CN107450347B - A kind of GPU Real-time Nonuniformity Correction method based on infrared semi-matter simulating system - Google Patents
A kind of GPU Real-time Nonuniformity Correction method based on infrared semi-matter simulating system Download PDFInfo
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Abstract
The GPU Real-time Nonuniformity Correction method based on infrared semi-matter simulating system that the invention discloses a kind of, piecemeal processing is carried out to image, gray scale rearrangement and curve fit process have been carried out to infrared image block later, the correction coefficient of full gray scale is finally obtained, and image is corrected, the present invention can carry out nonuniformity correction to the infrared image semi-matter simulating system with high-resolution, high gray scale；And due to using piecemeal processing technique to greatly reduce the usage amount of data, it is possible to carry out Nonuniformity Correction to any image that semi-matter simulating system exports based on GPU, improve the real-time of calibration result.
Description
Technical field
The present invention relates to field of image processing, in particular to a kind of GPU based on infrared semi-matter simulating system is non-in real time
Uniformity correcting method.
Background technique
With the raising of infrared system tactical qualities, high time domain, airspace, frequency domain resolving power, powerful anti-electromagnetism are done
Ability, exclusive night observation function and good battlefield adaptability are disturbed, its semi-matter simulating system also proposed higher
It is required that matched HWIL simulation also needs have precision more higher than electro-optical system, resolution ratio and dynamic range.It is infrared by half
The radiation source of matter simulating system is due to being made of a black matrix or multiple black matrixes arranged side by side, and there are the infrared spokes in spatial dimension
Penetrate the non-uniform situation of intensity；In addition, in the case where input is identical value, due to DMD (Digital Micromirror
Device data micro mirror element) there are fine differences for the response parameter of single pixel member in array, so that the image of final output
Radiation intensity be heterogeneous.Since the heterogeneity of infrared semi-matter simulating system causes to simulate the scene come distortion,
So carrying out Nonuniformity Correction to it is particularly important.
There are many Nonuniformity Corrections of infrared semi-matter simulating system at present.It include: Temperature Scaling correction side
Method, temporal high pass filter bearing calibration, multi-point correcting method.Wherein, Temperature Scaling bearing calibration outputting and inputting in system
There are when linear relationship, good calibration result can be obtained, but the post-equalization parameter that works long hours does not adapt to the present situation, needed
It re-scales, prolonged real time correction effect is undesirable；Correction of movement scene is capable of in temporal high pass filter bearing calibration
Heterogeneity, but it has that convergence rate is low and " ghost "；The heterogeneity of high gray scale may be implemented in multi-point correcting method
Correction, but this method needs to be also very big to memory space.
In conclusion that in real time correction, there are calibration results is poor, convergence rate is low, it is existing to there is " ghost " for the prior art
As and occupy memory space it is larger, to the semi-matter simulating system with high frame frequency, high-resolution and high gray scale range property into
The effect of row heterogeneity real time correction is undesirable.
Summary of the invention
The invention reside in the above-mentioned deficiency for overcoming the prior art, provide that a kind of real time correction effect is good, and convergence is fast, is not present
" ghost " phenomenon, the GPU Real-time Nonuniformity Correction method based on infrared semi-matter simulating system to occupy little space.
In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is that:
A kind of GPU Real-time Nonuniformity Correction method based on infrared semi-matter simulating system, comprising the following steps:
S1, the energy value information for acquiring the continuous C width infrared image of gray value, wherein C is the exportable maximum ash of system
Angle value；
S2, the infrared image is divided into S infrared image block, wherein the resolution ratio of the infrared image block is M*N, as
Vegetarian refreshments number L=M*N, wherein S=22,32,42,52..., and S be no more than the pixel sum of infrared image；
S3, the energy value information according to the infrared image, find out maximum desired energy value E 'maxAnd smallest ideal energy
Value E 'min, and E ' is setmaxCorresponding maximum gradation value k 'max=C-1, E 'minCorresponding minimum gradation value k 'min=0；
S4, using gray value as abscissa, energy value be ordinate establish rectangular coordinate system；By (k'min,E'min) be used as
Beginning coordinate points, (k'max,E'max) it is used as terminating coordinates point, straight line is fitted according to origin coordinates point and terminating coordinates point and is made
For the ideal capacity straight line of infrared image, the slope for obtaining straight line is a, intercept b；
S5, energy matrix is reset according to the energy matrix E of the ideal capacity straight line and infrared image block, is obtained
Gray value array A' and energy reorder matrix E' in image block after pixel rearrangement；
S6, it is carried out curve fitting according to energy-gray-scale relation of the energy reorder matrix E' to pixel in image block,
Obtain the fitting a straight line of corresponding pixel points；
S7, according to the fitting a straight line of pixel in average energy fitting a straight line and the infrared image block, utilize formula alx
+blThe relation formula of gray value after=ak+b obtains the system input gray level value k of pixel and corrects, and save gray-scale relation public affairs
The slope and intercept of formula, wherein the slope of straight line is al, intercept bl, wherein l=1,2 ..., L, L are the infrared image block
Number of pixels；
S8, step S4-S7 is successively executed to S infrared image block, obtains the pixel gray level relationship of whole infrared image
The slope and intercept of formula；
S9, the gray scale rearrangement array of the slope, intercept data, each block image is stored as texture image.
S10, it the texture image is loaded into GPU calculates, to the gray scale of each pixel in scene image
It is corrected calculating, and the gray scale after correction is exported.
Further, step S2 further include:
S21, to each infrared image block according to from left to right, sequence from top to bottom, successively by the coordinate of pixel
It stores in P, wherein P is the array that length is L, l=1,2 ..., L；
S22, to infrared image block, matrix E that size is L*C is set as energy matrix, wherein E (l, k) storage gray scale
For k, coordinate PlPixel energy value.
Further, the step S3 is specifically included:
S31, setting size and the identical matrix E of image resolution ratiomax, find out the ceiling capacity of all pixels point in image
Value, is sequentially stored in matrix, obtains the gray scale value matrix E of ceiling capacitymax；
S32, setting size and the identical matrix E of image resolution ratiomin, find out the least energy of all pixels point in image
Value, is sequentially stored in matrix, obtains the gray scale value matrix E of least energymin；
S33, the gray scale value matrix E to ceiling capacitymaxIt is compared, the minimum value in matrix data is obtained, as maximum
Ideal capacity value E 'max, and E ' is setmaxCorresponding maximum gradation value k 'max=C-1；
S34, the gray scale value matrix E to least energyminIt is compared, the maximum value in matrix data is obtained, as minimum
Ideal capacity value E 'min, and E ' is setminCorresponding minimum gradation value k 'min=0.
Further, the step S4 is specifically included:
Ideal capacity value E ' on S41, calculating ideal capacity straight line at gray value k'k', wherein k'=0,1 ..., C-1；
S42, for the pixel in the infrared image block, utilize formulaSuccessively calculate ash
The energy value error on the energy value at k and ideal capacity straight line at gray scale k' is spent, the corresponding gray scale of minimum error values is found out and makees
For the rearrangement gray value at gray scale k'；
Gray value k' is successively reset gray value storage into the array accordingly, obtained by the array that S43, setting length are C
Gray scale to the infrared image block resets array A'.
Further, the step S5 is specifically included: the matrix that setting size is L*C, in the infrared image block
Pixel, successively inquire gray scale and reset energy value in array A' in the corresponding energy matrix E of each gray value, and should
Energy value is sequentially stored in the matrix that size is L*C, obtains the energy reorder matrix E' of infrared image.
Further, the step S6 is specifically included: according to the energy reorder matrix E' of the infrared image block, to pixel
Energy-gray-scale relation of point carries out curve fitting, and obtaining slope is al, intercept blFitting a straight line, wherein l=1,2 ...,
L, L are the number of pixels of the infrared image block.
Further, the step S7 is specifically included:
S71, according to fitting a straight line, utilize formula alx+bl=ak+b obtains gray-scale relation formulaWhen the input gray level value of i.e. infrared each pixel of semi-matter simulating system is k, the pixel
Desired gray level value x；
S72, the slope for saving gray-scale relation in each pixel in infrared image blockAnd intercept
The beneficial effects of the present invention are:
The present invention, can be to high-resolution infrared image semi-matter simulating system due to carrying out piecemeal processing to image
Carry out nonuniformity correction；Gray scale rearrangement and curve fit process have been carried out to infrared image block, then carried out nonuniformity correction, significantly
Reduce the usage amount of data；And due to the correction coefficient for having obtained full gray scale, so can be acquired to semi-matter simulating system
Any image carry out nonuniformity correction, improve the real-time of calibration result.It is deposited when overcoming real time correction in the prior art
Calibration result is poor, convergence rate is low, there is a problem of " ghost " phenomenon and occupy memory space it is larger.And due to using
Piecemeal processing technique greatly reduces the usage amount of data, it is possible to be exported based on GPU to semi-matter simulating system any
Image carries out Nonuniformity Correction, improves the real-time of calibration result.
Detailed description of the invention
Fig. 1 show the GPU Real-time Nonuniformity Correction method flow of the invention based on infrared semi-matter simulating system
Figure.
Fig. 2 show energy-gray-scale relation curve graph of infrared image block pixel in the present invention.
Fig. 3 show the single pixel point energy and ideal capacity curve graph of infrared image block in the present invention.
Fig. 4 show collected infrared radiation images.
Fig. 5 show the infrared radiation images after present invention correction.
Specific embodiment
The present invention is described in further detail With reference to embodiment.But this should not be interpreted as to the present invention
The range of above-mentioned theme is only limitted to embodiment below, all that model of the invention is belonged to based on the technology that the content of present invention is realized
It encloses.
Embodiment one:
Fig. 1 show the GPU Real-time Nonuniformity Correction method flow of the invention based on infrared semi-matter simulating system
Figure, comprising the following steps:
S1, the energy value information for acquiring the continuous C width infrared image of gray value, wherein C is the exportable maximum ash of system
Angle value；
S2, the infrared image is divided into S infrared image block, wherein the resolution ratio of the infrared image block is M*N, as
Vegetarian refreshments number L=M*N, wherein S=22,32,42,52..., and S be no more than the pixel sum of infrared image；
S3, the energy value information according to the infrared image, find out maximum desired energy value E 'maxAnd smallest ideal energy
Value E 'min, and E ' is setmaxCorresponding maximum gradation value k 'max=C-1, E 'minCorresponding minimum gradation value k 'min=0；
S4, using gray value as abscissa, energy value be ordinate establish rectangular coordinate system；By (k'min,E'min) be used as
Beginning coordinate points, (k'max,E'max) it is used as terminating coordinates point, straight line is fitted according to origin coordinates point and terminating coordinates point and is made
For the ideal capacity straight line of infrared image, the slope for obtaining straight line is a, intercept b；
S5, energy matrix is reset according to the energy matrix E of the ideal capacity straight line and infrared image block, is obtained
Gray value array A' and energy reorder matrix E' in the infrared image block after pixel rearrangement；
S6, it is carried out according to energy-gray-scale relation of the energy reorder matrix E' to pixel in the infrared image block
Curve matching obtains the fitting a straight line of corresponding pixel points；
S7, according to the fitting a straight line of pixel in average energy fitting a straight line and the infrared image block, utilize formula alx
+blThe relation formula of gray value after=ak+b obtains the system input gray level value k of pixel and corrects, and save gray-scale relation public affairs
The slope and intercept of formula, wherein the slope of straight line is al, intercept bl, wherein l=1,2 ..., L, L are the infrared image block
Number of pixels；
S8, step S4-S7 is successively executed to S infrared image block, obtains the pixel gray level relationship of whole infrared image
The slope and intercept of formula；
S9, the gray scale rearrangement array of the slope, intercept data, each block image is stored as texture image.Only
With the format of texture image, just subsequent processing can will be carried out in the incoming GPU hardwares such as slope, intercept data.
S10, it the texture image is loaded into GPU calculates, to the gray scale of each pixel in scene image
It is corrected calculating, and the gray scale after correction is exported.In specific implementation, it can use infrared texture and generate software, it will be grey
Slope, intercept and gray scale in degree relation formula reset array and are converted into infrared texture file, by gray scale in entire infrared image
Slope data, intercept data and the gray scale data rearrangement of relationship are saved into respectively in slope data file, raw using infrared texture
At software, the file of slope data, intercept data and gray scale data rearrangement is converted to texture dds file respectively, it finally will be red
Outer texture file is loaded into the scene simulation software of semi-matter simulating system, to the gray scale of each pixel in scene image
It is corrected calculating, and the gray scale after correction is exported, completes the Real-time Nonuniformity Correction to semi-matter simulating system.
Since high frame frequency, high-resolution require height to Nonuniformity Correction calculation amount and calculating speed, can be mentioned using GPU
Rise calibration result, however, when due to GPU operation can received texture image memory capacity, image pixel resolution in terms of
It is all restricted, if each pixel of scene image corresponds to a gray scale data rearrangement, whole gray scale data rearrangements
It has been more than capacity and the resolution ratio limitation of the texture image that GPU can be loaded, Non-uniformity Correction Algorithm is actually can not be
It is completed under GPU.Therefore, present invention employs the algorithm for carrying out the rearrangement of piecemeal gray scale to image, reduce gray scale data rearrangement
Data volume, to realize the Non-uniformity Correction Algorithm of high-resolution, high tonal range.
The present invention, can be to high-resolution infrared image semi-matter simulating system due to carrying out piecemeal processing to image
Carry out nonuniformity correction；Gray scale rearrangement and curve fit process have been carried out to infrared image block, then carried out nonuniformity correction, significantly
Reduce the usage amount of data；And due to the correction coefficient for having obtained full gray scale, so can be acquired to semi-matter simulating system
Any image carry out nonuniformity correction, improve the real-time of calibration result.It is deposited when overcoming real time correction in the prior art
Calibration result is poor, convergence rate is low, there is a problem of " ghost " phenomenon and occupy memory space it is larger.
Specifically, step S2 further include:
S21, to each infrared image block according to from left to right, sequence from top to bottom, successively by the coordinate of pixel
It stores in P, wherein P is the array that length is L, l=1,2 ..., L；
S22, to infrared image block, matrix E that size is L*C is set as energy matrix, wherein E (l, k) storage gray scale
For k, coordinate PlPixel energy value.
Specifically, the step S3 is specifically included:
S31, setting size and the identical matrix E of image resolution ratiomax, find out the ceiling capacity of all pixels point in image
Value, is sequentially stored in matrix, obtains the gray scale value matrix E of ceiling capacitymax；
S32, setting size and the identical matrix E of image resolution ratiomin, find out the least energy of all pixels point in image
Value, is sequentially stored in matrix, obtains the gray scale value matrix E of least energymin；
S33, the gray scale value matrix E to ceiling capacitymaxIt is compared, the minimum value in matrix data is obtained, as maximum
Ideal capacity value E 'max, and E ' is setmaxCorresponding maximum gradation value k 'max=C-1；
S34, the gray scale value matrix E to least energyminIt is compared, the maximum value in matrix data is obtained, as minimum
Ideal capacity value E 'min, and E ' is setminCorresponding minimum gradation value k 'min=0.
Specifically, the step S4 is specifically included:
Ideal capacity value E' on S41, calculating ideal capacity straight line at gray value k'k', wherein k'=0,1 ..., C；
S42, for the pixel in image block, utilize formulaSuccessively calculate the energy at gray scale k
Value and the energy value error on ideal capacity straight line at gray scale k', find out the corresponding gray scale of minimum error values as gray scale k' at
Reset gray value；
Gray value k' is successively reset gray value storage into the array accordingly, obtained by the array that S43, setting length are C
Gray scale to the infrared image block resets array A'.
Specifically, the step S5 is specifically included: the matrix that setting size is L*C, in the infrared image block
Pixel successively inquires gray scale and resets energy value in array A' in the corresponding energy matrix E of each gray value, and by the energy
Magnitude is sequentially stored in the matrix that size is L*C, obtains the energy reorder matrix E' of infrared image.
Specifically, the step S6 is specifically included: according to the energy reorder matrix E' of the infrared image block, to pixel
Energy-gray-scale relation carry out curve fitting, obtain slope be al, intercept blFitting a straight line, wherein l=1,2 ..., L, L
For the number of pixels of image block.Referring specifically to Fig. 2.
Specifically, the step S7 is specifically included:
S71, according to fitting a straight line, utilize formula alx+bl=ak+b obtains gray-scale relation formula
When the input gray level value of i.e. infrared each pixel of semi-matter simulating system is k, the desired gray level value x of the pixel；
S72, the slope for saving gray-scale relation in each pixel in infrared image blockAnd interceptReferring to figure
3, Fig. 3 show the single pixel point energy and ideal capacity curve graph of infrared image block in the present invention.
Embodiment two:
Invention shows a specific examples, in this embodiment, will be infrared for comparing the implementation result of the present invention program
The frame frequency of semi-matter simulating system is set as 200Hz, and tonal range is set as 0-214, resolution ratio is fixed value 1024*768, and
The pure color figure of any gray scale of one width is input in the system, the infrared radiation images of acquisition are exported, with the method for the present invention to this
The infrared image of acquisition is corrected test.Wherein, Fig. 4 is collected infrared radiation images, and Fig. 5 show correction of the present invention
Infrared radiation images afterwards.From fig. 4, it can be seen that collected infrared image its heterogeneity is clearly, and in image
There is vignetting, this is because radiation intensity when multiple black matrixes arranged side by side has spatial non-uniformity, and dmd array has inaccuracy
Property；After correcting through the invention as can be seen from Figure 5, image is relatively uniform, demonstrates the present invention to high frame frequency, high gray scale
With validity, real-time and the stability of the Nonuniformity Correction of high-resolution infrared semi-matter simulating system.
A specific embodiment of the invention is described in detail above in conjunction with attached drawing, but the present invention is not restricted to
Embodiment is stated, in the spirit and scope for not departing from claims hereof, those skilled in the art can make
Various modifications or remodeling out.
Claims (5)
1. a kind of GPU Real-time Nonuniformity Correction method based on infrared semi-matter simulating system, which is characterized in that including following
Step:
S1, the energy value information for acquiring the continuous C width infrared image of gray value, wherein C is the exportable maximum gradation value of system；
S2, the infrared image is divided into S infrared image block, wherein the resolution ratio of the infrared image block is M*N, pixel
Number L=M*N, wherein S=22,32,42,52..., and S be no more than the pixel sum of infrared image；
S3, the energy value information according to the infrared image, find out maximum desired energy value E 'maxAnd smallest ideal energy value
E’min, and E ' is setmaxCorresponding maximum gradation value k 'max=C-1, E 'minCorresponding minimum gradation value k 'min=0；
S4, using gray value as abscissa, energy value be ordinate establish rectangular coordinate system；By (k'min,E'min) sat as starting
Punctuate, (k'max,E'max) it is used as terminating coordinates point, straight line is fitted as red according to origin coordinates point and terminating coordinates point
The ideal capacity straight line of outer image, the slope for obtaining straight line is a, intercept b；
S5, energy matrix is reset according to the energy matrix E of the ideal capacity straight line and infrared image block, is obtained described
Gray value array A' and energy reorder matrix E' in infrared image block after pixel rearrangement, specifically include:
Calculate the ideal capacity value E' on ideal capacity straight line at gray value k'k', wherein k '=0,1 ..., C-1；
For the pixel in the infrared image block, formula is utilizedSuccessively calculate the energy at gray scale k
Energy value error on magnitude and ideal capacity straight line at gray scale k', find out the corresponding gray scale of minimum error values as gray scale k' at
Rearrangement gray value；
The array that length is C is set, successively gray value k' is reset into gray value storage into the array accordingly, obtained described red
The gray scale of outer image block resets array A'；
The matrix that size is L*C is set, for the pixel in the infrared image block, successively inquires gray scale and resets in array A'
Energy value in the corresponding energy matrix E of each gray value, and the energy value is sequentially stored in the matrix that size is L*C
In, obtain the energy reorder matrix E' of infrared image；
S6, curve is carried out according to energy-gray-scale relation of the energy reorder matrix E' to pixel in the infrared image block
Fitting, obtains the fitting a straight line of corresponding pixel points；
S7, according to the fitting a straight line of pixel in average energy fitting a straight line and the infrared image block, utilize formula alx+bl=
The relation formula of gray value after ak+b obtains the system input gray level value k of pixel and corrects, and save gray-scale relation formula
Slope and intercept, wherein the slope of straight line is al, intercept bl, wherein l=1,2 ..., L, L are the picture of the infrared image block
Prime number mesh；
S8, step S4-S7 is successively executed to the S infrared image block, obtains the pixel gray level relationship of whole infrared image
The slope and intercept of formula；
S9, the gray scale rearrangement array of the slope, intercept data, each infrared image piecemeal is stored as texture image；
S10, it the texture image is loaded into GPU calculates, the gray scale of each pixel in scene image is carried out
Correction calculates, and the gray scale after correction is exported.
2. the GPU Real-time Nonuniformity Correction method according to claim 1 based on infrared semi-matter simulating system, special
Sign is, step S2 further include:
S21, to the infrared image block according to from left to right, sequence from top to bottom, successively by the coordinate storage of pixel to P
In, wherein P is the array that length is L, and l=1,2 ..., L, L are the number of pixels of the infrared image block, and l indicates pixel
Serial number；
S22, to the infrared image block, matrix E that size is L*C is set as energy matrix, wherein E (l, k) storage gray scale
For k, coordinate PlPixel energy value.
3. the GPU Real-time Nonuniformity Correction method according to claim 2 based on infrared semi-matter simulating system, special
Sign is that the step S3 is specifically included:
S31, setting size and the identical matrix E of image resolution ratiomax, find out the ceiling capacity of all pixels point in infrared image
Value, is sequentially stored in matrix, obtains the gray scale value matrix E of ceiling capacitymax；
S32, setting size and the identical matrix E of infrared image resolution ratiomin, find out the minimum of all pixels point in infrared image
Energy value is sequentially stored in matrix, obtains the gray scale value matrix E of least energymin；
S33, the gray scale value matrix E to ceiling capacitymaxIt is compared, the minimum value in matrix data is obtained, as maximum desired
Energy value E 'max, and E ' is setmaxCorresponding maximum gradation value k 'max=C-1；
S34, the gray scale value matrix E to least energyminIt is compared, the maximum value in matrix data is obtained, as smallest ideal
Energy value E 'min, and E ' is setminCorresponding minimum gradation value k 'min=0.
4. the GPU Real-time Nonuniformity Correction method according to claim 3 based on infrared semi-matter simulating system, special
Sign is that the step S6 is specifically included: according to the energy reorder matrix E' of the infrared image block, to the energy-of pixel
Gray-scale relation carries out curve fitting, and obtaining slope is al, intercept blFitting a straight line, wherein l=1,2 ..., L, L are described
The number of pixels of infrared image block.
5. the GPU Real-time Nonuniformity Correction method according to claim 4 based on infrared semi-matter simulating system, special
Sign is that the step S7 is specifically included:
S71, according to fitting a straight line, utilize formula alx+bl=ak+b obtains gray-scale relation formulaIt is i.e. infrared
When the input gray level value of each pixel of semi-matter simulating system is k, the desired gray level value x of the pixel；
S72, the slope for saving gray-scale relation in each pixel in infrared image blockAnd intercept
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102385701A (en) * | 2011-10-14 | 2012-03-21 | 华中科技大学 | Ununiformity correction method of scanning type infrared imaging system |
| CN102521797A (en) * | 2011-11-04 | 2012-06-27 | 华中科技大学 | Scene non-uniform correction method for scanning type infrared imaging system |
| CN104296876A (en) * | 2013-07-15 | 2015-01-21 | 南京理工大学 | FPGA-based scene non-uniformity correction method and device |
| CN105957034A (en) * | 2016-04-28 | 2016-09-21 | 武汉大学 | Scanning infrared imaging system scene non-uniformity correction based on registration |
| CN106373094A (en) * | 2016-08-25 | 2017-02-01 | 中国科学院长春光学精密机械与物理研究所 | Non-uniformity correction method and apparatus for infrared image |
-
2017
- 2017-07-14 CN CN201710576702.8A patent/CN107450347B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102385701A (en) * | 2011-10-14 | 2012-03-21 | 华中科技大学 | Ununiformity correction method of scanning type infrared imaging system |
| CN102521797A (en) * | 2011-11-04 | 2012-06-27 | 华中科技大学 | Scene non-uniform correction method for scanning type infrared imaging system |
| CN104296876A (en) * | 2013-07-15 | 2015-01-21 | 南京理工大学 | FPGA-based scene non-uniformity correction method and device |
| CN105957034A (en) * | 2016-04-28 | 2016-09-21 | 武汉大学 | Scanning infrared imaging system scene non-uniformity correction based on registration |
| CN106373094A (en) * | 2016-08-25 | 2017-02-01 | 中国科学院长春光学精密机械与物理研究所 | Non-uniformity correction method and apparatus for infrared image |
Non-Patent Citations (2)
| Title |
|---|
| Parallel non-uniformity correction of infrared images using CUDA;peng chen，etc;《SPIED Digital Library》;20091231;全文 * |
| 基于GPU的实时红外场景仿真系统研究;黄曦;《航空兵器》;20151231(第6期);全文 * |
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