatial position under a scale, and expansion calculating, structure are carried out to the wavelet coefficient after interpolation amplification
Element isg 1 Withg 2 , wavelet coefficientf 0 2H、f 0 3HIt is represented by：
,
,
Wherein,Indicate that above-mentioned expansion calculates, structural element、。
It is respectively completed the wavelet decomposition of three sub-spaces, obtains three Coefficient Spaces being made of wavelet coefficientK 1、K 2、K 3。
Step 3：Nonlinear transformation is carried out to three Coefficient Spaces respectively, obtains the enhanced infrared spectrum wave band of feature.
Spectral signature unobvious are to restrict the major reason of recognizer performance, therefore use nonlinear transformation to subtle
Spectral signature variation is enhanced.It is considered as following features when tectonic transition function：It is special between spectrum to be identified and standard spectrum
It levies at the smaller wave band of difference, it is little to handle the latter two the change of divergence by the function；Between spectrum to be identified and standard spectrum
At the larger wave band of feature difference, handles the latter two SPECTRAL DIVERSITYs by the function and be remarkably reinforced.
Using exponential function respectively to Coefficient SpaceK 1、K 2、K 3Nonlinear transformation is carried out, transforming function transformation function is represented by：
,
Wherein,, c andFor regulatory factor, generally takeAnd, W is to be obtained after nonlinear transformation
The new space arrived,Expression pairIn each component takePower.
After nonlinear transformation, small spectral signature changes and is enhanced in spectrum to be identified, applied to red
When external spectrum target identification, background clutter interference problem in infrared spectroscopic imaging can be effectively inhibited.
The present invention has the following advantages that compared with prior art：
The present invention carries out feature enhancing using wavelet decomposition and nonlinear transformation to infrared spectrum wave band, effectively inhibits infrared light
Background clutter interference problem in spectrogram picture highlights spectral signature difference small between spectrum and standard spectrum to be identified.We
Method is calculated first with similitude of the Pearson correlation coefficient between infrared spectrum adjacent band and carries out subspace and drawn
Point, wavelet decomposition then is carried out to three sub-spaces respectively, obtained Coefficient Space describes in infrared spectroscopic imaging well
The minutia of spectral signal.Nonlinear transformation finally is carried out to Coefficient Space so that small spectral signature changes to obtain
Enhancing.This method implementation steps are clear, and enhancing effect is preferable, are a kind of preferably infrared spectrum wave band feature Enhancement Methods.
Description of the drawings
Fig. 1 is the flow chart of the present invention.
Fig. 2 is the 3-D view that 25 × 25pixel size original images are chosen in embodiment.
Fig. 3 is to carry out the enhanced 3-D view of feature using the present invention.
Specific implementation mode
Illustrate the specific implementation mode of the present invention with reference to embodiment and attached drawing：It will be based on wavelet transformation and non-linear change
The infrared spectrum wave band feature Enhancement Method changed applies in the enhancing of infrared spectrum feature.
The description of infrared spectroscopic imaging data is provided first：Experimental subjects is Kennedy Space Center spectrum pictures
Data, wave-length coverage 400nm-2500nm, including 224 wave bands, size is 512 × 614pixel.It is eliminated in data set
Several wave bands of Atmospheric Absorption interference, leave 176 wave bands and are used as experimental subjects, chosen from data set size for 25 ×
25pixel and includes the image data of a target point and be denoted asIMG (25×25,176)。
Nonlinear transformation regulatory factor c=0.1, ε=3 are selected in the present embodiment.
Execute step 1：Input infrared spectroscopic imagingIMG (25×25,176)Infrared spectrum is calculated using Pearson correlation coefficient
The related coefficient of 175 pairs of adjacent bands is obtained in correlation between image adjacent band, and it is minimum to select two absolute values
Related coefficient simultaneously carries out Subspace Decomposition in this, as 176 wave bands of node pair are decomposed, and obtains three sub-spaces.
Execute step 2：Wavelet decomposition is carried out to executing three obtained subspace in step 1 respectively, and respectively by three
Group wavelet coefficient forms three Coefficient SpacesK 1、K 2、K 3。
Execute step 3：Using transforming function transformation function respectively to three Coefficient SpacesK 1、K 2、K 3Nonlinear transformation is carried out, obtains three
A new spaceW 1、W 2、W 3, and by three enhanced infrared spectroscopic imagings of new space composition characteristic.
The present embodiment conclusion：Feature enhancing, the 3-D view of original image are carried out to infrared spectroscopic imaging using the present invention
It is as shown in Figures 2 and 3 with the enhanced 3-D view difference of feature is carried out using the present invention（The longitudinal axis is all each pixel institute in two figures
There is the average value of band spectrum data, remaining bidimensional correspondence image dimension）, it is seen that the latter's targets improvement is with obvious effects.It is of the invention first
Feature enhancing is carried out to the Coefficient Space that decomposition obtains first with nonlinear transformation, it is micro- between prominent spectrum to be identified and standard spectrum
Then small feature difference carries out feature enhancing, prominent light to be identified using nonlinear transformation to the Coefficient Space that decomposition obtains
Small feature difference between spectrum and standard spectrum.It can be seen that Target Signal Strength and background signal intensities that the present invention identifies
Difference is apparent, can tell small spectral signature difference between different atural objects well, simultaneously effective inhibit in image
Background clutter interference, most background signals have obtained good inhibition.
Claims (5)
1. a kind of infrared spectrum wave band feature Enhancement Method based on wavelet transformation and nonlinear transformation, it is characterised in that it includes
Following steps：
Step 1：It calculates the correlation between infrared spectroscopic imaging adjacent band and carries out Subspace Decomposition；
Step 2：Wavelet decomposition is carried out to three sub-spaces respectively, the wavelet coefficient of every sub-spaces is obtained and composition three is
Number space；
Step 3：Nonlinear transformation is carried out to three Coefficient Spaces respectively, obtains the enhanced infrared spectrum wave band of feature.
2. the infrared spectrum wave band feature Enhancement Method according to claim 1 based on wavelet transformation and nonlinear transformation,
It is characterized in that the step one specifically includes：
1）Infrared spectroscopic imaging is read in, Pearson correlation coefficient is selected to calculate the correlation between infrared spectroscopic imaging adjacent band
Property；
2）Select the adjacent band of two pairs of Pearson correlation coefficient absolute value minimums as disjunction node, by infrared spectroscopic imaging point
Solution is at three sub-spaces, after decomposition so that and the related coefficient between each bands of a spectrum inside per sub-spaces is all higher than a threshold value,
And the variation range of related coefficient is less than a threshold value.
3. the infrared spectrum wave band feature Enhancement Method according to claim 2 based on wavelet transformation and nonlinear transformation,
It is characterized in that the selection Pearson correlation coefficient calculates the correlation of infrared spectroscopic imaging adjacent band, specifically
Process is as follows：
High spectrum imageSIn each band imageM n Data available sequence (m n (1),m n (2),…,m n (k)) indicate, wherein 1≤n
≤N,NFor the wave band number of high spectrum image；
TheiA wave band andjPearson correlation coefficient between a wave band is represented by：
,
Wherein,,,Respectively,,Desired value,Respectively,Variance；
For withNThe high spectrum image of a wave band calculates the correlation of the Pearson came between all adjacent bands according to above-mentioned formula
The absolute value of coefficient, Pearson correlation coefficient is smaller, and the correlation between wave band is smaller.
4. the infrared spectrum wave band feature Enhancement Method according to claim 1 based on wavelet transformation and nonlinear transformation,
It is characterized in that the step two is：
Three layers of wavelet decomposition are carried out to three sub-spaces respectively first, for ease of calculation and Objective extraction can be had an impact
Only horizontal component, using Haar wavelet basis, the wavelet coefficient of the horizontal component of 3 layers of extraction：f 1H、f 2H、f 3H, wherein target master
It is present in the 1st layer or the 2nd layer of horizontal coefficients, background clutter is primarily present in the 3rd layer of horizontal coefficients；
It is right respectivelyf 2H,f 3HIt, will using closest interpolation methodf 2HWithf 3HBe resized tof 1HIt is identical, it obtainsf u1 2H,f u2 3H,
Whereinu1Indicate one times of dimension enlargement,u2Indicate 4 times of dimension enlargement.In order to expand the clutter region of background, therefore make 3 scales
Lower wavelet coefficient is corresponding consistent on spatial position, carries out expansion calculating to the wavelet coefficient after interpolation amplification, structural element isg 1 Withg 2 , wavelet coefficientf 0 2H,f 0 3HIt is represented by：
,
,
Wherein,,,Indicate that above-mentioned expansion calculates；
It is respectively completed the wavelet decomposition of three sub-spaces, obtains three Coefficient Spaces being made of wavelet coefficientK 1、K 2、K 3。
5. the infrared spectrum wave band feature Enhancement Method according to claim 1 based on wavelet transformation and nonlinear transformation,
It is characterized in that the step three is：
Using exponential function respectively to Coefficient SpaceK 1、K 2、K 3Nonlinear transformation is carried out, transforming function transformation function is represented by：
,
Wherein,, c andFor regulatory factor, generally takeAnd, W is to be obtained after nonlinear transformation
The new space arrived,Expression pairIn each component takePower；
After nonlinear transformation, small spectral signature changes and is enhanced in spectrum to be identified, applied to infrared light
When composing target identification, background clutter interference problem in infrared spectroscopic imaging can be effectively inhibited.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710070138.2A CN108414468A (en) | 2017-02-09 | 2017-02-09 | Infrared spectrum wave band feature Enhancement Method based on wavelet transformation and nonlinear transformation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710070138.2A CN108414468A (en) | 2017-02-09 | 2017-02-09 | Infrared spectrum wave band feature Enhancement Method based on wavelet transformation and nonlinear transformation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108414468A true CN108414468A (en) | 2018-08-17 |
Family
ID=63124792
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710070138.2A Pending CN108414468A (en) | 2017-02-09 | 2017-02-09 | Infrared spectrum wave band feature Enhancement Method based on wavelet transformation and nonlinear transformation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108414468A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111189784A (en) * | 2020-01-14 | 2020-05-22 | 哈尔滨工业大学 | Method for identifying stamping signature sequence based on polarization spectrum imaging |
| CN111709637A (en) * | 2020-06-11 | 2020-09-25 | 中国科学院西安光学精密机械研究所 | Qualitative analysis method for interference degree of spectral curve |
| CN111968067A (en) * | 2019-05-20 | 2020-11-20 | 北京航空航天大学 | Short wave infrared image processing method, device and equipment based on silicon sensor camera |
| CN112184839A (en) * | 2020-10-21 | 2021-01-05 | 湖北省地震局(中国地震局地震研究所) | Post-earthquake reconstruction map information encryption method facing space situation |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590716A (en) * | 2012-02-11 | 2012-07-18 | 广东电网公司电力科学研究院 | Power cable partial discharge signal denoising method based on wavelet analysis |
| CN103400358A (en) * | 2013-08-27 | 2013-11-20 | 中国兵器工业导航与控制技术研究所 | Wavelet information redundancy based infrared image denoising algorithm |
| CN103617630A (en) * | 2013-12-18 | 2014-03-05 | 国网重庆潼南县供电有限责任公司 | Infrared image dividing method and system for power system equipment based on wavelet analysis |
-
2017
- 2017-02-09 CN CN201710070138.2A patent/CN108414468A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590716A (en) * | 2012-02-11 | 2012-07-18 | 广东电网公司电力科学研究院 | Power cable partial discharge signal denoising method based on wavelet analysis |
| CN103400358A (en) * | 2013-08-27 | 2013-11-20 | 中国兵器工业导航与控制技术研究所 | Wavelet information redundancy based infrared image denoising algorithm |
| CN103617630A (en) * | 2013-12-18 | 2014-03-05 | 国网重庆潼南县供电有限责任公司 | Infrared image dividing method and system for power system equipment based on wavelet analysis |
Non-Patent Citations (2)
| Title |
|---|
| 于天河: "针对人眼视觉特性的红外图像增强技术研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
| 徐长发、李国宽: "《实用小波方法第2版》", 31 January 2004, 华中科技大学出版社 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111968067A (en) * | 2019-05-20 | 2020-11-20 | 北京航空航天大学 | Short wave infrared image processing method, device and equipment based on silicon sensor camera |
| CN111968067B (en) * | 2019-05-20 | 2023-08-22 | 北京航空航天大学 | Short wave infrared image processing method, device and equipment based on silicon sensor camera |
| CN111189784A (en) * | 2020-01-14 | 2020-05-22 | 哈尔滨工业大学 | Method for identifying stamping signature sequence based on polarization spectrum imaging |
| CN111709637A (en) * | 2020-06-11 | 2020-09-25 | 中国科学院西安光学精密机械研究所 | Qualitative analysis method for interference degree of spectral curve |
| CN111709637B (en) * | 2020-06-11 | 2023-08-22 | 中国科学院西安光学精密机械研究所 | Qualitative analysis method for interference degree of spectrum curve |
| CN112184839A (en) * | 2020-10-21 | 2021-01-05 | 湖北省地震局(中国地震局地震研究所) | Post-earthquake reconstruction map information encryption method facing space situation |
| CN112184839B (en) * | 2020-10-21 | 2022-07-08 | 湖北省地震局(中国地震局地震研究所) | Post-earthquake reconstruction map information encryption method facing space situation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108414468A (en) | Infrared spectrum wave band feature Enhancement Method based on wavelet transformation and nonlinear transformation | |
| CN111145131A (en) | Infrared and visible light image fusion method based on multi-scale generation type countermeasure network | |
| US8810658B2 (en) | Estimating a visible vector representation for pixels in an infrared image | |
| CN105389591B (en) | A kind of verification method of infrared imaging sensor typical effect emulation | |
| Johnson et al. | AutoGAD: An improved ICA-based hyperspectral anomaly detection algorithm | |
| CN104318243B (en) | High-spectral data dimension reduction method based on rarefaction representation and empty spectrum Laplce's figure | |
| CN114120176A (en) | Behavior analysis method for fusion of far infrared and visible light video images | |
| CN106124050B (en) | A kind of high spectrum sub-pixel target Blind extracting detection method based on subspace | |
| CN107689038A (en) | A kind of image interfusion method based on rarefaction representation and circulation guiding filtering | |
| WO2020027167A1 (en) | System, method, and non-transitory, computer-readable medium containing instructions for image processing | |
| Vafadar et al. | Hyperspectral anomaly detection using outlier removal from collaborative representation | |
| Zhang et al. | A Fast and Adaptive Method for Determining $ K_ {1} $, $ K_ {2} $, and $ K_ {3} $ in the Tensor Decomposition-Based Anomaly Detection Algorithm | |
| CN113420718B (en) | Hyperspectral anomaly detection method and device based on component analysis and sparse constraint | |
| JP2000251052A5 (en) | Data management method and equipment, recording medium | |
| CN107239768A (en) | A kind of high spectrum image object detection method based on tensor principal component analysis dimensionality reduction | |
| CN106650681A (en) | Method for target identification of spectral-spatial cooperative hyperspectral image based on low-rank matrix recovery | |
| CN108648176A (en) | A kind of non-linear hyperspectral image abnormal object detection method based on neural network | |
| CN117115669A (en) | Object-level ground object sample self-adaptive generation method and system with double-condition quality constraint | |
| Schott et al. | A subpixel target detection technique based on the invariance approach | |
| Acito et al. | Hyperspectral signal subspace identification in the presence of rare vectors and signal-dependent noise | |
| CN110717485A (en) | Hyperspectral image sparse representation classification method based on local preserving projection | |
| JP6826277B2 (en) | Image processing system, image processing method, and image processing program | |
| CN111899226B (en) | Hyperspectral image target prior optimization method based on multitask sparse learning | |
| CN108007569A (en) | A kind of multispectral image Calibration Method | |
| JPH0749945A (en) | Discriminating device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180817 |
|
| WD01 | Invention patent application deemed withdrawn after publication |