CN106651793B - A kind of PST Stray Light Test data processing method - Google Patents
A kind of PST Stray Light Test data processing method Download PDFInfo
- Publication number
- CN106651793B CN106651793B CN201611073975.2A CN201611073975A CN106651793B CN 106651793 B CN106651793 B CN 106651793B CN 201611073975 A CN201611073975 A CN 201611073975A CN 106651793 B CN106651793 B CN 106651793B
- Authority
- CN
- China
- Prior art keywords
- pst
- ccd
- stray light
- energy
- test
- 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.)
- Active
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 77
- 238000003672 processing method Methods 0.000 title claims abstract description 14
- 238000009826 distribution Methods 0.000 claims abstract description 12
- 238000004458 analytical method Methods 0.000 claims abstract description 9
- 239000011159 matrix material Substances 0.000 claims description 35
- 238000000034 method Methods 0.000 claims description 29
- 230000002068 genetic effect Effects 0.000 claims description 8
- 201000009310 astigmatism Diseases 0.000 claims 1
- 230000005611 electricity Effects 0.000 claims 1
- 238000012545 processing Methods 0.000 abstract description 20
- 238000004364 calculation method Methods 0.000 abstract description 6
- 230000005540 biological transmission Effects 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 8
- 238000000605 extraction Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000002427 irreversible effect Effects 0.000 description 2
- KBPHJBAIARWVSC-RGZFRNHPSA-N lutein Chemical compound C([C@H](O)CC=1C)C(C)(C)C=1\C=C\C(\C)=C\C=C\C(\C)=C\C=C\C=C(/C)\C=C\C=C(/C)\C=C\[C@H]1C(C)=C[C@H](O)CC1(C)C KBPHJBAIARWVSC-RGZFRNHPSA-N 0.000 description 2
- 229960005375 lutein Drugs 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- KBPHJBAIARWVSC-XQIHNALSSA-N trans-lutein Natural products CC(=C/C=C/C=C(C)/C=C/C=C(C)/C=C/C1=C(C)CC(O)CC1(C)C)C=CC=C(/C)C=CC2C(=CC(O)CC2(C)C)C KBPHJBAIARWVSC-XQIHNALSSA-N 0.000 description 2
- FJHBOVDFOQMZRV-XQIHNALSSA-N xanthophyll Natural products CC(=C/C=C/C=C(C)/C=C/C=C(C)/C=C/C1=C(C)CC(O)CC1(C)C)C=CC=C(/C)C=CC2C=C(C)C(O)CC2(C)C FJHBOVDFOQMZRV-XQIHNALSSA-N 0.000 description 2
- 235000008210 xanthophylls Nutrition 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000001429 visible spectrum Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M11/00—Testing of optical apparatus; Testing structures by optical methods not otherwise provided for
- G01M11/02—Testing optical properties
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Photometry And Measurement Of Optical Pulse Characteristics (AREA)
Abstract
A kind of PST Stray Light Test data processing method is as follows: (1) comprehensive assessment based on remote sensing camera PST characteristic and ccd detector performance, is finally inversed by the requirement for surveying environment stray light；(2) it by carrying out Difference Calculation to Stray Light Test image under different conditions, separates, extract distribution of all kinds of test noises on ccd detector, to complete the removing of test noise；(3) mathematical analysis is carried out to the test image after peel test noise, parses the scale of image data and the characteristic distributions of numerical value；(4) according to resulting data scale and determining stable, efficient, the accurate subsequent processing algorithm of numeric distribution feature is parsed, the test of each column stray energy transmission capacity of ccd detector is completed.Present invention is mainly used for PST Stray Light Test data processings, can reduce test macro for the technical requirements of instrument and equipment, portable and versatility with higher can satisfy the demand of all kinds of Stray Light Test data processings.
Description
Technical field
The present invention relates to a kind of PST Stray Light Test data processing methods, distant suitable for various types, various spectral coverage space flight
The data processing and analysis of sensor imaging system high-precision PST Stray Light Test, belong to Aid of Space Remote Sensing Technology field.
Background technique
The accuracy and precision of PST Stray Light Test data processing are largely determined by test noise removing and data processing
Two aspects of algorithm.The former mainly passes through the method for image procossing to CCD dark current noise, test environment stray light noise
(being introduced by testing light source, darkroom etc.) etc. is separated and is extracted, and the quantitative and positioning analysis to each noise like is realized, to reach
To the purpose of removing；The latter is the analysis carried out after separation, extraction and the rejecting for completing noise for valid data and place
Reason, it is contemplated that PST Stray Light Test generate data volume it is huge, Processing Algorithm must have both stability is good, accuracy is high, receive
Fireballing feature is held back, and to have good portable and versatility, all kinds of optical systems can be met in varying environment
The demand of lower PST Stray Light Test data processing.The present invention solves in PST Stray Light Test data handling procedure, and test is made an uproar
Acoustic fix ranging and removing inaccuracy or can not remove, large-scale data Treatment Stability and accuracy not can guarantee, algorithm is removable
The problems such as plant property and versatility are poor.
Summary of the invention
Technology of the invention solves the problems, such as: overcoming the shortage of prior art, proposes a kind of PST Stray Light Test data processing
Method, solves separation, extraction and the rejecting of all kinds of test noises in PST Stray Light Test data handling procedure, and based at
As the comprehensive assessment of system PST characteristic and ccd detector performance, the requirement for surveying environment stray light can be finally inversed by；Together
When, carry out the processing of large-scale image and data automatically with the numerical analyses such as least square method, genetic algorithm and operation method,
Directly output PST is as a result, improve portable and versatility.
Technical solution of the invention are as follows: a kind of PST Stray Light Test data processing method, steps are as follows:
(1) according to the point source transmitance PST of remote sensing camera and ccd detector electric property, including photon efficiency, really
Determine environment stray light ENERGY EEnv, according to environment stray light ENERGY EEnv, determine the energy of light source for being radiated at remote sensing camera entrance；
(2) under light source output different-energy state, remote sensing camera ccd image is acquired, then ccd image is carried out
Difference Calculation is obtained with separating, extracting distribution of all kinds of test noises on ccd detector to complete the removing of test noise
To the ccd image for having removed noise；
(3) mathematical analysis is carried out to the ccd image for having removed noise in step (2), parses expression and has removed noise
The matrix of ccd image whether there is inverse matrix；
(4) inverse matrix if it exists, then directly solve the matrix for the ccd image for having removed noise, inverse if it does not exist
Matrix is then selected least square method or genetic algorithm or dichotomy to solve the matrix for the ccd image for having removed noise, is obtained
To each column stray energy carry-over factor of remote sensing camera CCD, i.e. the contribution of each pixel stray light total for the row in the every row of CCD
Ability, with the image rectification for the later period.
The step (1) is according to the point source transmitance PST and ccd detector electric property of remote sensing camera, including light
Sub- efficiency determines environment stray light ENERGY EEnvMethod are as follows: calculate separately out under the receptible spuious light energy of remote sensing camera institute
Limit EPSTWith CCD minimum electrical noise ENERGY ECCD, then environment stray light ENERGY E is surveyedEnvIt is required that EEnv<(EPST+ECCD)。
In the step (2), point source transmitance is PST, and the pixel scale of CCD is that rows row and cols are arranged, at i-th
Under energy state, it is E that light source, which inputs spuious light energy,0i(i=1,2 ..., n+1), l row, the kth column pixel of ccd detector connect
The stray energy received is E'(i)(l,k)(l=1,2 ... rows；K=1,2 ... cols).
So, the input energy difference DELTA E twice adjacent with jth of jth+10j=E0(j+1)-E0j(j=1,2 ..., n)；And
The stray energy difference that l row, kth column pixel receive twice is Δ E'(j)(l,k)=E'(j+1)(l,k)-E'(j)(l,k), then
The difference of the pixel is Diff (E twicej)(l,k)=Δ E0iPST-ΔE'(j)(l.k), the difference of this pixel is averagedWherein δiFor the weight of difference, the difference that can similarly acquire other pixels is flat
Mean value, and the difference average value of each pixel constitutes rows × cols rank matrix, and corresponds to rows × cols rank picture of CCD
Member, as test noise.Ccd detector each received gross energy of pixel under the 1st energy state subtracts the difference of the pixel
Dividing average value is to realize the removing for having surveyed test noise.
Data pathosis in the step (3) refers to the solution difficulty or ease journey of rows × cols rank picture element matrix of CCD
Degree.
The step (4) is selected according to the matrix for indicating to have removed the ccd image of noise is parsed with the presence or absence of inverse matrix
Select least square method or generalized inverse least square method, genetic algorithm, dichotomy, it can be ensured that the stability and operation of calculating process
As a result accuracy, and guarantee that operation has higher convergence rate, meet the needs of all kinds of Stray Light Test data processings.
The advantages of the present invention over the prior art are that:
(1) integrated use of the present invention image procossing, difference algorithm, least square method, genetic algorithm, unmanaged code skill
The multiple technologies means such as art solve in PST Stray Light Test data handling procedure, test noise positioning and removing inaccuracy or
Person can not remove, large-scale data Treatment Stability and accuracy not can guarantee, algorithm is portable and versatility is poor etc. asks
Topic has stronger practicability and versatility, is the key technology in PST test process,
(2) present invention can reduce test macro for the technical requirements of test equipment equipment, while guarantee measuring accuracy.
The present invention can satisfy the demand of all kinds of Stray Light Test data processings, and reduce the early investment of such test macro.
Detailed description of the invention
Fig. 1 is PST Stray Light Test data processing method flow chart of the present invention；
Fig. 2 is a series of images to be processed acquired during the test.
Specific embodiment
A kind of PST Stray Light Test data processing method of the present invention is as follows: (1) being based on remote sensing camera PST characteristic and CCD
The comprehensive assessment of detector performance is finally inversed by the requirement for surveying environment stray light；(2) by flash ranging spuious under different conditions
Attempt to separate as carrying out Difference Calculation, extract distribution of all kinds of test noises on CCD focal plane, to complete test noise
Removing；(3) mathematical analysis is carried out to the test image after peel test noise, parses the scale of image data and point of numerical value
Cloth feature；(4) determine that stable, efficient, accurate subsequent processing is calculated according to the resulting data scale of parsing and numeric distribution feature
Method completes the drafting of PST curve and the judgement of spuious Xanthophyll cycle angle.Present invention is mainly used for PST Stray Light Test data
Processing can reduce test macro for the technical requirements of instrument and equipment, portable and versatility with higher, Ke Yiman
The demand of all kinds of Stray Light Test data processings of foot.
A kind of PST Stray Light Test data processing method of the invention, including early period test noise removing and it is subsequent
Large-scale data automatically processes, and is related to the technological means such as image procossing, algorithm development, the system integration.Firstly, in test process
The image of acquisition carries out difference algorithm processing, obtains D/N Distribution value variation of CCD pixel array under the conditions of different stray lights,
And the characteristic distributions of combination CCD dark current noise, test environment stray light noise etc., to separate, extract all kinds of test noises
Distribution on ccd detector；Secondly, the data volume of PST Stray Light Test is huge, it is necessary to assure Processing Algorithm has powerful
Stability and reliable accuracy avoid the interference of singular point to cause calculated result that can not restrain；Finally, removable in order to improve
Plant property and versatility use unmanaged code technology, realize rapid deployment, avoid due to interface communication, registration table note
The problem of the brings program stability differences such as volume.The present invention is as a kind of novel PST Stray Light Test data processing technique, no
It only can satisfy the data processing needs of the PST Stray Light Test of all kinds of space remote sensors under various circumstances, and can be
While guaranteeing measuring accuracy, requirement of the test macro for instrument and equipment, save the cost are reduced.
Embodiment 1
To visible spectrum, focal length value 40mm, relative aperture 0.5, CCD pixel array is the remote sensing phase of rectangular array
Machine Stray Light Test image data is handled.
As shown in Figure 1, the specific steps of the present invention are as follows:
(1) determination of environmental energy is tested
It is calculated using point source transmitance PST of the Analysis for Stray Light such as Fred, LightTools software to remote sensing camera,
Obtain the point source transmitance PST of remote sensing camera.
Stray light energy lower limit E is calculated separately out according to following two formulaPSTWith CCD minimum electrical noise ENERGY ECCD:
In upper two formula, S/N is the signal-to-noise ratio of camera, N 'eFor the image-forming electron number of camera CCD, N 'ei(i=1 ..., n) be
The electron number that each noise like of camera CCD generates, such as dark current noise.d2For pixel area；T is the time of integration；λ is photon
Wavelength；η is photon efficiency；H is Planck's constant；C is the light velocity in vacuum.
Then to survey environment stray light ENERGY EEnvThere is following requirement:
EEnv<(EPST+ECCD)
(2) determination of energy of light source
According to survey environment stray light ENERGY EEnvAnd the reflectivity γ of camera outer surface is calculated and is radiated at remote sensing camera and enters
The maximum value E of mouth energy of light source0(n+1):
E0(n+1)=EENV/γ
(3) Image Acquisition
By E0(n+1)Start point n times and gradually decreases the output energy of light source until E01, that is, meet E0(n+1)>E0n>…>E0i>…
E01(1 < i < n), and under the output of each energy, the image that acquisition camera generates obtains CCD pixel array in different spuious striations
D/N value under part.6 images are acquired in this example altogether, as shown in Fig. 2, i.e. n=5；
(4) peel test noise
The pixel number of each width image is identical in Fig. 2, and is arranged according to 50 × 1000 matrix, i.e. rows
=50, cols=1000.Remember i-th (i=1,2 ..., 6) width figure in, l (l=1,2 ..., 50) row, kth (k=1,2 ...,
1000) stray energy that column pixel receives is E (i)(l,k), calculation method is as follows:
Wherein, DN (i)(l,k)For in the i-th width figure, the DN value of l row, kth column pixel, k is that CCD is turned by electronics to DN value
Change coefficient.
The l row of stray energy and jth width image that then l row, the kth column pixel of+1 width image of jth receive, kth
The difference Δ E (j) for the stray energy that column pixel receives(l,k)(j=1,2 ..., 5) it can be calculated by following formula:
ΔE'(j)(l,k)=E'(j+1)(l,k)-E'(j)(l,k)
It is E that 6 width images in Fig. 2 from top to bottom, which respectively correspond light source and input spuious light energy,01、E02、E03、E04、E05、
E06, then the difference Δ E of the input energy of+1 width image of jth and jth width image0jAre as follows:
ΔE0j=E0(j+1)-E0(j)
So, l row, the kth column pixel difference Diff (E of+1 width image of jth and jth width imagej)(l,k)It can be by following formula meter
It calculates:
Diff(E'j)(l,k)=Δ E0iPST-ΔE'(j)(l,k)
The then difference average value of this 6 width image l row, kth column pixelCalculation method is as follows:
Wherein, δjFor the weight of difference, it can be calculated and be acquired by following formula:
Above-mentioned identical calculations are done to other each pixels, the rows being made of the difference average value of each pixel can be obtained
× cols rank matrixAnd remember that the rows × cols rank matrix being made of the stray energy of the 1st each pixel of width image is E
(1), then the matrix E ' after peel test noise0Are as follows:
(5) E ' is parsed0With the presence or absence of inverse matrix
The large-scale data of peel test noise is handled according to data scale and numeric distribution, analyzes Processing Algorithm
Different strategies can be used according to the scale and characteristic distributions of input data, to achieve the purpose that fast convergence.If E '0It is not
Square matrix, then E '0It is irreversible, inverse matrix is not present；If E '0For square matrix, but determinant | E'0|=0, then E'0It is irreversible, it does not deposit
In inverse matrix；If E'0For square matrix, and determinant | E'0| ≠ 0, then E'0Reversible, there are inverse matrixs；Due to pixel in this example
Columns is far longer than line number, so E '0It is not square matrix, inverse matrix is not present；
(6) solution of spuious carry-over factor
E'0There are inverse matrixs, are directly solved；
E'0There is no inverse matrixs, are solved using least square method, genetic algorithm etc.；
Due to E' in this example0There is no inverse matrixs, therefore are solved using least square method, as a result as follows:
X=((E'0)TE'0)-1(E'0)TB
In above formula, X is the vector that the spuious carry-over factor respectively arranged to be asked is 1 × cols rank, and Cong Gelie's is spuious
Carry-over factor can interpret the information such as spuious Xanthophyll cycle angle, abnormal incident angle., (E'0)TFor matrix E '0Transposition, B is
The vector of one rank of rows × 1 characterizes the stray light summation of each row of CCD.
Embodiment 2
When handling the image generated in other cameras PST test process, analytic process is same as above, and can obtain similar knot
Fruit.Only handling square CCD image, and matrix E '0There are when inverse matrix, the solution of X is as follows:
X=(E'0)-1B
Integrated use of the present invention image procossing, difference algorithm, least square method, genetic algorithm, unmanaged code technology
Etc. multiple technologies means, solve in PST Stray Light Test data handling procedure, test noise positioning and removing inaccuracy or
It can not remove, the problems such as large-scale data Treatment Stability and accuracy not can guarantee, algorithm is portable and versatility is poor,
With stronger practicability and versatility, it is the key technology in PST test process, can reduce test macro for tester
The technical requirements of device equipment, while guaranteeing measuring accuracy.
After tested and the experiment present invention can satisfy the demand of all kinds of Stray Light Test data processings, and reduce this class testing
The early investment of system.
Unspecified content belongs to the common knowledge of this field in the present invention.
Claims (5)
1. a kind of PST Stray Light Test data processing method, it is characterised in that steps are as follows:
(1) according to the point source transmitance PST of remote sensing camera and ccd detector electric property, including photon efficiency, ring is determined
Border stray light ENERGY EEnv, according to environment stray light ENERGY EEnv, determine the energy of light source for being radiated at remote sensing camera entrance；
(2) under light source output different-energy state, remote sensing camera ccd image is acquired, then difference is carried out to ccd image
It calculates, is shelled with separating, extracting distribution of all kinds of test noises on ccd detector to complete the removing of test noise
Ccd image from noise；
(3) mathematical analysis is carried out to the ccd image for having removed noise in step (2), parses the CCD figure for indicating to have removed noise
The matrix of picture whether there is inverse matrix；
(4) inverse matrix if it exists, then directly solve the matrix for the ccd image for having removed noise, if it does not exist inverse matrix,
It then selects least square method or genetic algorithm or dichotomy to solve the matrix for the ccd image for having removed noise, obtains distant
Feel each column stray energy carry-over factor of camera CCD, i.e. the contribution energy of each pixel stray light total for the row in the every row of CCD
Power, with the image rectification for the later period.
2. a kind of PST Stray Light Test data processing method according to claim 1, it is characterised in that: the step
(1) according to the point source transmitance PST of remote sensing camera and ccd detector electric property, including photon efficiency, determine that environment is miscellaneous
Astigmatism ENERGY EEnvMethod are as follows: calculate separately out the receptible spuious light energy lower limit E of remote sensing camera institutePSTWith the minimum electricity of CCD
Noise energy ECCD, then environment stray light ENERGY EEnv, it is desirable that EEnv<(EPST+ECCD)。
3. a kind of PST Stray Light Test data processing method according to claim 1, it is characterised in that: the step
(2) in, point source transmitance is PST, and the pixel scale of CCD is that rows row and cols are arranged, and under i-th of energy state, light source is defeated
Entering spuious light energy is E0i(i=1,2 ..., n+1), the stray energy that l row, the kth column pixel of ccd detector receive are
E'(i)(l,k)(l=1,2 ... rows；K=1,2 ... cols)；
So, the input energy difference DELTA E twice adjacent with jth of jth+10j=E0(j+1)-E0j(j=1,2 ..., n)；And twice
The stray energy difference that l row, kth column pixel receive is Δ E'(j)(l,k)=E'(j+1)(l,k)-E'(j)(l,k), then twice should
The difference of pixel is Diff (Ej)(l,k)=Δ E0iPST-ΔE'(j)(l.k), the difference of this pixel is averagedWherein δiFor the weight of difference, the difference that can similarly acquire other pixels is flat
Mean value, and the difference average value of each pixel constitutes rows × cols rank matrix, and corresponds to rows × cols rank picture of CCD
Member, as test noise；Ccd detector each received gross energy of pixel under the 1st energy state subtracts the difference of the pixel
Dividing average value is to realize the removing for having surveyed test noise.
4. a kind of PST Stray Light Test data processing method according to claim 3, it is characterised in that: the rows of CCD ×
Pathosis of the solution complexity of cols rank matrix to measure data.
5. a kind of PST Stray Light Test data processing method according to claim 1, it is characterised in that: the step
(4) according to the matrix for indicating to have removed the ccd image of noise is parsed with the presence or absence of inverse matrix, least square method or wide may be selected
The inverse least square method of justice, genetic algorithm, dichotomy are calculated accordingly.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611073975.2A CN106651793B (en) | 2016-11-29 | 2016-11-29 | A kind of PST Stray Light Test data processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611073975.2A CN106651793B (en) | 2016-11-29 | 2016-11-29 | A kind of PST Stray Light Test data processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106651793A CN106651793A (en) | 2017-05-10 |
| CN106651793B true CN106651793B (en) | 2019-05-24 |
Family
ID=58814967
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611073975.2A Active CN106651793B (en) | 2016-11-29 | 2016-11-29 | A kind of PST Stray Light Test data processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106651793B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108036928B (en) * | 2017-12-15 | 2023-09-01 | 中国科学院西安光学精密机械研究所 | Method and system for calibrating entrance pupil voltage value in PST test and PST test system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1369743A2 (en) * | 2002-06-07 | 2003-12-10 | Brion Technologies, Inc. | Characterisation of lithography apparatus |
| CN102111532A (en) * | 2010-05-27 | 2011-06-29 | 周渝斌 | Camera lens occlusion detecting system and method |
| CN103149016A (en) * | 2013-02-27 | 2013-06-12 | 中国科学院西安光学精密机械研究所 | Stray light testing method and system for optical system to be inspected |
| CN104966278A (en) * | 2015-07-17 | 2015-10-07 | 电子科技大学 | Hyperspectral image noise removing method and apparatus |
| CN106154761A (en) * | 2015-04-15 | 2016-11-23 | 上海微电子装备有限公司 | A kind of veiling glare measurement apparatus and measuring method |
-
2016
- 2016-11-29 CN CN201611073975.2A patent/CN106651793B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1369743A2 (en) * | 2002-06-07 | 2003-12-10 | Brion Technologies, Inc. | Characterisation of lithography apparatus |
| CN102111532A (en) * | 2010-05-27 | 2011-06-29 | 周渝斌 | Camera lens occlusion detecting system and method |
| CN103149016A (en) * | 2013-02-27 | 2013-06-12 | 中国科学院西安光学精密机械研究所 | Stray light testing method and system for optical system to be inspected |
| CN106154761A (en) * | 2015-04-15 | 2016-11-23 | 上海微电子装备有限公司 | A kind of veiling glare measurement apparatus and measuring method |
| CN104966278A (en) * | 2015-07-17 | 2015-10-07 | 电子科技大学 | Hyperspectral image noise removing method and apparatus |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106651793A (en) | 2017-05-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Galicher et al. | Astrometric and photometric accuracies in high contrast imaging: The SPHERE speckle calibration tool (SpeCal) | |
| Rigault et al. | Fully automated integral field spectrograph pipeline for the SEDMachine: pysedm | |
| US10267729B2 (en) | Systems and methods for detecting gas leaks | |
| Lin et al. | Radiometric normalization and cloud detection of optical satellite images using invariant pixels | |
| Stritzinger et al. | The Carnegie Supernova Project I-Photometry data release of low-redshift stripped-envelope supernovae | |
| CN104729718B (en) | A kind of processing system and method for infrared imaging system NETD | |
| CN103604371A (en) | Mobile terminal and object measurement method thereof | |
| Kim et al. | New modules for the SEDMachine to remove contaminations from cosmic rays and non-target light: BYECR and CONTSEP | |
| CN106289532A (en) | The temperature extracting method of a kind of thermal-induced imagery and device | |
| Song et al. | Relative flux calibration for the Guoshoujing Telescope (LAMOST) | |
| Duchesne et al. | The remnant radio galaxy associated with NGC 1534 | |
| CN106651793B (en) | A kind of PST Stray Light Test data processing method | |
| CN113256733B (en) | Camera spectral sensitivity reconstruction method based on confidence voting convolutional neural network | |
| Covey et al. | The ChaMP Extended Stellar Survey (ChESS): Photometric and Spectroscopic Properties of Serendipitously Detected Stellar X-Ray Sources | |
| CN113639881A (en) | Color temperature testing method and device, computer readable medium and electronic equipment | |
| CN109141636B (en) | Equivalent noise radiance measurement method of Fourier infrared spectrometer | |
| CN115830318A (en) | Method and device for segmenting overlapped tobacco shred images based on improved Mask-RCNN (Richardson network) | |
| CN108154478B (en) | A kind of remote sensing image processing method | |
| CN112017180B (en) | Building settlement measurement method and system based on big data and storage medium | |
| CN114755226A (en) | Detection method, device and medium for lithium battery cathode, and detection system | |
| Balaguer-Nunez et al. | uvby–H CCD photometry and membership segregation of the open cluster NGC 2548; gaps in the Main Sequence of open clusters | |
| Lin et al. | A feature domain space transfer method for improving identification of maize haploid seed based on near-infrared spectroscopy | |
| Miravet et al. | Correlation of INGENIO/SEOSAT radiometric model with the results of the radiometric campaigns | |
| CN113095368B (en) | Spectral color representative sample selection method and system | |
| CN112131746B (en) | Chlorophyll a concentration inversion method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |