CN113643388A - Black frame calibration and correction method and system for hyperspectral image - Google Patents

Black frame calibration and correction method and system for hyperspectral image Download PDF

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Publication number
CN113643388A
CN113643388A CN202111196941.3A CN202111196941A CN113643388A CN 113643388 A CN113643388 A CN 113643388A CN 202111196941 A CN202111196941 A CN 202111196941A CN 113643388 A CN113643388 A CN 113643388A
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black frame
image
dimension
hyperspectral
wavelength
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CN113643388B (en
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黄锦标
郁幸超
任哲
郭斌
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Shenzhen Haippi Nanooptical Technology Co ltd
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Shenzhen Haippi Nanooptical Technology Co ltd
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Abstract

The method comprises the steps of carrying out black frame collection on a shooting wave band at the same test temperature and in different exposure times, carrying out black frame collection on different test temperature sequences at the initial exposure time to obtain a corresponding black frame data set, obtaining a wavelength dimension average value and an image dimension average value from the black frame data set according to the image dimension and the wavelength dimension, respectively fitting the wavelength dimension average value and the image dimension average value based on the exposure time and the initial test temperature, and keeping the wavelength dimension average value and the image dimension average value during the initial exposure time and the initial test temperature. And acquiring a complete hyperspectral black frame image sequence matrix by using the parameters, and deducting the product of the hyperspectral black frame image sequence matrix and the digital gain from the hyperspectral image to obtain a corrected hyperspectral image. The method and the system not only can save the time for repeatedly shooting the black frame every time, but also can greatly reduce the space occupied by storage.

Description

Black frame calibration and correction method and system for hyperspectral image
Technical Field
The invention relates to the technical field of hyperspectral imaging and spectral analysis, in particular to a black frame calibration and correction method and system for hyperspectral images.
Background
The hyperspectral imaging technology can simultaneously obtain image information and spectral information, and can perform spectral analysis depending on spectra while distinguishing objects by combining a machine vision technology, so that the hyperspectral imaging technology is a new technology with great potential. The spectral analysis capability of the hyperspectral imaging technology comes from the fact that hyperspectrum can collect spectral information emitted by substances under different wavelengths, and the spectral information directly reflects information such as physical and chemical components of an object; the hyperspectral imaging technology can realize the full automation of target detection, component judgment and result output by combining the information of image identification, region selection and the like.
As with conventional RGB cameras, image data captured by a hyperspectral camera also requires black-frame correction (or flat-field correction) which means that the image captured by the imaging system, after passing through the correction (optics and sensors), each pixel gives the same output value at the same amount of incoming light.
This correction can be achieved by subtracting the black frame (i.e., the image data in the dark) from each frame of the electrical signal read by the sensor without any external light incident on the lens (e.g., with the lens cover on). The black frame deduction can eliminate the nonuniformity of the initial background of the image, keep the consistent corresponding relation between the light input quantity and the digital signal and remove the dark noise to a certain degree. The black frame signal (mainly including dark current and FPN) is positively correlated with temperature and exposure time, and is also correlated with gain or ISO, aperture value, and the like. Dark current and FPN vary from sensor to sensor, and the same sensor has the same FPN at different exposure times, according to which principle: and for the same sensor of the same hyperspectral camera, the corresponding FPNs under different wave bands and exposure time are the same. Therefore, the hyperspectral black frame signal data can be compressed according to the principle, or black frame signal characteristics can be extracted, and the black frame data under different wave bands do not need to be repeatedly acquired.
The black frame deduction can eliminate the nonuniformity of the initial background of the image, keep the consistent corresponding relation between the light incoming quantity and the digital signal and remove dark noise, especially FPN to a certain extent. And is therefore an important pre-treatment step. However, the hyperspectral image has large shooting data amount and takes relatively long time to shoot, if the black frame of the sensor is not processed by a hardware means and needs to be shot in real time for correction, the independent black frame needs to be deducted for each wave band, so that the total time is doubled, and the data storage amount is doubled. Meanwhile, since the exposure time may vary, the black frame cannot be stored in advance, because the amount of data that may exist is extremely large in consideration of different exposure times. Even if only black frame data for the same exposure time is considered and stored, the storage space is consumed.
Disclosure of Invention
In order to solve the technical problem that the data volume of black frame data processing of a hyperspectral image in the prior art is huge and a large amount of time is consumed, the invention provides a black frame calibration and correction method and system for a hyperspectral image, and aims to solve the problems in the prior art.
According to one aspect of the invention, a black frame calibration method for a hyperspectral image is provided, which comprises the following steps:
s1: under the same test temperature and different exposure time, black frame acquisition is carried out on the shooting wave band to obtain a black frame data set
Figure 728665DEST_PATH_IMAGE001
Wherein, in the step (A),
Figure 911384DEST_PATH_IMAGE002
which represents the coordinates of the image and which,
Figure 19018DEST_PATH_IMAGE003
which represents the wavelength of the light emitted by the light source,
Figure 679806DEST_PATH_IMAGE004
represents an exposure time;
s2: obtaining a first wavelength dimension average from a set of black frame data based on an image dimension and a wavelength dimension
Figure 523872DEST_PATH_IMAGE005
And a first image dimension average
Figure 674230DEST_PATH_IMAGE006
Based on exposure time, respectively
Figure 206843DEST_PATH_IMAGE007
Fitting the first wavelength dimension average values respectively
Figure 936902DEST_PATH_IMAGE008
And a first image dimension average
Figure 933676DEST_PATH_IMAGE009
And the initial exposure time is kept
Figure 192619DEST_PATH_IMAGE010
First wavelength dimension average of time
Figure 979572DEST_PATH_IMAGE011
And a first image dimension average
Figure 247742DEST_PATH_IMAGE012
The calibration data of (2);
s3: at the initial exposure time
Figure 302286DEST_PATH_IMAGE013
Next, for different test temperature sequencesShooting and collecting black frames to obtain black frame matrix
Figure 60027DEST_PATH_IMAGE014
Wherein, in the step (A),
Figure 832810DEST_PATH_IMAGE015
it is meant that the temperature of the different tests,
Figure 639092DEST_PATH_IMAGE016
；
s4: obtaining a second wavelength dimension average from the black frame data set based on the image dimension and the wavelength dimension
Figure 813722DEST_PATH_IMAGE017
And second image dimension average
Figure 172722DEST_PATH_IMAGE018
Based on the initial test temperature
Figure 167223DEST_PATH_IMAGE019
Fitting the second wavelength dimension average values respectively
Figure 777196DEST_PATH_IMAGE020
And second image dimension average
Figure 806332DEST_PATH_IMAGE021
And testing the initial test temperature
Figure 109137DEST_PATH_IMAGE022
Second wavelength dimension average of
Figure 387672DEST_PATH_IMAGE023
And second image dimension average
Figure 801336DEST_PATH_IMAGE024
And storing the data into calibration data.
In some specific embodiments, the first wavelength dimension average
Figure 186443DEST_PATH_IMAGE025
Mean value of first image dimension
Figure 660150DEST_PATH_IMAGE026
Average value of second wavelength dimension
Figure 363663DEST_PATH_IMAGE027
Second image dimension average
Figure 112177DEST_PATH_IMAGE028
Wherein, in the step (A),
Figure 115905DEST_PATH_IMAGE029
indicating the number of bands. The random fluctuation of the noise of the individual pixels can be eliminated by the average value.
In some specific embodiments, the first wavelength dimension average is fitted in step S2
Figure 494933DEST_PATH_IMAGE030
And a first image dimension average
Figure 951323DEST_PATH_IMAGE031
Respectively is a matrix
Figure 2062DEST_PATH_IMAGE032
Sum vector
Figure 594717DEST_PATH_IMAGE033
Polynomial function fitting is adopted.
In some specific embodiments, the second wavelength dimension average is fitted in step S4
Figure 410226DEST_PATH_IMAGE034
And second image dimension average
Figure 88332DEST_PATH_IMAGE035
Respectively is a matrix
Figure 444227DEST_PATH_IMAGE036
Sum vector
Figure 156969DEST_PATH_IMAGE037
. The function fits to the subtle changes in the fixed pattern noise of different pixels of the black frame image with exposure time.
In some specific embodiments, the method further comprises acquiring corresponding calibration data at different aperture or gain values. The black frame signals of different apertures and internal gains will be different and therefore need to be calibrated separately.
According to a second aspect of the present invention, a black frame correction method for hyperspectral images is provided, where the black frame calibration method includes:
acquiring shooting parameters of the current hyperspectral region, wherein the shooting parameters comprise digital gains
Figure 113685DEST_PATH_IMAGE038
Wave band
Figure 341404DEST_PATH_IMAGE039
Exposure time
Figure 438673DEST_PATH_IMAGE040
And temperature
Figure 5921DEST_PATH_IMAGE041
And loading calibration data corresponding to the current aperture and the internal gain, wherein the calibration data comprises a first wavelength dimension average value
Figure 694391DEST_PATH_IMAGE042
First image dimension average
Figure 612669DEST_PATH_IMAGE043
Matrix of fitting coefficients
Figure 283602DEST_PATH_IMAGE044
Sum vector
Figure 236515DEST_PATH_IMAGE045
；
Performing band interpolation processing on the image dimension average value;
respectively pre-generating components of a black frame in a wavelength dimension and components of the black frame in an image dimension, and combining the two components to obtain a complete hyperspectral black frame image sequence matrix;
removing a hyperspectral black frame image sequence matrix and digital gain by using a currently shot hyperspectral image
Figure 95887DEST_PATH_IMAGE046
Obtaining a corrected hyperspectral image.
In some specific embodiments, for a hyperspectral device without a temperature sensor, the first image dimension is averaged
Figure 65242DEST_PATH_IMAGE047
Obtained by performing actual band interpolation processing
Figure 301051DEST_PATH_IMAGE048
。
In some specific embodiments, the component of the pre-generated black frame in the wavelength dimension is
Figure 374049DEST_PATH_IMAGE050
The component of the pre-generated black frame in the image dimension is
Figure 404322DEST_PATH_IMAGE052
Therein, functional
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To represent
Figure 38490DEST_PATH_IMAGE054
And exposure time
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Relation, functional of
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To represent
Figure 78493DEST_PATH_IMAGE057
And exposure time
Figure 891990DEST_PATH_IMAGE058
The relationship (2) of (c).
In some specific embodiments, for a hyperspectral device with a temperature sensor, the second image dimension is averaged
Figure 939581DEST_PATH_IMAGE059
Obtained by performing actual band interpolation processing
Figure 842815DEST_PATH_IMAGE060
。
In some specific embodiments, the component of the pre-generated black frame in the wavelength dimension is
Figure 491969DEST_PATH_IMAGE061
The component of the pre-generated black frame in the image dimension is
Figure 607692DEST_PATH_IMAGE062
Therein, functional
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To represent
Figure 787187DEST_PATH_IMAGE064
And exposure time
Figure 643410DEST_PATH_IMAGE065
Relation, functional of
Figure 359562DEST_PATH_IMAGE066
To represent
Figure 116165DEST_PATH_IMAGE067
Relation to temperature T, functional
Figure 830043DEST_PATH_IMAGE068
To represent
Figure 436212DEST_PATH_IMAGE069
And exposure time
Figure 893738DEST_PATH_IMAGE070
Relation, functional of
Figure 504848DEST_PATH_IMAGE071
To represent
Figure 389627DEST_PATH_IMAGE072
And temperature T.
In some specific embodiments, the combining the components of the pre-generated black frame in the wavelength dimension and the components in the image dimension to obtain the complete hyperspectral black frame image sequence matrix specifically includes: copying the components of the pre-generated black frame in the image dimension to all current bands
Figure 486022DEST_PATH_IMAGE073
And multiplying the image data of each wave band with the component of the pre-generated black frame in the wavelength dimension at the value of the wave band to obtain a complete hyperspectral black frame image sequence matrix.
According to a third aspect of the present invention, a black frame calibration system for hyperspectral images is provided, the system comprising:
black frame data acquisition unit: the device is configured to carry out black frame acquisition on a shooting waveband at the same test temperature and in different exposure times to acquire a black frame data set
Figure 747239DEST_PATH_IMAGE074
Wherein, in the step (A),
Figure 416117DEST_PATH_IMAGE075
which represents the coordinates of the image and which,
Figure 737377DEST_PATH_IMAGE076
display waveThe length of the utility model is long,
Figure 323997DEST_PATH_IMAGE077
represents an exposure time;
a calibration data acquisition unit: obtaining a first wavelength dimension average from a set of black frame data based on an image dimension and a wavelength dimension
Figure 123326DEST_PATH_IMAGE078
And a first image dimension average
Figure 912291DEST_PATH_IMAGE079
Based on exposure time, respectively
Figure 935610DEST_PATH_IMAGE080
Fitting the first wavelength dimension average values respectively
Figure 475438DEST_PATH_IMAGE081
And a first image dimension average
Figure 875196DEST_PATH_IMAGE082
And the initial exposure time is kept
Figure 315404DEST_PATH_IMAGE083
First wavelength dimension average of time
Figure 712887DEST_PATH_IMAGE084
And a first image dimension average
Figure 2661DEST_PATH_IMAGE085
The calibration data of (1).
In some specific embodiments, for a hyperspectral device that does not include a temperature sensor:
the black frame data acquisition unit is further configured to detect an initial exposure time
Figure 143792DEST_PATH_IMAGE086
Then, shooting and collecting black frames for different test temperature sequences to obtain a black frame matrix
Figure 704087DEST_PATH_IMAGE087
Wherein, in the step (A),
Figure 538051DEST_PATH_IMAGE088
it is meant that the temperature of the different tests,
Figure 318050DEST_PATH_IMAGE089
；
the calibration data obtaining unit is further configured to obtain a second wavelength dimension average value from the black frame data set based on the image dimension and the wavelength dimension
Figure 466135DEST_PATH_IMAGE090
And second image dimension average
Figure 146515DEST_PATH_IMAGE091
Based on the initial test temperature
Figure 151380DEST_PATH_IMAGE092
Fitting the second wavelength dimension average values respectively
Figure 878728DEST_PATH_IMAGE093
And second image dimension average
Figure 627241DEST_PATH_IMAGE094
And testing the initial test temperature
Figure 162127DEST_PATH_IMAGE095
Second wavelength dimension average of
Figure 275577DEST_PATH_IMAGE096
And second image dimension average
Figure 528704DEST_PATH_IMAGE097
And storing the data into calibration data.
In some specific embodiments, the calibration data obtaining unit is further configured to obtain corresponding calibration data at different aperture or gain values. The black frame signals of different apertures and internal gains will be different and therefore need to be calibrated separately.
According to a fourth aspect of the present invention, a black frame rectification system for hyperspectral images is provided, where the black frame calibration system as described above is utilized, and the black frame rectification system further includes:
a data acquisition unit: configuring shooting parameters for acquiring the current hyperspectral image, wherein the shooting parameters comprise digital gains
Figure 316793DEST_PATH_IMAGE098
Wave band
Figure 706186DEST_PATH_IMAGE099
Exposure time
Figure 52854DEST_PATH_IMAGE100
And temperature
Figure 527698DEST_PATH_IMAGE101
And loading calibration data corresponding to the current aperture and the internal gain, wherein the calibration data comprises a first wavelength dimension average value
Figure 585390DEST_PATH_IMAGE102
First image dimension average
Figure 626028DEST_PATH_IMAGE103
Matrix of fitting coefficients
Figure 143597DEST_PATH_IMAGE104
Sum vector
Figure 607201DEST_PATH_IMAGE105
；
An interpolation processing unit: the method comprises the steps of configuring and processing wave band interpolation on an image dimension average value;
a hyperspectral black frame image sequence matrix generation unit: the method comprises the steps of configuring components in the wavelength dimension and the image dimension of a black frame for pre-generation respectively, and combining the two components to obtain a complete hyperspectral black frame image sequence matrix;
a correction unit: configuring a sequence matrix and digital gain for removing a hyperspectral black frame image by using a currently shot hyperspectral image
Figure 438891DEST_PATH_IMAGE106
Obtaining a corrected hyperspectral image.
The method and the system for calibrating and correcting the hyperspectral image not only can save the time for repeatedly shooting the black frame every time, but also can greatly reduce the space occupied by storage. The corresponding black frame signals under different temperatures, different wave bands and different exposure time are generated by using a very small amount of pre-stored data. It has the following beneficial effects: the method has the characteristic of saving the time for shooting the hyperspectral data, and saves a large amount of hyperspectral imaging shooting time by avoiding shooting the black frame corresponding to each waveband in real time; the method has the technical characteristic of small occupied storage space. The data of corresponding different black frame signals with different exposure time, different wave bands and even different working temperatures are obtained only by calculation simulation of pre-stored extremely small amount of data (single-frame black frame images and other parameters); the method has the characteristic of high adaptability of flexible application, and the black frame generated by calculation under the method can cover corresponding black frame signals at any exposure time, any waveband and even at working temperature, and is still effective for multi-sensor hyperspectral equipment, so that the method has extremely high adaptability and extensibility and can be used under various different shooting requirements.
Drawings
The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and together with the description serve to explain the principles of the invention. Other embodiments and many of the intended advantages of embodiments will be readily appreciated as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding similar parts.
FIG. 1 is a flow diagram of a black frame calibration method for hyperspectral images according to one embodiment of the invention;
FIG. 2 is a flowchart of a black frame calibration method for hyperspectral equipment with a temperature sensor according to a specific embodiment of the invention;
FIG. 3 is a flow chart of a black frame calibration method for hyperspectral equipment without a temperature sensor in accordance with a specific embodiment of the invention;
FIG. 4 is a flow diagram of a black frame rectification method for hyperspectral images according to one embodiment of the invention;
FIG. 5 is a flow chart of a black frame correction method of a hyperspectral device with a temperature sensor according to a specific embodiment of the invention;
FIG. 6 is a flow diagram of a black frame correction method for a hyperspectral device without a temperature sensor in accordance with a specific embodiment of the invention;
FIG. 7 is a graph of the effect of a fit of black frame means in the wavelength dimension according to one embodiment of the invention;
FIG. 8 is a graph of the corrective effect of black frame means over a certain band of image dimensions, according to one embodiment of the present invention;
FIG. 9 is a block diagram of a black frame calibration system for hyperspectral images in accordance with an embodiment of the invention;
FIG. 10 is a block diagram of a black frame rectification system for hyperspectral images in accordance with an embodiment of the invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a flowchart of a black frame calibration method for hyperspectral images according to an embodiment of the invention, as shown in fig. 1, the method comprises the following steps:
s101: under the same test temperature and different exposure time, black frame acquisition is carried out on the shooting wave band to obtain a black frame data set
Figure 537297DEST_PATH_IMAGE107
Wherein
Figure 225767DEST_PATH_IMAGE108
Which represents the coordinates of the image and which,
Figure 675203DEST_PATH_IMAGE109
which represents the wavelength of the light emitted by the light source,
Figure 346137DEST_PATH_IMAGE110
the exposure time is indicated.
S102: obtaining a first wavelength dimension average from a set of black frame data based on an image dimension and a wavelength dimension
Figure 564629DEST_PATH_IMAGE111
And a first image dimension average
Figure 220738DEST_PATH_IMAGE112
Based on exposure time, respectively
Figure 95153DEST_PATH_IMAGE113
Fitting the first wavelength dimension average values respectively
Figure 832427DEST_PATH_IMAGE114
And a first image dimension average
Figure 905425DEST_PATH_IMAGE115
And the initial exposure time is kept
Figure 935698DEST_PATH_IMAGE116
First wavelength dimension average of time
Figure 94147DEST_PATH_IMAGE117
And a first image dimension average
Figure 101024DEST_PATH_IMAGE118
The calibration data of (1).
In a specific embodiment, the first wavelength dimension average
Figure 294108DEST_PATH_IMAGE119
Mean value of first image dimension
Figure 495282DEST_PATH_IMAGE120
Wherein, in the step (A),
Figure 141027DEST_PATH_IMAGE121
indicating the number of bands. The average value of the wavelength dimension reflects the fixed pattern noise FPN, random fluctuation of individual pixel noise can be eliminated by using the average value, the average value of the image dimension can obtain the average value of each frame of image, and the relation between the size of the average value and the wavelength is obtained.
In a specific embodiment, the first wavelength dimension average is fitted
Figure 954525DEST_PATH_IMAGE122
And a first image dimension average
Figure 798853DEST_PATH_IMAGE123
Respectively is a matrix
Figure 170928DEST_PATH_IMAGE124
Sum vector
Figure 507232DEST_PATH_IMAGE125
Polynomial function fitting is adopted. Wherein, functional is utilized
Figure 935806DEST_PATH_IMAGE126
To represent
Figure 837903DEST_PATH_IMAGE127
And at the time of exposureWorkshop
Figure 380880DEST_PATH_IMAGE128
Relation, functional of
Figure 237103DEST_PATH_IMAGE129
To represent
Figure 156517DEST_PATH_IMAGE130
And exposure time
Figure 913120DEST_PATH_IMAGE131
Fitting the first wavelength dimension average
Figure 830261DEST_PATH_IMAGE132
And a first image dimension average
Figure 937894DEST_PATH_IMAGE133
Respectively is a matrix
Figure 690693DEST_PATH_IMAGE134
Sum vector
Figure 36224DEST_PATH_IMAGE135
And fitting all the fitting modes by adopting polynomial functions.
In an embodiment where the image sensor carries a temperature sensor, the calibration method further includes the following steps:
s103: at the initial exposure time
Figure 186582DEST_PATH_IMAGE136
Then, shooting and collecting black frames for different test temperature sequences to obtain a black frame matrix
Figure 17397DEST_PATH_IMAGE137
Wherein, in the step (A),
Figure 13035DEST_PATH_IMAGE138
indicating different test temperatures
Figure 744231DEST_PATH_IMAGE139
。
S104: obtaining a second wavelength dimension average from the black frame data set based on the image dimension and the wavelength dimension
Figure 65491DEST_PATH_IMAGE140
And second image dimension average
Figure 120952DEST_PATH_IMAGE141
Based on the initial test temperature
Figure 451440DEST_PATH_IMAGE142
Fitting the second wavelength dimension average values respectively
Figure 37142DEST_PATH_IMAGE143
And second image dimension average
Figure 263724DEST_PATH_IMAGE144
And testing the initial test temperature
Figure 69131DEST_PATH_IMAGE145
Second wavelength dimension average of
Figure 672151DEST_PATH_IMAGE146
And second image dimension average
Figure 846780DEST_PATH_IMAGE147
And storing the data into calibration data.
In specific embodiments, the second and wavelength dimension averages
Figure 509843DEST_PATH_IMAGE148
And second image dimension average
Figure 534037DEST_PATH_IMAGE149
Step S102 is synchronized in the calculation method of (1). Wherein, functional is utilized
Figure 206327DEST_PATH_IMAGE150
To represent
Figure 766621DEST_PATH_IMAGE151
Relation to temperature T, functional
Figure 335006DEST_PATH_IMAGE152
To represent
Figure 115005DEST_PATH_IMAGE153
And temperature T. Fitting the second wavelength dimension average
Figure 263090DEST_PATH_IMAGE154
And second image dimension average
Figure 943470DEST_PATH_IMAGE155
Respectively is a matrix
Figure 682756DEST_PATH_IMAGE156
Sum vector
Figure 386269DEST_PATH_IMAGE157
。
In some other embodiments, the steps S101-S104 are repeated at different aperture or gain values and corresponding calibration data is obtained to cope with the black frame processing of the hyperspectral image under different parameters.
With continuing reference to fig. 2, fig. 2 is a flowchart illustrating a black frame calibration method for hyperspectral equipment with a temperature sensor according to a specific embodiment of the invention, where in the case that an image sensor carries a temperature detection sensor, more accurate black frame data can be automatically obtained, and as shown in fig. 2, the method includes the following steps:
step 201: and (3) under the default aperture and internal gain (non-digital gain), placing the equipment in a temperature control box, adjusting to the common test temperature, and starting the equipment to preheat until the temperature is stable. In that
Figure 627458DEST_PATH_IMAGE158
Exposure ofObtaining the average temperature of the sensor under time shooting
Figure 162345DEST_PATH_IMAGE159
。
Figure 806953DEST_PATH_IMAGE160
And
Figure 794500DEST_PATH_IMAGE161
both temperature and exposure time are calibrated to initial default values, which are preferably commonly used intermediate values to minimize the offset error of the fitting correction.
Step 202: black frame acquisition is carried out on all the photographable wave bands (such as 1nm interval) under different exposure time (such as a sequence from 1ms to 1 s), and a data set is obtained
Figure 284388DEST_PATH_IMAGE162
. The sampling interval of the exposure time can be adjusted according to the degree of change or the frequency of use, and the wavelength can be selected according to a specific application scene, and the wavelength which is continuous as much as possible can be taken in the working wavelength range, for example, every 1nm is taken as an interval, so that the fine correction is realized. The data set
Figure 142622DEST_PATH_IMAGE163
And sampling the traversed data set of all the photographable wave bands and the exposure time at the same ambient temperature for subsequent feature extraction and calibration. Wherein the content of the first and second substances,
Figure 459596DEST_PATH_IMAGE164
y represents the coordinates of the image and,
Figure 137702DEST_PATH_IMAGE165
which represents the wavelength of the light emitted by the light source,
Figure 493597DEST_PATH_IMAGE166
the exposure time is indicated.
Step 203: respectively averaging the wavelength dimension and the image dimension of the black frame set to obtain
Figure 206338DEST_PATH_IMAGE167
And
Figure 927170DEST_PATH_IMAGE168
. In particular, the method comprises the following steps of,
Figure 358151DEST_PATH_IMAGE169
，
Figure 455420DEST_PATH_IMAGE170
(ii) a Average value of wavelength dimension
Figure 317940DEST_PATH_IMAGE171
Reflecting the FPN (fixed pattern noise),
Figure 475252DEST_PATH_IMAGE172
representing the number of bands. Wherein the random rise and fall of the individual pixel noise is eliminated by the averaging method; average of image dimensions
Figure 127950DEST_PATH_IMAGE173
The average value of each frame image is obtained, and the relation between the average value and the wavelength is obtained.
Step 204: to pair
Figure 28910DEST_PATH_IMAGE174
Fitting separately
Figure 716244DEST_PATH_IMAGE175
And
Figure 778878DEST_PATH_IMAGE176
the fitting coefficients are expressed as vectors
Figure 918872DEST_PATH_IMAGE177
Sum matrix
Figure 859408DEST_PATH_IMAGE178
Using a function
Figure 197986DEST_PATH_IMAGE179
And
Figure 697100DEST_PATH_IMAGE180
respectively represent
Figure 324391DEST_PATH_IMAGE181
、
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And exposure time
Figure 229079DEST_PATH_IMAGE183
To preserve the relationship of
Figure 633515DEST_PATH_IMAGE184
Time of flight
Figure 986917DEST_PATH_IMAGE185
And
Figure 33370DEST_PATH_IMAGE186
value of (A)
Figure 346540DEST_PATH_IMAGE187
、
Figure 187457DEST_PATH_IMAGE188
. To pair
Figure 523761DEST_PATH_IMAGE189
The fitting uses a second order polynomial function fitting. To pair
Figure 108326DEST_PATH_IMAGE190
The fitting is the slight change of FPN of different pixels of the black frame image along with the exposure time, generally, the longer the exposure time is, the more the hot pixels are, the larger the fluctuation of dark noise is, the change relation can be fitted by a polynomial function, and if the third-order polynomial fitting is used, the dimension size of the obtained coefficient matrix bxyt is X, Y and 3.
Step 205: adjusting the temperature of the temperature control box to other different test temperature sequences to be setSelecting the exposure time after the temperature is stable
Figure 479264DEST_PATH_IMAGE191
Taking black frames to obtain the average temperature sequence of the sensor
Figure 726968DEST_PATH_IMAGE192
And corresponding black frame matrix
Figure 816147DEST_PATH_IMAGE194
。
Step 206: respectively collecting black frames
Figure 735561DEST_PATH_IMAGE195
Averaging the image dimension and the wavelength dimension to obtain
Figure 226586DEST_PATH_IMAGE196
And
Figure 143726DEST_PATH_IMAGE197
. The method used in this step is the same as that in step 203.
Step 207: to pair
Figure 251359DEST_PATH_IMAGE198
Fitting separately
Figure 410683DEST_PATH_IMAGE199
And
Figure 552951DEST_PATH_IMAGE200
the fitting coefficients are respectively recorded as
Figure 172151DEST_PATH_IMAGE201
And
Figure 298239DEST_PATH_IMAGE202
using a function
Figure 60921DEST_PATH_IMAGE203
And
Figure 995379DEST_PATH_IMAGE204
respectively represent
Figure 51060DEST_PATH_IMAGE205
、
Figure 867706DEST_PATH_IMAGE206
And sensor temperature
Figure 714307DEST_PATH_IMAGE207
To preserve the relationship of
Figure 300009DEST_PATH_IMAGE208
Value of time
Figure 729853DEST_PATH_IMAGE209
And
Figure 299375DEST_PATH_IMAGE210
. And obtaining the exposure time
Figure 105657DEST_PATH_IMAGE211
And the average value
Figure 47330DEST_PATH_IMAGE212
And
Figure 241551DEST_PATH_IMAGE213
the variation relationship is similarly performed, and the average value of the new data set in the image and wavelength dimensions and the sensor temperature are obtained
Figure 298369DEST_PATH_IMAGE214
The relationship between the changes of (c).
Step 208: if there are other aperture or internal gain values, repeating all the above calibration processes and storing corresponding data under different aperture or gain values. Because the black frame signals under different apertures and internal gains are different, if the sensor of the hyperspectral camera has other aperture values or gain values (non-digital gain), the steps are required to be repeated for calibration respectively.
Fig. 3 is a flowchart illustrating a black frame calibration method for a hyperspectral device without a temperature sensor according to a specific embodiment of the invention, which takes an FPI hyperspectral device without a temperature sensor (which has non-adjustable internal gain and aperture value and is embedded with two sensors for expansion of the shooting band, and the resolution of each sensor is 1024 × 1024) as an example to attempt to automatically generate a corresponding black frame and obtain flat field correction. By using the method, automatic black frame generation is realized by using a small amount of pre-stored data under different shooting scenes (such as 400-700nm, 100 wave bands and 10ms of exposure time of each frame as an example) and a black frame removing effect of hyperspectral image flat field correction is obtained. As shown in fig. 3, the method comprises the steps of:
step 301: the device is started to be preheated to a stable temperature under default aperture, internal gain (non-digital gain) and use temperature (including operation of a heat dissipation device such as a fan, a water cooling device and the like).
Step 302: under different exposure time (sequence from 1ms to 1 s), black frame acquisition is carried out on all the photographable wave bands of a first sensor in the equipment to obtain a data set
Figure 352DEST_PATH_IMAGE215
. Specifically, in this embodiment, 1, 10, 20, 30, 50, 100, 200, 300, 500, and 1000ms exposure sequences are used, black frame acquisition (400 + 700nm, 1nm interval) is performed on all the photographable bands of the first sensor in the device, and since the hyperspectral device is spliced by two sensors, it needs to be calibrated separately, and one of the two sensors is calibrated in its operating band.
Step 303: respectively averaging the image dimensionality and the wavelength dimensionality of the black frame set to obtain
Figure 498330DEST_PATH_IMAGE216
And
Figure 332293DEST_PATH_IMAGE217
. In particular, the method comprises the following steps of,
Figure 345249DEST_PATH_IMAGE218
，
Figure 791536DEST_PATH_IMAGE219
wherein
Figure 471916DEST_PATH_IMAGE220
Is a matrix of size 1024 x 10,
Figure 476781DEST_PATH_IMAGE221
is a matrix of size 300 x 10.
Step 304: to pair
Figure 977032DEST_PATH_IMAGE222
Fitting
Figure 229940DEST_PATH_IMAGE223
By quadratic functions
Figure 499247DEST_PATH_IMAGE224
Fitting
Figure 675014DEST_PATH_IMAGE225
And integration time
Figure 865824DEST_PATH_IMAGE226
The fitting coefficient is recorded as a vector
Figure 152449DEST_PATH_IMAGE227
(300*3). Retention
Figure 43307DEST_PATH_IMAGE228
Time of flight
Figure 389974DEST_PATH_IMAGE229
Value of (A)
Figure 864818DEST_PATH_IMAGE230
。
Step 305: to pair
Figure 158396DEST_PATH_IMAGE231
Fitting
Figure 900831DEST_PATH_IMAGE232
Using cubic functions
Figure 683979DEST_PATH_IMAGE233
Fitting
Figure 380539DEST_PATH_IMAGE234
And integration time
Figure 212229DEST_PATH_IMAGE235
The fitting coefficient is recorded as a matrix
Figure 841794DEST_PATH_IMAGE236
(1024*1024*4). Retention
Figure 234991DEST_PATH_IMAGE237
Time of flight
Figure 684427DEST_PATH_IMAGE238
Value of (A)
Figure 116545DEST_PATH_IMAGE239
. In a specific embodiment, fig. 7 shows the fitting effect of two sensor black frame means in the wavelength dimension.
Step 306: and repeating the calibration process and storing corresponding data for another sensor in the hyperspectral camera, and combining and storing the data of the two sensors as the calibration data of the equipment.
Fig. 4 shows a flowchart of a black frame rectification method for a hyperspectral image according to an embodiment of the invention, and as shown in fig. 4, the rectification method comprises:
s401: acquiring shooting parameters of the current hyperspectral region, wherein the shooting parameters comprise digital gains
Figure 803879DEST_PATH_IMAGE240
Wave band
Figure 397671DEST_PATH_IMAGE241
Exposure time
Figure 827079DEST_PATH_IMAGE242
And temperature
Figure 531730DEST_PATH_IMAGE243
And loading calibration data corresponding to the current aperture and the internal gain, wherein the calibration data comprises the mean value of the first image dimension
Figure 870307DEST_PATH_IMAGE244
First wavelength dimension average value
Figure 900580DEST_PATH_IMAGE245
Fitting coefficient
Figure 560494DEST_PATH_IMAGE246
And
Figure 537677DEST_PATH_IMAGE247
。
s402: and performing band interpolation processing on the image dimension average value.
In a particular embodiment, for a hyperspectral device without a temperature sensor, the first image dimension is averaged
Figure 465182DEST_PATH_IMAGE248
Obtained by performing actual band interpolation processing
Figure 931935DEST_PATH_IMAGE249
(ii) a Averaging the second image dimension for a hyperspectral device with a temperature sensor
Figure 780942DEST_PATH_IMAGE250
Obtained by performing actual band interpolation processing
Figure 857090DEST_PATH_IMAGE251
。
S403: respectively pre-generating components of the black frame in the wavelength dimension and the components in the image dimension, and combining the two components to obtain a complete hyperspectral black frame image sequence matrix.
In a specific embodiment, for a hyperspectral device without a temperature sensor, the component of the pre-generated black frame in the wavelength dimension is
Figure 435838DEST_PATH_IMAGE252
The component of the pre-generated black frame in the image dimension is
Figure 542335DEST_PATH_IMAGE253
Therein, functional
Figure 176841DEST_PATH_IMAGE254
To represent
Figure 292564DEST_PATH_IMAGE255
And exposure time
Figure 194661DEST_PATH_IMAGE256
Relation, functional of
Figure 737638DEST_PATH_IMAGE257
To represent
Figure 862370DEST_PATH_IMAGE258
And exposure time
Figure 516205DEST_PATH_IMAGE259
The relationship (2) of (c).
In a specific embodiment, for a hyperspectral device with a temperature sensor, the component of the pre-generated black frame in the wavelength dimension is
Figure 272808DEST_PATH_IMAGE261
The component of the pre-generated black frame in the image dimension is
Figure 252266DEST_PATH_IMAGE263
Therein, functional
Figure 595784DEST_PATH_IMAGE264
To represent
Figure 850048DEST_PATH_IMAGE265
And exposure time
Figure 726737DEST_PATH_IMAGE266
Relation, functional of
Figure 345938DEST_PATH_IMAGE267
To represent
Figure 173823DEST_PATH_IMAGE268
Relation to temperature T, functional
Figure 700619DEST_PATH_IMAGE269
To represent
Figure 431815DEST_PATH_IMAGE270
And exposure time
Figure 690758DEST_PATH_IMAGE266
Relation, functional of
Figure 8869DEST_PATH_IMAGE271
To represent
Figure 73777DEST_PATH_IMAGE272
And temperature T.
In a specific embodiment, the components of the pre-generated black frame in the image dimension are copied to all current bands
Figure 659479DEST_PATH_IMAGE273
And multiplying the image data of each wave band with the component of the pre-generated black frame in the wavelength dimension at the value of the wave band to obtain a complete hyperspectral black frame image sequence matrix.
S404: removing a hyperspectral black frame image sequence matrix and digital gain by using a currently shot hyperspectral image
Figure 354903DEST_PATH_IMAGE274
Obtaining a corrected hyperspectral image.
With continuing reference to FIG. 5, FIG. 5 is a flowchart illustrating a black frame correction method for a hyperspectral device carrying a temperature sensor according to a specific embodiment of the invention, as shown in FIG. 5, comprising the steps of:
step 501: acquiring shooting parameters of the current hyperspectral image: gain of
Figure 924424DEST_PATH_IMAGE275
Band of wavelengths
Figure 777978DEST_PATH_IMAGE276
Exposure time
Figure 687028DEST_PATH_IMAGE277
Temperature of the sensor
Figure 84511DEST_PATH_IMAGE278
。
Step 502: loading the pre-stored value corresponding to the current aperture and the internal gain
Figure 406908DEST_PATH_IMAGE280
、
Figure 846242DEST_PATH_IMAGE281
Sum coefficient
Figure 140957DEST_PATH_IMAGE282
、
Figure 974921DEST_PATH_IMAGE283
。
Step 503: to pair
Figure 486412DEST_PATH_IMAGE285
Is processed by band interpolation to obtain
Figure 431234DEST_PATH_IMAGE287
. Namely realityThe shooting wave band can be a part of the shooting wave band range of the hyperspectral equipment or a plurality of certain wave bands, and the actual shooting wave band is obtained in an interpolation mode
Figure 111614DEST_PATH_IMAGE288
Corresponding to
Figure 319741DEST_PATH_IMAGE290
；
Step 504: the component of the pre-generated black frame in the wavelength dimension is
Figure 85572DEST_PATH_IMAGE291
. The formula gives the data simulation calculated from previous calibration and fitting that at the actual exposure time,
Figure 69971DEST_PATH_IMAGE292
and operating temperature
Figure 808120DEST_PATH_IMAGE293
Is as follows
Figure 780624DEST_PATH_IMAGE295
And (4) components.
Step 505: the component of the pre-generated black frame in the image dimension is
Figure 803724DEST_PATH_IMAGE296
. The formula gives the data simulation calculated from previous calibration and fitting that at the actual exposure time,
Figure 824770DEST_PATH_IMAGE297
and operating temperature
Figure 948584DEST_PATH_IMAGE298
Is as follows
Figure 295251DEST_PATH_IMAGE299
And (4) components.
Step 506:complete pre-generated hyperspectral black frame
Figure 537139DEST_PATH_IMAGE301
Is composed of
Figure 627455DEST_PATH_IMAGE303
And
Figure 871355DEST_PATH_IMAGE305
and dimension filling the obtained three-dimensional matrix. Grouping black frame components of image dimensions
Figure 123344DEST_PATH_IMAGE307
Copy to all current bands
Figure 288746DEST_PATH_IMAGE308
Multiplying the image data of each wave band by the value (as a gain coefficient) of the black frame component of the wavelength dimension in the wave band to obtain a complete three-dimensional hyperspectral black frame array
Figure 212447DEST_PATH_IMAGE310
。
Step 507: subtracting the currently shot hyperspectral image
Figure 576432DEST_PATH_IMAGE312
*
Figure 202585DEST_PATH_IMAGE313
And obtaining the hyperspectral image after the black frame correction.
In another embodiment, fig. 6 is a flowchart illustrating a black frame correction method for a hyperspectral device without a temperature sensor according to a specific embodiment of the invention, and when a complete black frame characteristic calibration is established and necessary data (one frame of image data, one piece of spectral data and two sets of polynomial fitting parameters) are stored as in the calibration method in fig. 3, the hyperspectral data captured by the device can be subsequently used for automatic black frame generation and correction. As shown in FIG. 6, assume that the current shooting band sequence wact has 200 wavelengths, exposureLight time of
Figure 652021DEST_PATH_IMAGE314
The rectification of each sensor comprises the following steps:
step 601: obtaining the shooting parameters of the current hyperspectral camera, including digital gain
Figure 585604DEST_PATH_IMAGE315
Wave band
Figure 538517DEST_PATH_IMAGE316
And exposure time
Figure 663468DEST_PATH_IMAGE317
。
Step 602: load calibrated prestore
Figure 537883DEST_PATH_IMAGE319
、
Figure 266368DEST_PATH_IMAGE321
And fitting coefficient
Figure 604945DEST_PATH_IMAGE322
、
Figure 635218DEST_PATH_IMAGE323
。
Step 603: to pair
Figure 996929DEST_PATH_IMAGE324
The pre-stored value is obtained by actual wave band interpolation processing
Figure 36430DEST_PATH_IMAGE325
。
Step 604: the component of the pre-generated black frame in the wavelength dimension is
Figure 668662DEST_PATH_IMAGE327
。
Step 605: the component of the pre-generated black frame in the image dimension is
Figure 135415DEST_PATH_IMAGE329
。
Step 606: complete pre-generated hyperspectral black frame
Figure 984422DEST_PATH_IMAGE331
Is composed of
Figure 562034DEST_PATH_IMAGE332
And
Figure 78466DEST_PATH_IMAGE334
and dimension filling the obtained three-dimensional matrix. Combining the two components obtained above
Figure 919383DEST_PATH_IMAGE336
And
Figure 19801DEST_PATH_IMAGE338
and obtaining a complete matrix of the hyperspectral black frame image sequence. The specific method comprises the following steps: grouping black frame components of image dimensions
Figure 338787DEST_PATH_IMAGE339
Copy to all current bands
Figure 709725DEST_PATH_IMAGE340
And combining the image data of each band with the black frame component of the wavelength dimension
Figure 721544DEST_PATH_IMAGE341
Multiplying the values (as gain coefficients) of the wave bands to obtain a complete three-dimensional hyperspectral black frame array
Figure 545143DEST_PATH_IMAGE343
Its dimension size is 1024 x 200.
Step 607: subtracting the currently shot hyperspectral image
Figure 933399DEST_PATH_IMAGE345
*
Figure 955582DEST_PATH_IMAGE346
And obtaining the high spectral data after the black frame correction from the same sensor. The data from another sensor is processed by the same calculation according to the steps, and finally the current shooting wave band sequence is obtained
Figure 639766DEST_PATH_IMAGE347
And exposure time
Figure 216241DEST_PATH_IMAGE348
And (5) correcting the hyperspectral image by using the complete black frame. In a specific embodiment, fig. 8 shows the correction effect of the black frame on a certain wave band of the image dimension, and the hyperspectral image after the black frame is removed is obtained.
With continuing reference to fig. 9, fig. 9 is a schematic diagram of a frame of a black frame calibration system for hyperspectral images according to an embodiment of the invention, and as shown in fig. 9, the system includes a black frame data acquisition unit 701 and a calibration data acquisition unit 702, where the black frame data acquisition unit 701 is configured to perform black frame acquisition on a shooting waveband at the same test temperature and at different exposure times to acquire a black frame data set
Figure 142609DEST_PATH_IMAGE349
Wherein, in the step (A),
Figure 222560DEST_PATH_IMAGE350
which represents the coordinates of the image and which,
Figure 841761DEST_PATH_IMAGE351
which represents the wavelength of the light emitted by the light source,
Figure 374373DEST_PATH_IMAGE352
represents an exposure time; at the initial exposure time
Figure 901169DEST_PATH_IMAGE353
Then, shooting and collecting black frames for different test temperature sequences to obtain a black frame matrix
Figure 74443DEST_PATH_IMAGE354
Wherein, in the step (A),
Figure 864544DEST_PATH_IMAGE355
it is meant that the temperature of the different tests,
Figure 946770DEST_PATH_IMAGE356
. The calibration data obtaining unit 702 is configured to obtain a first wavelength dimension average value from the set of black frame data based on the image dimension and the wavelength dimension
Figure 949361DEST_PATH_IMAGE357
And a first image dimension average
Figure 800642DEST_PATH_IMAGE358
Based on exposure time, respectively
Figure 496065DEST_PATH_IMAGE359
Fitting the first wavelength dimension average values respectively
Figure 35893DEST_PATH_IMAGE360
And a first image dimension average
Figure 638913DEST_PATH_IMAGE361
And the initial exposure time is kept
Figure 344701DEST_PATH_IMAGE362
First wavelength dimension average of time
Figure 742184DEST_PATH_IMAGE364
And a first image dimension average
Figure 31958DEST_PATH_IMAGE366
(ii) a Obtaining from the black frame data set an image dimension and a wavelength dimension basedMean value of two wavelength dimensions
Figure 173089DEST_PATH_IMAGE368
And second image dimension average
Figure 202225DEST_PATH_IMAGE369
Based on the initial test temperature
Figure 36189DEST_PATH_IMAGE370
Fitting the second wavelength dimension average values respectively
Figure 816188DEST_PATH_IMAGE372
And second image dimension average
Figure 761011DEST_PATH_IMAGE374
And maintaining the initial test temperature
Figure 175811DEST_PATH_IMAGE375
Second wavelength dimension average of
Figure 383939DEST_PATH_IMAGE376
And second image dimension average
Figure 884190DEST_PATH_IMAGE378
。
Fig. 10 is a schematic diagram of a framework of a black frame rectification system for hyperspectral images according to an embodiment of the invention, and as shown in fig. 10, the system further includes a data acquisition unit 801, an interpolation processing unit 802, a hyperspectral black frame image sequence matrix generation unit 803, and a rectification unit 804 on the basis of the relevant calibration data acquired by the black frame calibration system in fig. 9. The data acquisition module 801 is configured to acquire shooting parameters of a current hyperspectral region, where the shooting parameters include digital gains
Figure 211134DEST_PATH_IMAGE379
Wave band
Figure 277179DEST_PATH_IMAGE380
Exposure time
Figure 688831DEST_PATH_IMAGE381
And temperature
Figure 676378DEST_PATH_IMAGE382
And loading calibration data corresponding to the current aperture and the internal gain, wherein the calibration data comprises a first wavelength dimension average value
Figure 963003DEST_PATH_IMAGE383
First image dimension average
Figure 352396DEST_PATH_IMAGE384
Matrix of fitting coefficients
Figure 197599DEST_PATH_IMAGE385
Sum vector
Figure 406864DEST_PATH_IMAGE386
(ii) a The interpolation processing module 802 is configured to average the second image dimension
Figure 231600DEST_PATH_IMAGE388
Obtained by performing band interpolation processing
Figure 475500DEST_PATH_IMAGE390
(ii) a The hyperspectral black frame image sequence matrix generation unit 803 is configured to respectively pre-generate components of a black frame in a wavelength dimension and components in an image dimension, and combine the two components to obtain a complete hyperspectral black frame image sequence matrix; the rectification unit 804 is configured to remove the hyperspectral black frame image sequence matrix and the digital gain by using the currently shot hyperspectral image
Figure 494533DEST_PATH_IMAGE391
Obtaining a corrected hyperspectral image.
According to the method and the system for calibrating and correcting the black frame in the hyperspectral image, after the black frame data of the sensor are calibrated and extracted, the required black frame data are automatically calculated and generated, the same black frame characteristic data shared by multiple frames is extracted for black frame or flat field correction according to the characteristic that the same hyperspectral image sensor can shoot multiple frames, real-time black frame deduction processing is carried out, and the shooting time of the hyperspectral sensor for black frame correction is greatly saved. Through the characteristics of the black frame signal, the change rule of the black frame signal under different exposure time, different wave bands and different sensor temperatures is found, a small amount of necessary information is extracted to serve as pre-stored data, the complete hyperspectral black frame data under different shooting parameter scenes are reconstructed in real time, and the pre-stored data space is greatly saved. The pre-stored single-frame black frame data is the result of multi-frame averaging, the fixed noise mode of the sensor is directly reflected, and the influence of random thermal noise is reduced.
The above description is only for the specific embodiment of the present invention or the description thereof, and the protection scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (16)

1. A black frame calibration method for a hyperspectral image is characterized by comprising the following steps:
s1: under the same test temperature and different exposure time, black frame acquisition is carried out on the shooting wave band to obtain a black frame data set
Figure 274182DEST_PATH_IMAGE002
Wherein, in the step (A),
Figure 850044DEST_PATH_IMAGE004
which represents the coordinates of the image and which,
Figure 160940DEST_PATH_IMAGE006
which represents the wavelength of the light emitted by the light source,
Figure 228253DEST_PATH_IMAGE008
represents an exposure time;
s2: obtaining a first wavelength dimension average from the set of black frame data based on an image dimension and a wavelength dimension
Figure 573783DEST_PATH_IMAGE010
And a first image dimension average
Figure 52038DEST_PATH_IMAGE012
Based on exposure time, respectively
Figure DEST_PATH_IMAGE014
Fitting the first wavelength dimension average values separately
Figure 522334DEST_PATH_IMAGE016
And a first image dimension average
Figure 377026DEST_PATH_IMAGE018
And the initial exposure time is kept
Figure 842642DEST_PATH_IMAGE020
First wavelength dimension average of time
Figure 242531DEST_PATH_IMAGE022
And a first image dimension average
Figure DEST_PATH_IMAGE024
The calibration data of (1).
2. The black frame calibration method for the hyperspectral image according to claim 1, wherein for the hyperspectral equipment including the temperature sensor, the method further comprises:
at the initial exposure time
Figure DEST_PATH_IMAGE026
Then, shooting and collecting black frames for different test temperature sequences to obtain a black frame matrix
Figure DEST_PATH_IMAGE028
Wherein, in the step (A),
Figure DEST_PATH_IMAGE030
it is meant that the temperature of the different tests,
Figure 305515DEST_PATH_IMAGE032
；
s4: obtaining a second wavelength dimension average from the black frame data set based on an image dimension and a wavelength dimension
Figure 573685DEST_PATH_IMAGE034
And second image dimension average
Figure 503595DEST_PATH_IMAGE036
Based on the initial test temperature
Figure 464598DEST_PATH_IMAGE038
Fitting the second wavelength dimension average values separately
Figure 627595DEST_PATH_IMAGE040
And second image dimension average
Figure 309243DEST_PATH_IMAGE042
And testing the initial test temperature
Figure 749452DEST_PATH_IMAGE044
Second wavelength dimension average of
Figure 740410DEST_PATH_IMAGE046
And second image dimension average
Figure 490DEST_PATH_IMAGE048
And storing the data into the calibration data.
3. A black frame calibration method for hyperspectral images according to claim 2, wherein the first wavelength dimension average
Figure 751409DEST_PATH_IMAGE050
The first image dimension average
Figure 905178DEST_PATH_IMAGE052
Average value of said second wavelength dimension
Figure 207984DEST_PATH_IMAGE054
The second image dimension average
Figure 565147DEST_PATH_IMAGE056
Wherein, in the step (A),
Figure 978811DEST_PATH_IMAGE058
indicating the number of bands.
4. A black frame calibration method for hyperspectral images according to claim 1, wherein the step S2 is performed by fitting the first wavelength dimension average value
Figure 255596DEST_PATH_IMAGE059
And a first image dimension average
Figure 729303DEST_PATH_IMAGE060
Respectively is a matrix
Figure 573762DEST_PATH_IMAGE062
Sum vector
Figure 525537DEST_PATH_IMAGE064
Polynomial function fitting is adopted.
5. A black frame calibration method for hyperspectral images according to claim 2, wherein the step S4 is performed by fitting the average value of the second wavelength dimension
Figure 919478DEST_PATH_IMAGE066
And second image dimension average
Figure DEST_PATH_IMAGE067
Respectively is a matrix
Figure DEST_PATH_IMAGE069
Sum vector
Figure DEST_PATH_IMAGE071
。
6. A black frame calibration method for hyperspectral images according to claim 1 or 2, characterized by further comprising acquiring corresponding calibration data at different aperture or gain values.
7. A black frame correction method for hyperspectral images, which utilizes the black frame calibration method of any one of claims 1 to 5, and is characterized by comprising the following steps:
acquiring shooting parameters of the current hyperspectral region, wherein the shooting parameters comprise digital gains
Figure 157562DEST_PATH_IMAGE073
Wave band
Figure 489317DEST_PATH_IMAGE075
Exposure time
Figure 510363DEST_PATH_IMAGE077
And temperature
Figure 227652DEST_PATH_IMAGE079
And loading calibration data corresponding to the current aperture and the internal gain, wherein the calibration data comprises the mean value of the dimensionality of the first image
Figure DEST_PATH_IMAGE080
First wavelength dimension average value
Figure DEST_PATH_IMAGE082
Fitting coefficient
Figure 980844DEST_PATH_IMAGE083
And
Figure DEST_PATH_IMAGE084
；
performing band interpolation processing on the image dimension average value;
respectively pre-generating components of a black frame in a wavelength dimension and components of the black frame in an image dimension, and combining the two components to obtain a complete hyperspectral black frame image sequence matrix;
removing the hyperspectral black frame image sequence matrix and the digital gain by using the currently shot hyperspectral image
Figure DEST_PATH_IMAGE086
Obtaining a corrected hyperspectral image.
8. A black frame rectification method for hyperspectral images according to claim 7, characterized in that for a hyperspectral device without a temperature sensor the first image dimension is averaged
Figure DEST_PATH_IMAGE088
Obtained by performing actual band interpolation processing
Figure 905288DEST_PATH_IMAGE090
。
9. A black frame rectification method for hyperspectral images according to claim 8, characterized in that the component of the pre-generated black frame in the wavelength dimension is
Figure DEST_PATH_IMAGE092
The component of the pre-generated black frame in the image dimension is
Figure DEST_PATH_IMAGE094
Therein, functional
Figure DEST_PATH_IMAGE096
To represent
Figure DEST_PATH_IMAGE098
And exposure time
Figure 854658DEST_PATH_IMAGE099
Relation, functional of
Figure 442766DEST_PATH_IMAGE101
To represent
Figure 553810DEST_PATH_IMAGE103
And exposure time
Figure DEST_PATH_IMAGE104
The relationship (2) of (c).
10. A black frame rectification method for hyperspectral images according to claim 7, characterized in that for a hyperspectral device with a temperature sensor the second image dimension is averaged
Figure DEST_PATH_IMAGE106
Obtained by performing actual band interpolation processing
Figure 188054DEST_PATH_IMAGE108
。
11. A black frame rectification method for hyperspectral images according to claim 10, characterized in that the component of the pre-generated black frame in the wavelength dimension is
Figure 412886DEST_PATH_IMAGE110
The component of the pre-generated black frame in the image dimension is
Figure 245713DEST_PATH_IMAGE112
Therein, functional
Figure 12812DEST_PATH_IMAGE114
To represent
Figure DEST_PATH_IMAGE116
And exposure time
Figure 586881DEST_PATH_IMAGE117
Relation, functional of
Figure 628787DEST_PATH_IMAGE119
To represent
Figure 50541DEST_PATH_IMAGE121
Relation to temperature T, functional
Figure 503388DEST_PATH_IMAGE123
To represent
Figure 643382DEST_PATH_IMAGE125
And exposure time
Figure 488978DEST_PATH_IMAGE117
Relation, functional of
Figure DEST_PATH_IMAGE127
To represent
Figure 686610DEST_PATH_IMAGE128
And temperature T.
12. The black frame correction method for the hyperspectral image according to claim 7, wherein the step of combining the components of the pre-generated black frame in the wavelength dimension and the components in the image dimension to obtain a complete hyperspectral black frame image sequence matrix specifically comprises the following steps: copying the components of the pre-generated black frame in the image dimension to all current bands
Figure DEST_PATH_IMAGE129
And multiplying the image data of each wave band with the component of the pre-generated black frame in the wavelength dimension at the value of the wave band to obtain a complete hyperspectral black frame image sequence matrix.
13. A black frame calibration system for hyperspectral images, comprising:
black frame data acquisition unit: the device is configured to carry out black frame acquisition on a shooting waveband at the same test temperature and in different exposure times to acquire a black frame data set
Figure 857829DEST_PATH_IMAGE131
Wherein, in the step (A),
Figure DEST_PATH_IMAGE132
which represents the coordinates of the image and which,
Figure DEST_PATH_IMAGE134
which represents the wavelength of the light emitted by the light source,
Figure 567944DEST_PATH_IMAGE104
represents an exposure time;
a calibration data acquisition unit: obtaining a first wavelength dimension average from the set of black frame data based on an image dimension and a wavelength dimension
Figure DEST_PATH_IMAGE136
And a first image dimension average
Figure DEST_PATH_IMAGE137
Based on exposure time, respectively
Figure DEST_PATH_IMAGE138
Fitting the first wavelength dimension average values separately
Figure DEST_PATH_IMAGE140
And a first image dimension average
Figure DEST_PATH_IMAGE141
And the initial exposure time is kept
Figure DEST_PATH_IMAGE142
First wavelength dimension average of time
Figure DEST_PATH_IMAGE143
And a first image dimension average
Figure DEST_PATH_IMAGE144
The calibration data of (1).
14. A black frame calibration system for hyperspectral images according to claim 13, wherein for a hyperspectral device that does not include a temperature sensor:
the black frame data acquisition unit is further configured to detect an initial exposure time
Figure DEST_PATH_IMAGE145
Then, shooting and collecting black frames for different test temperature sequences to obtain a black frame matrix
Figure DEST_PATH_IMAGE147
Wherein, in the step (A),
Figure DEST_PATH_IMAGE148
it is meant that the temperature of the different tests,
Figure DEST_PATH_IMAGE149
；
the calibration data acquisition unit is further configured to obtain a second wavelength dimension average from the set of black frame data based on an image dimension and a wavelength dimension
Figure DEST_PATH_IMAGE151
And second image dimension average
Figure DEST_PATH_IMAGE153
Based on the initial test temperature
Figure DEST_PATH_IMAGE154
Fitting the second wavelength dimension average values separately
Figure DEST_PATH_IMAGE156
And second image dimension average
Figure DEST_PATH_IMAGE158
And testing the initial test temperature
Figure DEST_PATH_IMAGE159
Second wavelength dimension average of
Figure DEST_PATH_IMAGE161
And second image dimension average
Figure DEST_PATH_IMAGE163
And storing the data into the calibration data.
15. A black frame calibration system for hyperspectral images according to claim 13, wherein the calibration data acquisition unit is further configured to acquire corresponding calibration data at different aperture or gain values.
16. A black frame rectification system for hyperspectral images, using the black frame calibration system of any of claims 13-15, further comprising:
a data acquisition unit: configuring shooting parameters for acquiring the current hyperspectral image, wherein the shooting parameters comprise digital gains
Figure DEST_PATH_IMAGE165
Wave band
Figure DEST_PATH_IMAGE167
Exposure time
Figure DEST_PATH_IMAGE168
And temperature
Figure DEST_PATH_IMAGE170
And loading calibration data corresponding to the current aperture and the internal gain, wherein the calibration data comprises a first wavelength dimension average value
Figure DEST_PATH_IMAGE171
First image dimension average
Figure DEST_PATH_IMAGE172
Matrix of fitting coefficients
Figure DEST_PATH_IMAGE174
Sum vector
Figure DEST_PATH_IMAGE175
；
An interpolation processing unit: the method comprises the steps of configuring and processing wave band interpolation on an image dimension average value;
a hyperspectral black frame image sequence matrix generation unit: the method comprises the steps of configuring components in the wavelength dimension and the image dimension of a black frame for pre-generation respectively, and combining the two components to obtain a complete hyperspectral black frame image sequence matrix;
a correction unit: configured to remove the hyperspectral black frame image sequence matrix and the digital gain using a currently captured hyperspectral image
Figure DEST_PATH_IMAGE177
Obtaining a corrected hyperspectral image.
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