CN115567651B - Pixel response non-uniformity correction method, system, electronic equipment and medium - Google Patents
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Abstract
The invention discloses a pixel response non-uniformity correction method, a system, electronic equipment and a medium, and relates to the technical field of pixel response correction, wherein the method comprises the following steps: acquiring micro-light observation image data output by a satellite-borne micro-light imager; the micro-light observation image data comprise longitude and latitude and radiation values; preprocessing the micro-light observation image data; performing relative deviation calculation and histogram processing on the preprocessed micro-light observation image data in sequence to determine bright line data of the observation image; and sequentially carrying out bilinear interpolation processing and mapping correction processing on the bright line data of the observed image to obtain corrected micro-light observed image data. The invention realizes the non-uniform dynamic detection and correction of the CCD response of the satellite-borne low-light-level imager, and improves the low-light-level image quality.
Description
Technical Field
The invention relates to the technical field of pixel response correction, in particular to a pixel response non-uniformity correction method, a system, electronic equipment and a medium.
Background
During the on-orbit running period of a CCD camera of the satellite-borne low-light imager, the working environment of the CCD camera is greatly different from the calibration environment of a laboratory, and the CCD camera is influenced by factors such as space radiation, single event effect and the like, and the response characteristic of the satellite CCD and the performance of circuit elements can be changed, so that the response of partial CCD pixels is abnormal. By analyzing the non-uniform response characteristics of the CCD of the satellite-borne low-light imager, the CCD is mainly shown as a plurality of continuous along-track bright lines with different intensities in the low-light image. As can be seen from the analysis of the bright line duty ratio of the long-time sequence images, the number of bright lines is obviously increased along with the increase of the on-orbit time, the positions of the newly increased bright lines are random, and the bright line response has the characteristics of time variability and nonlinearity, so that the image quality and quantitative application are seriously affected.
Disclosure of Invention
The invention aims to provide a pixel response non-uniformity correction method, a system, electronic equipment and a medium, which are used for realizing the dynamic detection and correction of the non-uniformity of the response of a CCD (charge coupled device) of a satellite-borne low-light-level imager and improving the quality of low-light-level images.
In order to achieve the above object, the present invention provides the following solutions:
in a first aspect, the present invention provides a method for correcting non-uniformity of pixel response, comprising:
acquiring micro-light observation image data output by a satellite-borne micro-light imager; the micro-light observation image data comprise longitude and latitude and radiation values;
performing relative deviation calculation and histogram processing on the micro-light observation image data in sequence to determine bright line data of the observation image;
and sequentially carrying out bilinear interpolation processing and mapping correction processing on the bright line data of the observed image to obtain corrected micro-light observed image data.
Optionally, before the step of sequentially performing the relative deviation calculation and the histogram processing on the micro-observed image data to determine the observed image bright line data, the method further includes:
removing the missing value in the micro-light observation image data to obtain actual measurement image data; the measured image data includes a measured radiation value;
judging whether the actual measured radiation value is larger than a preset radiation threshold or not according to each actual measured radiation value, and eliminating the actual measured radiation value when the actual measured radiation value is larger than the preset radiation threshold.
Optionally, the preset radiation threshold is 2.5 times of a radiation average value; the radiation average value is an average value of a plurality of the measured radiation values.
Optionally, performing relative deviation calculation and histogram processing on the micro-light observed image data in sequence to determine bright line data of the observed image, which specifically includes:
calculating the corresponding relative deviation of each measured radiation value according to a plurality of measured radiation values;
establishing a relative deviation histogram according to a plurality of the relative deviations;
determining a bright line detection threshold according to the relative deviation histogram;
and determining the bright line data of the observed image according to the bright line detection threshold.
Optionally, the measured image data further includes a measured longitude and latitude;
sequentially performing bilinear interpolation processing and mapping correction processing on the observed image bright line data to obtain corrected micro-light observed image data, wherein the method comprises the following steps of:
determining the spatial position relation between the image bright line pixels and each pixel in the first pixel group according to the measured longitude and latitude and the observed image bright line data; the first pixel group comprises a plurality of pixels adjacent to the image bright line pixel;
interpolating measured radiation values corresponding to all pixels in a first pixel group into the measured radiation values corresponding to the image bright line pixels by adopting a bilinear interpolation method based on the spatial position relation between the image bright line pixels and all pixels in the first pixel group so as to obtain a bright line reference radiation array;
and correcting the bright line data of the observed image by adopting a mapping correction method based on the bright line reference radiation array so as to obtain corrected micro-light observed image data.
Optionally, based on the bright line reference radiation array, the observed image bright line data is corrected by adopting a mapping correction method to obtain corrected micro-light observed image data, which specifically includes:
according to the formula
[x′ 1j ,x′ 2j ,…,x′ mj ]＝R([x 1j ,x 2j ,…,x mj ])
[y′ 1j ,y′ 2j ,…,y′ mj ]＝R([y 1j ,y 2j ,…,y mj ])
y′ ij ＝F j (x′ ij )
Correcting the bright line data of the observed image to obtain corrected micro-light observed image data;
wherein, array [ x ] 1j ,x 2j ,…,x mj ]Actually measured radiation value array representing bright line data of jth observation image in micro-light observation image data from 1 st row to mth row and array [ y ] 1j ,y 2j ,…,y mj ]A bright line reference radiation array for representing bright line data of a j-th observation image in the micro-light observation image data from a 1 st row to an m-th row; r is a sorting function, wherein the sorting function is used for arranging the input arrays into new arrays according to the sequence from small to large; x's' ij Representing the ordered array [ x ]' 1j ,x' 2j ,…,x' mj ]The i-th element, y' ij Representing the ordered array [ y ]' 1j ,y' 2j ,…,y' mj ]The i-th element of (a); f (F) j A correction function representing bright line data of the j-th observation image, the correction function being used for creating an ordered array [ y ]' 1j ,y' 2j ,…,y' mj ]And ordered array [ x ]' 1j ,x' 2j ,…,x' mj ]Is a mapping relation of (a) to (b).
Optionally, calculating the relative deviation corresponding to each measured radiation value according to a plurality of measured radiation values specifically includes:
determining a mark pixel and a measured radiation value corresponding to the mark pixel; the marked pixel is any pixel in the preprocessed micro-light observation image data;
determining an actual measurement radiation value corresponding to each pixel in a pixel group to be calculated according to the marked pixels; the pixel group to be calculated comprises a plurality of pixels to be calculated, and the pixels to be calculated are adjacent to the mark pixels;
calculating the relative deviation of the pixel to be calculated and the marked pixel for each pixel to be calculated in the pixel group to be calculated so as to obtain the relative deviation of adjacent pixels;
screening the largest adjacent pixel relative deviation from the plurality of adjacent pixel relative deviations; the largest adjacent pixel relative deviation is the relative deviation of the measured radiation value corresponding to the marked pixel.
In a second aspect, the present invention provides a pixel response non-uniformity correction system comprising:
the data acquisition module is used for acquiring the micro-light observation image data output by the satellite-borne micro-light imager; the micro-light observation image data comprise longitude and latitude and radiation values;
the bright line detection module is used for sequentially carrying out relative deviation calculation and histogram processing on the low-light observed image data so as to determine the observed image bright line data;
and the bright line correction module is used for sequentially carrying out bilinear interpolation processing and mapping correction processing on the bright line data of the observed image so as to obtain corrected low-light observed image data.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor;
the memory is for storing a computer program, and the processor is for executing the computer program to perform the pel response non-uniformity correction method.
A computer-readable storage medium storing a computer program;
the computer program when executed by a processor implements the steps of a method for correcting non-uniformity of pixel response.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a pixel response non-uniformity correction method, a system, electronic equipment and a medium, which sequentially perform relative deviation calculation and histogram processing on low-light observation image data output by a satellite-borne low-light imager, so as to determine bright line data of the observation image and realize automatic detection in the process; and then bilinear interpolation processing and mapping correction processing are sequentially carried out to obtain corrected micro-light observation image data, so that the problem of observation information loss caused by direct data replacement in a spatial domain is avoided. In summary, the invention can automatically identify and respond to non-uniform pixels (observed image bright line data) and complete quick correction, obviously improves the quality of low-light images, does not depend on historical data and other reference data, and has the advantages of high automation degree, strong instantaneity, good applicability and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for correcting non-uniformity of pixel response according to the present invention;
FIG. 2 is a graph showing the distribution of the relative deviation of measured radiation values in the row direction of adjacent pixels under the crescent moon condition;
FIG. 3 is a graph showing the distribution of the relative deviation of measured radiation values in the row direction of adjacent pixels in a half-month situation;
FIG. 4 is a graph showing the distribution of the relative deviation of measured radiation values in the row direction of adjacent pixels for a full month;
FIG. 5 is a schematic diagram of a pixel response non-uniformity correction system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a pixel response non-uniformity correction method, a system, electronic equipment and a medium, which can automatically identify and respond to non-uniform pixels and complete quick correction, thereby remarkably improving the quality of low-light images.
The invention will be further described in detail with reference to the drawings and detailed description below in order to make the objects, features and advantages of the invention more comprehensible.
Example 1
As shown in fig. 1, the present embodiment provides a pixel response non-uniformity correction method, including:
After step 100, before step 200, the pixel response non-uniformity correction method further comprises: preprocessing the micro-light observation image data, specifically as follows:
(1) Removing the missing value in the micro-light observation image data to obtain actual measurement image data; the measured image data includes a measured radiation value and a measured longitude and latitude. In particular, when the on-board low-light imager processes data, missing values (whether missing values in an L1 longitude and latitude data file or missing values in a radiation value data file) are generally filled with 999.9 and-999.9, and the data have no practical meaning, so as to ensure the validity of the data and reduce the complexity of subsequent calculation, the missing values are deleted.
(2) The radiation values of the strong illumination pixels (such as urban illumination pixels, wildfire pixels, lightning pixels and the like) are far higher than those of the Yu Dehai table, cloud and fog and other background radiation, and the characteristics of isolated distribution and weak continuity exist, and the strong illumination pixels are easy to introduce larger uncertainty in the determination process of the bright line data of the observation image in the step 300, so the following processing is needed:
and judging whether the actual measurement radiation value is larger than a preset radiation threshold or not according to each actual measurement radiation value, and eliminating the actual measurement radiation value when the actual measurement radiation value is larger than the preset radiation threshold so as to eliminate the interference of the strong illumination pixels. Wherein the preset radiation threshold is a radiation average value of 2.5 times; the radiation average value is an average value of a plurality of the measured radiation values.
And 200, sequentially performing relative deviation calculation and histogram processing on the micro-light observation image data to determine the bright line data of the observation image. Specifically, the pre-processed micro-light observation image data is sequentially subjected to relative deviation calculation and histogram processing.
In theory, the better the consistency of the response of each pixel of the low-light observation image data output by the satellite-borne low-light imager is, the closer the relative deviation of the radiation values of adjacent pixels in the column direction is to zero; otherwise, the more the deviation from zero. Thus, the relative deviation of the radiation values can be used as a criterion for bright line detection.
Step 300 includes:
(1) And calculating the corresponding relative deviation of each measured radiation value according to the measured radiation values. In order to realize the detection of a plurality of continuous bright lines, the relative deviation generally takes the maximum value of the calculated values of the relative deviation of each 2 adjacent pixels around a certain pixel, and the specific steps are as follows:
1) Determining a mark pixel and a measured radiation value corresponding to the mark pixel; the marked pixel is any pixel in the preprocessed micro-light observation image data.
2) Determining a pixel group to be calculated and an actual measurement radiation value corresponding to each pixel in the pixel group according to the marked pixels; the pixel group to be calculated comprises a plurality of pixels to be calculated, and the pixels to be calculated are adjacent to the mark pixels. Further, the pixel to be calculated is a pixel adjacent to the left side of the marked pixel in the row direction, or a pixel separated from the left side of the marked pixel in the row direction by one pixel, or a pixel adjacent to the right side of the marked pixel in the row direction, or a pixel separated from the right side of the marked pixel in the row direction by one pixel. Specifically, the number and the positions of the pixels to be calculated in the pixel group to be calculated can be selected according to actual needs.
3) And calculating the relative deviation of the pixel to be calculated and the marked pixel for each pixel to be calculated in the pixel group to be calculated so as to obtain the relative deviation of the adjacent pixels.
4) Screening the largest adjacent pixel relative deviation from the plurality of adjacent pixel relative deviations; the largest adjacent pixel relative deviation is the relative deviation of the measured radiation value corresponding to the marked pixel.
As can be seen from fig. 2, 3 and 4, the relative deviation remains generally around the zero relative deviation reference line, but there are also a large number of picture elements with deviations much larger than 0. In addition, in the process from crescent moon to full moon, as the illumination intensity of moon increases, the total average value of the relative deviation (total relative deviation in the figure) is reduced from 45.8% to 2.6%, which indicates that the intensity of bright line is affected by the illumination condition of moon, and the worse the illumination condition is, the more obvious the effect of bright line on the image quality is.
(2) In order to select a bright line detection threshold suitable for various moonlight conditions, a relative deviation histogram is established according to a plurality of relative deviations. From the corresponding deviation histograms of crescent, half-moon and full-moon, it is evident that: the histogram trend under three typical lunar conditions is substantially uniform, with a relative deviation of about 1800 pels (68% of the total pel count) centered around 0. As the relative deviation increases, the pixel frequency drops to about 500 and then to below 100.
(3) And determining a bright line detection threshold according to the relative deviation histogram. According to the deviation histogram feature, the relative deviation corresponding to the first dip point can be selected as the bright line detection threshold. Wherein, the threshold value is 12% at maximum in the new month; the threshold is centered at half month and 7%; the threshold is minimum at full month, 1%. In view of small relative deviation of the weak bright lines, to improve the recognition rate of the weak bright lines, a conservative threshold t=1% is selected as the bright line detection threshold.
(4) And determining the bright line data of the observed image according to the bright line detection threshold. The analysis of the bright line duty ratio of the long-time sequence image shows that the number of bright lines is obviously increased along with the increase of the on-orbit time, the relative duty ratio of the number of the annual bright lines reaches 8 percent, and the position of the new bright line has randomness.
(1) Determining the spatial position relation between the image bright line pixels and each pixel in the first pixel group according to the measured longitude and latitude and the observed image bright line data; the first pixel group comprises a plurality of pixels adjacent to the image bright line pixel, namely normal pixels around the bright line pixel.
(2) Based on the spatial position relation between the image bright line pixels and each pixel in the first pixel group, interpolating the actual measurement radiation values corresponding to each pixel in the first pixel group into the actual measurement radiation values corresponding to the image bright line pixels by adopting a bilinear interpolation method so as to obtain a bright line reference radiation array.
(3) And correcting the bright line data of the observed image by adopting a mapping correction method based on the bright line reference radiation array so as to obtain corrected micro-light observed image data.
Assuming that single micro-light observation image data comprises m rows and n columns of pixels, correcting the observation image bright line data by adopting a mapping correction method based on a bright line reference radiation array to obtain corrected micro-light observation image data, wherein the method specifically comprises the following steps of:
according to the formula
[x′ 1j ,x′ 2j ,…,x′ mj ]＝R([x 1j ,x 2j ,…,x mj ])
[y′ 1j ,y′ 2j ,…,y′ mj ]＝R([y 1j ,y 2j ,…,y mj ])
y′ ij ＝F j (x′ ij )
And correcting the bright line data of the observed image to obtain corrected micro-light observed image data.
Wherein, array [ x ] 1j ,x 2j ,…,x mj ]Actually measured radiation value array representing bright line data of jth observation image in micro-light observation image data from 1 st row to mth row and array [ y ] 1j ,y 2j ,…,y mj ]Representing the number of microoptical observation imagesAccording to the j-th column, observing bright line data of the image from a bright line reference radiation array of the 1 st row to the m-th row; r is a sorting function, wherein the sorting function is used for arranging the input arrays into new arrays according to the sequence from small to large; x's' ij Representing the ordered array [ x ]' 1j ,x' 2j ,…,x' mj ]The i-th element, y' ij Representing the ordered array [ y ]' 1j ,y' 2j ,…,y' mj ]The i-th element of (a); f (F) j A correction function representing bright line data of the j-th observation image, the correction function being used for creating an ordered array [ y ]' 1j ,y' 2j ,…,y' mj ]And ordered array [ x ]' 1j ,x' 2j ,…,x' mj ]Is a mapping relation of (a) to (b).
It should be noted that the ordered array [ x 'in the actual operation process' 1j ,x' 2j ,…,x' mj ]May have consecutive identical values, while the correction function F j One-to-many mapping cannot be achieved, so that consecutive identical x's need to be first used' ij Corresponding y' ij And (3) carrying out average treatment, and only reserving the average value to finish one-to-one mapping.
According to the correction algorithm, for any bright line radiation value x ij Should satisfy x ij ∈[x' 1j ,x' 2j ,…,x' mj ]Has x ij ＝x' kj (1.ltoreq.k.ltoreq.m). By means of a function F j Can obtain the corresponding y' kj I.e. corrected radiation values, for example: let x be 5j ＝x' 20j With y' 20j ＝F j (x' 20j ) Then y' 20j That is x 5j Corrected radiation values.
In practical application, after corrected micro-light observation image data is obtained through bright line detection and bright line correction, the micro-light observation image data can be verified. Since the bright lines can cause significant increase of the non-uniformity of the local area of the low-light image, thereby affecting the visual quality of the image, a non-uniformity index (nunu) can be used to verify the bright line correction effect of the algorithm, and the mathematical expression of the non-uniformity is as follows:
wherein x is avg Mean value of CCD pixel radiation, x ij Radiating values for individual picture elements.
In addition, in order to verify the applicability of the algorithm under different illumination conditions, the micro-light sample data of 5 typical uniform scenes such as sea surface, desert, lake ice, heavy fog, glaciers and the like are respectively selected for experiment, and after preprocessing, relative deviation calculation, histogram processing, bilinear interpolation processing and mapping correction processing are sequentially carried out on the micro-light sample data of the 5 typical uniform scenes, the following results can be obtained: a large number of bright lines exist in the original low-light-level images under different illumination conditions, and after the processing, the bright lines basically disappear from the images, so that the visual effect of the images is obviously improved.
And respectively selecting one uniform target from the micro light sample data of 5 typical uniform scenes, and respectively calculating pixel radiation non-uniformity of target scenes before and after the bright line correction for the finally obtained 5 uniform targets, wherein the result is shown in table 1.
TABLE 1 schematic table of non-uniformity of image element radiation of target scene before and after bright line correction
The overall non-uniformity refers to non-uniformity of all pixel radiation in a target scene, and reflects the effect of an algorithm on the overall quality improvement of an image; the strong bright line non-uniformity refers to non-uniformity of pixel radiation of a strong bright line column and a nearest non-bright line column forming area in a target scene (the judgment standard of the strong bright line is that the relative deviation of the radiation mean value in the column direction is more than 30%), and the restoration capability of an algorithm on image non-uniformity caused by the strong bright line is reflected on a great deal.
As can be seen from table 1, as the target scene goes from sea surface to glacier, the corresponding radiation mean value is from 3.44×10 -9 W·cm -2 ·sr -1 Gradually increase to 3.51 x 10 -8 W·cm -2 ·sr -1 . Target scene radiation rangeThe light-emitting diode spans 1 order of magnitude, covers low, medium and high reflection characteristic targets, and has good representativeness.
Comprehensively analyzing 5 scenes, wherein the overall non-uniformity of the image before the bright line correction is 21.1% on average, the non-uniformity is reduced to 11.8% after the correction, the non-uniformity is reduced by 9.3%, and the relative improvement amount is about 44% from the viewpoint of the overall improvement effect of the image; from the viewpoint of the effect of repairing the bright lines, the uniformity of the bright lines before correction is averagely 29.8 percent, the uniformity is reduced to 12 percent after correction, the uniformity is reduced by 17.8 percent, and the relative improvement amount is about 60 percent. Notably, the overall non-uniformity of the east-ocean surface bright line corrected image is 23.7%, while the non-uniformity of other 4-category target scenes is between 7% and 10%. This is mainly due to the 4.0X10 dynamic range of detection of the low-light imager -9 ～3.0×10 -2 W·cm -2 ·sr -1 Sea surface radiation approaches to the low end of instrument response, and satellite received radiation signal-to-noise ratio is low (the instrument detection sensitivity index is SNR)>[email protected]×10 -9 W·cm -2 ·sr -1 )。
To further evaluate the correction effect, an analysis can also be performed with respect to the signal-to-noise ratio under typical dark background conditions. For sea surfaces that are relatively uniform in nature, the ratio of the radiation value of the target scene to the standard deviation (i.e., the inverse of the non-uniformity) can be equivalently considered as the signal-to-noise ratio. Analysis shows that the signal-to-noise ratio of the sea surface image of the east China sea is 2 before correction, the signal-to-noise ratio of the sea surface after correction is increased to 4.2, and the signal-to-noise ratio of the sea surface can be improved by more than 1 time by the algorithm, so that the design index of the detection sensitivity of the instrument is achieved.
As can be seen from the above, the present embodiment first calculates the relative deviation of the radiation values in the column direction, determines the bright line detection threshold value by using the histogram analysis, and realizes the automatic detection. And then, a bright line reference radiation array is established by using a bilinear interpolation method, bright line correction is realized by sequencing and mapping the bright line data of the observed image and the bright line reference radiation array in a radiation domain, and the problem of observation information loss caused by direct replacement in a spatial domain is avoided.
Finally, 5 typical target scenes with low, medium and high reflectivity and uniform surfaces are selected for experiments: qualitatively, the bright lines of the processed image basically disappear, and the visual effect of the image is obviously improved; quantitatively, the overall non-uniformity of the processed image reaches 44% and the non-uniformity of the bright line reaches 60%, indicating the effectiveness of the method of this example. The analysis of the typical dark background image shows that the signal to noise ratio of the image under the condition of weak signals is increased from 2 to 4.2 before correction, and the signal to noise ratio is consistent with the design index of the instrument, so that the effect of the method of the embodiment is verified.
Example two
As shown in fig. 5, to perform a corresponding method of the above embodiment to achieve corresponding functions and technical effects, this embodiment provides a pixel response non-uniformity correction system, including:
the data acquisition module 101 is used for acquiring the micro-light observation image data output by the satellite-borne micro-light imager; the micro-optic observation image data comprises longitude and latitude and radiation values.
The bright line detection module 201 is configured to sequentially perform relative deviation calculation and histogram processing on the micro-light observed image data to determine observed image bright line data.
The bright line correction module 301 is configured to sequentially perform bilinear interpolation processing and mapping correction processing on the bright line data of the observed image, so as to obtain corrected micro-light observed image data.
Example III
The embodiment provides an electronic device, which comprises a memory and a processor; the memory is configured to store a computer program, and the processor is configured to execute the computer program to perform the pel response non-uniformity correction method described in embodiment one.
Optionally, the electronic device is a server.
In addition, the present embodiment also provides a computer-readable storage medium storing a computer program; the computer program when executed by a processor implements the steps of the pixel response non-uniformity correction method described in embodiment one.
Compared with the prior art, the invention has the following advantages:
(1) The invention can automatically identify and respond to the non-uniform pixels and complete quick correction, does not depend on historical data and other reference data, and has the advantages of high automation degree, strong instantaneity, good applicability and the like.
(2) The invention is essentially a relative calibration method, the radiation value after the bright line correction has physical meaning, can keep the spatial resolution of the image not to be reduced, and is suitable for the push-broom CCD optical remote sensing satellite radiation correction business processing without an on-board calibration device. The method can be used for solving the problem of inconsistency among cameras in the follow-up process, absolute calibration or cross calibration with similar loads are required to be carried out by utilizing uniform scenes, the data quality of the satellite-borne low-light-level imager is further improved, and a foundation is laid for quantitative application of low-light-level loads.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (6)
1. A method of correcting for pixel response non-uniformity, the method comprising:
acquiring micro-light observation image data output by a satellite-borne micro-light imager; the micro-light observation image data comprise longitude and latitude and radiation values;
removing the missing value in the micro-light observation image data to obtain actual measurement image data; the measured image data comprises a measured radiation value and a measured longitude and latitude;
performing relative deviation calculation and histogram processing on the micro-light observation image data in sequence to determine bright line data of the observation image; the method specifically comprises the following steps:
calculating the corresponding relative deviation of each measured radiation value according to a plurality of measured radiation values; the method specifically comprises the following steps: determining a mark pixel and a measured radiation value corresponding to the mark pixel; the marked pixel is any pixel in the preprocessed micro-light observation image data; determining an actual measurement radiation value corresponding to each pixel in a pixel group to be calculated according to the marked pixels; the pixel group to be calculated comprises a plurality of pixels to be calculated, and the pixels to be calculated are adjacent to the mark pixels; calculating the relative deviation of the pixel to be calculated and the marked pixel for each pixel to be calculated in the pixel group to be calculated so as to obtain the relative deviation of adjacent pixels; screening the largest adjacent pixel relative deviation from the plurality of adjacent pixel relative deviations; the largest adjacent pixel relative deviation is the relative deviation of the measured radiation value corresponding to the marked pixel;
establishing a relative deviation histogram according to a plurality of the relative deviations;
determining a bright line detection threshold according to the relative deviation histogram;
according to the bright line detection threshold value, determining bright line data of an observation image;
sequentially carrying out bilinear interpolation processing and mapping correction processing on the bright line data of the observed image to obtain corrected micro-light observed image data; comprising the following steps:
determining the spatial position relation between the image bright line pixels and each pixel in the first pixel group according to the measured longitude and latitude and the observed image bright line data; the first pixel group comprises a plurality of pixels adjacent to the image bright line pixel;
interpolating measured radiation values corresponding to all pixels in a first pixel group into the measured radiation values corresponding to the image bright line pixels by adopting a bilinear interpolation method based on the spatial position relation between the image bright line pixels and all pixels in the first pixel group so as to obtain a bright line reference radiation array;
based on the bright line reference radiation array, correcting the bright line data of the observed image by adopting a mapping correction method to obtain corrected micro-light observed image data; the method specifically comprises the following steps:
according to the formula
[x′ 1j ,x′ 2j ,…,x′ mj ]＝R([x 1j ,x 2j ,…,x mj ])
[y′ 1j ,y′ 2j ,…,y′ mj ]＝R([y 1j ,y 2j ,…,y mj ])
y′ ij ＝F j (x′ ij )
Correcting the bright line data of the observed image to obtain corrected micro-light observed image data;
wherein, array [ x ] 1j ,x 2j ,…,x mj ]Actually measured radiation value array representing bright line data of jth observation image in micro-light observation image data from 1 st row to mth row and array [ y ] 1j ,y 2j ,…,y mj ]A bright line reference radiation array for representing bright line data of a j-th observation image in the micro-light observation image data from a 1 st row to an m-th row; r is a sorting function, wherein the sorting function is used for arranging the input arrays into new arrays according to the sequence from small to large; x's' ij Representing the ordered array [ x ]' 1j ,x′ 2j ,…,x′ mj ]The i-th element, y' ij Representing the ordered array [ y ]' 1j ,y′ 2j ,…,y′ mj ]The i-th element of (a); f (F) j A correction function representing bright line data of the j-th observation image, the correction function being used for creating an ordered array [ y ]' 1j ,y′ 2j ,…,y′ mj ]And ordered array [ x ]' 1j ,x′ 2j ,…,x′ mj ]Is a mapping relation of (a) to (b).
2. The method of claim 1, wherein prior to the step of sequentially performing a relative deviation calculation and a histogram processing on the micro-observed image data to determine observed image bright line data, further comprising:
judging whether the actual measured radiation value is larger than a preset radiation threshold or not according to each actual measured radiation value, and eliminating the actual measured radiation value when the actual measured radiation value is larger than the preset radiation threshold.
3. The method of claim 2, wherein the predetermined radiation threshold is a radiation average value of 2.5 times; the radiation average value is an average value of a plurality of the measured radiation values.
4. A pixel response non-uniformity correction system, said pixel response non-uniformity correction system comprising:
the data acquisition module is used for acquiring the micro-light observation image data output by the satellite-borne micro-light imager; the micro-light observation image data comprise longitude and latitude and radiation values;
removing the missing value in the micro-light observation image data to obtain actual measurement image data; the measured image data comprises a measured radiation value and a measured longitude and latitude;
the bright line detection module is used for sequentially carrying out relative deviation calculation and histogram processing on the low-light observed image data so as to determine the observed image bright line data; the method specifically comprises the following steps:
calculating the corresponding relative deviation of each measured radiation value according to a plurality of measured radiation values; the method specifically comprises the following steps: determining a mark pixel and a measured radiation value corresponding to the mark pixel; the marked pixel is any pixel in the preprocessed micro-light observation image data; determining an actual measurement radiation value corresponding to each pixel in a pixel group to be calculated according to the marked pixels; the pixel group to be calculated comprises a plurality of pixels to be calculated, and the pixels to be calculated are adjacent to the mark pixels; calculating the relative deviation of the pixel to be calculated and the marked pixel for each pixel to be calculated in the pixel group to be calculated so as to obtain the relative deviation of adjacent pixels; screening the largest adjacent pixel relative deviation from the plurality of adjacent pixel relative deviations; the largest adjacent pixel relative deviation is the relative deviation of the measured radiation value corresponding to the marked pixel;
establishing a relative deviation histogram according to a plurality of the relative deviations;
determining a bright line detection threshold according to the relative deviation histogram;
according to the bright line detection threshold value, determining bright line data of an observation image;
the bright line correction module is used for sequentially carrying out bilinear interpolation processing and mapping correction processing on the bright line data of the observation image so as to obtain corrected low-light observation image data; comprising the following steps:
determining the spatial position relation between the image bright line pixels and each pixel in the first pixel group according to the measured longitude and latitude and the observed image bright line data; the first pixel group comprises a plurality of pixels adjacent to the image bright line pixel;
interpolating measured radiation values corresponding to all pixels in a first pixel group into the measured radiation values corresponding to the image bright line pixels by adopting a bilinear interpolation method based on the spatial position relation between the image bright line pixels and all pixels in the first pixel group so as to obtain a bright line reference radiation array;
based on the bright line reference radiation array, correcting the bright line data of the observed image by adopting a mapping correction method to obtain corrected micro-light observed image data; the method specifically comprises the following steps:
according to the formula
[x′ 1j ,x′ 2j ,…,x′ mj ]＝R([x 1j ,x 2j ,…,x mj ])
[y′ 1j ,y′ 2j ,…,y′ mj ]＝R([y 1j ,y 2j ,…,y mj ])
y′ ij ＝F j (x′ ij )
Correcting the bright line data of the observed image to obtain corrected micro-light observed image data;
wherein, array [ x ] 1j ,x 2j ,…,x mj ]Actually measured radiation value array representing bright line data of jth observation image in micro-light observation image data from 1 st row to mth row and array [ y ] 1j ,y 2j ,…,y mj ]A bright line reference radiation array for representing bright line data of a j-th observation image in the micro-light observation image data from a 1 st row to an m-th row; r is a sorting function, wherein the sorting function is used for arranging the input arrays into new arrays according to the sequence from small to large; x's' ij Representing the ordered array [ x ]' 1j ,x′ 2j ,…,x′ mj ]The i-th element, y' ij Representing the ordered array [ y ]' 1j ,y′ 2j ,…,y′ mj ]The i-th element of (a); f (F) j A correction function representing bright line data of the j-th observation image, the correction function being used for creating an ordered array [ y ]' 1j ,y′ 2j ,…,y′ mj ]And ordered array [ x ]' 1j ,x′ 2j ,…,x′ mj ]Is a mapping relation of (a) to (b).
5. An electronic device comprising a memory and a processor;
the memory is for storing a computer program, and the processor is for running the computer program to perform the pel response non-uniformity correction method of any of claims 1-3.
6. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program;
the computer program when executed by a processor implements the steps of the pixel response non-uniformity correction method according to any one of claims 1-3.
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