CN113888442A - Infrared image non-uniformity correction method and system based on fpga - Google Patents
Infrared image non-uniformity correction method and system based on fpga Download PDFInfo
- Publication number
- CN113888442A CN113888442A CN202111222399.4A CN202111222399A CN113888442A CN 113888442 A CN113888442 A CN 113888442A CN 202111222399 A CN202111222399 A CN 202111222399A CN 113888442 A CN113888442 A CN 113888442A
- Authority
- CN
- China
- Prior art keywords
- value
- image
- cumulative histogram
- fpga
- original
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000012937 correction Methods 0.000 title claims abstract description 21
- 230000001186 cumulative effect Effects 0.000 claims abstract description 77
- 238000013507 mapping Methods 0.000 claims abstract description 18
- 238000004364 calculation method Methods 0.000 claims description 18
- 230000000694 effects Effects 0.000 description 11
- 238000012935 Averaging Methods 0.000 description 10
- 238000012163 sequencing technique Methods 0.000 description 8
- 230000009466 transformation Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 241000764238 Isis Species 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003702 image correction Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000003331 infrared imaging Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000001931 thermography Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Facsimile Image Signal Circuits (AREA)
- Image Processing (AREA)
Abstract
The invention provides an infrared image non-uniformity correction method and system based on fpga, comprising the following steps: step S1: acquiring a cumulative histogram value of an original image; step S2: calculating a new value according to the cumulative histogram value; step S3: and mapping according to the new value and the original image to obtain a final image. The invention provides an infrared image non-uniformity correction method based on fpga based on a special structure of fpga and a traditional median histogram algorithm, can effectively remove stripe noise on an original infrared image, and is convenient to realize on fpga.
Description
Technical Field
The invention relates to the technical field of infrared image correction, in particular to an infrared image non-uniformity correction method and system based on fpga.
Background
With the development of focal plane arrays and the continuous maturation of image processing technologies, infrared thermal imaging systems are increasingly widely used. However, due to the limitation of the manufacturing process in the infrared imaging system, the photoelectric responses of the detection units on the focal plane array to the same irradiation are not completely consistent, which may cause the disadvantages of large noise, low contrast, poor visual effect, poor ability to distinguish image details, etc., and may seriously affect the imaging quality of the infrared system. The streak is a relatively obvious intrinsic noise on the original infrared image.
The chinese patent publication CN110211056A discloses an adaptive infrared image de-striping algorithm based on local median histogram, and finds out a window with the minimum scene change between each column and adjacent columns in the original infrared image by calculating scene complexity in a sliding window. Calculating the cumulative histograms of all the columns of the window to obtain a median histogram of the central column of the window, calculating the corrected gray value of each pixel of the central column of the window according to the median histogram, and subtracting the corrected average gray value from the original average gray value to obtain the fringe value of the column. And subtracting the fringe value from the whole column to obtain the gray value finally output by the column.
In view of the above related technologies, the inventor believes that the conventional median histogram de-striping method is complicated in calculation process and is inconvenient to implement in fpga.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an infrared image non-uniformity correction method and system based on fpga.
The invention provides an infrared image non-uniformity correction method based on fpga, which comprises the following steps:
step S1: acquiring a cumulative histogram value of an original image;
step S2: calculating a new value according to the cumulative histogram value;
step S3: and mapping according to the new value and the original image to obtain a final image.
Preferably, the step S1 includes the following steps:
step S1.1: acquiring a histogram value of each row of original images or each column of original images;
step S1.2: calculating a cumulative histogram value of each row of original images according to the histogram value of each row of original images in the step S1.1; alternatively, the first and second electrodes may be,
and calculating the cumulative histogram value of each column of original images according to the histogram value of each column of original images in the step S1.1.
Preferably, the step S2 includes the following steps:
step S2.1: determining the number of rows or columns of the cumulative histogram values participating in the calculation;
step S2.2: a new value is obtained by determining the cumulative histogram value for the number of rows or columns.
Preferably, in step S2.2, cumulative histogram value inverse transformation is performed on the cumulative histogram values with the determined number of rows or columns, and the inverse transformed cumulative histogram values are averaged by a quartile method to obtain a new value.
Preferably, in step S2.2, the inverse transformed cumulative histogram values are sorted to obtain sorted data;
the sorted data is equally divided by Q1, Q2, and Q3, wherein each section contains equally divided sorted data; obtaining the calculation formulas of Q1, Q2 and Q3:
wherein n represents the number of sorted data; q1 represents a position corresponding to a quartile P0.25; q2 represents a position corresponding to a quartile P0.5; q3 represents a position corresponding to a quartile P0.75; p0.25 represents data at the fourth quarter position of the sorted data; p0.5 represents data at the second half position of the sorted data; p0.75 represents data at the three-fourth position of the sorted data.
Preferably, in said step S2.2,
s＝k*(P0.75-P0.25)；
wherein s represents the standard deviation of the sorted data; k represents the reciprocal of the standard normal quartile range;
the minimum estimated value min of the ranking data is P0.75+1.5 s, the maximum estimated value max of the ranking data is P0.25-1.5 s, and the new value is equal to (max + min)/2.
Preferably, in the step S3, performing gray scale mapping by combining the new value, the original cumulative histogram and the original image, so as to obtain a final image;
the mapping relation is as follows:
final(i，j)＝result(Chist(image(i，j)+1，j)，j)；
wherein, (i, j) represents the ith row and the jth column, image represents an original image, and Chist represents an original accumulated histogram image; result represents the first four steps and final represents the final image.
The invention provides an infrared image non-uniformity correction system based on fpga, which comprises the following modules:
module M1: acquiring a cumulative histogram value of an original image;
module M2: calculating a new value according to the cumulative histogram value;
module M3: and mapping according to the new value and the original image to obtain a final image.
Preferably, the module M1 includes the following modules:
module M1.1: acquiring a histogram value of each row of original images or each column of original images;
module M1.2: calculating the cumulative histogram value of each row of original images according to the histogram value of each row of original images of the module M1.1; alternatively, the first and second electrodes may be,
and calculating the cumulative histogram value of each column of original images according to the histogram value of each column of original images of the module M1.1.
Preferably, the module M2 includes the following modules:
module M2.1: determining the number of rows or columns of the cumulative histogram values participating in the calculation;
module M2.2: a new value is obtained by determining the cumulative histogram value for the number of rows or columns.
Compared with the prior art, the invention has the following beneficial effects:
1. the infrared image non-uniformity correction algorithm based on the fpga is provided based on the special structure of the fpga and the traditional median histogram algorithm, so that the stripe noise on the original infrared image can be effectively removed, and the realization on the fpga is convenient;
2. the method takes the improvement of the quality of the infrared image as a starting point, can effectively remove the stripe noise on the original infrared image, can improve the contrast, enhances the infrared image and obtains good effect.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a mapping relationship diagram of the present invention;
fig. 3 is an original image, an original algorithm effect image and an algorithm effect image contrast map of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The embodiment of the invention discloses an infrared image non-uniformity correction method based on fpga, which comprises the following steps as shown in fig. 1 and fig. 2: step S1: and acquiring a cumulative histogram value of the original image, and obtaining the original cumulative histogram value. Step S1 includes the following steps: step S1.1: histogram values of each row of the original image or each column of the original image are acquired. If the original is a vertical stripe, the processing is performed by columns, and if the original is a horizontal stripe, the processing is performed by rows. The histogram value for each row or column (stripe direction) is acquired. fpga is known as Field Programmable Gate Array in english, and chinese translation is a Field-editable logic Gate Array.
Step S1.2: calculating a cumulative histogram value of each row of original images according to the histogram value of each row of original images in the step S1.1; alternatively, the cumulative histogram value of each column of original images is calculated from the histogram value of each column of original images of step S1.1. The cumulative histogram value for each row or column (stripe direction) is calculated.
Step S2: a new value is calculated from the cumulative histogram values. Step S2 includes the following steps: step S2.1: and grouping the cumulative histogram values, and determining the row number or the column number of the cumulative histogram values participating in calculation. The number of rows or columns involved in the calculation is determined, here for example 5 columns, i.e. the window size of the target column (the number of columns for which the cumulative histogram values involved in the calculation are determined) is 5 and the central column is column 3.
Step S2.2: a new value is obtained by determining the cumulative histogram value for the number of rows or columns. The histogram is averaged by determining the cumulative histogram value for the number of rows or columns.
If the principle of an original median histogram algorithm is adopted:
wherein HjFor a certain column of the cumulative histogram,
represents the inverse process of a histogram, such as: hj(222) When the number of dots representing the gradation value 222 is 10, 10 is set as 10
The gray value 222 corresponding to the number of dots of 10; where Φ (j) is the weight of the weighted average. The weight in the original median histogram algorithm uses a gaussian weight formula:
wherein sqrt represents the square of the opening, exp is the power of e, e represents the base of the natural logarithm, and d represents the distance from the current column to the center column of the window, for example, d of the 1 st column to the 3 rd column of the center column is 2. sigma is a smooth coefficient and needs to be artificially determined according to the image noise condition, but the process of implementing the Gaussian function calculation process on fpga is troublesome, so an averaging method is adopted here, and a quartile method is used.
And performing cumulative histogram value inverse transformation on the cumulative histogram values with the determined row number or column number, and averaging the cumulative histogram values subjected to inverse transformation by using a quartile method to obtain a new value. And sequencing the inversely transformed cumulative histogram values to obtain sequencing data. The inverse histogram transformation is performed and,
wherein the content of the first and second substances,
an average value representing the inverse transformed cumulative histogram values; aver represents the average value; hjThe histogram value is accumulated for the j-th column,
representing the inverse of the cumulative histogram value for column j. (j may be replaced by i, representing the ith row).
If there is
Is updated to
That is, data at the middle of a group of data is replaced with an average value. That is, the average value is used to replace the value of the middle position of the group of data, and the average value of the group of data is used to correct the value of the middle position of the group of data. The averaging method has various methods, the original algorithm uses weighted average, because the weighted average contains exponential operation, the algorithm is complex and not suitable for realizing the fpga, and the method is changed into quartering method averaging, so that the fpga is conveniently realized.
The basic principle of the quartile method: a set of data is sorted from small to large, with the entire data being divided equally into 4 parts by 3 position points (Q1, Q2, Q3), where each part contains 25% of the data. The 3 position points (Q1, Q2, Q3) correspond to the positions of the quartiles (P0.25, P0.5, P0.75). There are many ways to obtain the Q1, Q2, Q3 calculations, where n is the number of data (n is an odd number and an even number calculation is different, since image window sizes are usually odd numbers, so only the odd number calculation is described here).
The sorted data is equally divided by Q1, Q2, and Q3, wherein each section contains equally divided sorted data; obtaining the calculation formulas of Q1, Q2 and Q3:
wherein n represents the number of sorted data; q1 represents a position corresponding to a quartile P0.25; q2 represents a position corresponding to a quartile P0.5; q3 represents a position corresponding to a quartile P0.75; p0.25 represents data at the fourth quarter position of the sorted data; p0.5 represents data at the second half position of the sorted data; p0.75 represents data at the three-fourth position of the sorted data. Q represents a position in a set of data, P represents data for the Q position, P0.25 is at the Q1 position, and the Q1 position is the fourth position in the set of data.
For example, the following steps are carried out: assuming that a set of data [ 1415627 ] is arranged as [ 1124567 ] from small to large, the number of data is 7, Q1 is 2.5, Q2 is 4, Q3 is 5.5, and corresponding P0.25 is (1+2)/2 is 1.5 (the 2 nd number is 1, the 3 rd number is 2, and thus the 2.5 th number is the average), P0.5 is 4, and P0.75 is (5+6)/2 is 5.5, which are calculated according to the above formula.
If n is equal to 5, the number of n,
for a total of 5 numbers, the 5 numbers were averaged using the quartering method.
Then P0.25 corresponds to the number of Q1 positions, i.e., P0.25 is the 2 nd number of data,
the same principle is that:
next, the average value of the group data is obtained, and first, the standard deviation s of the group data is determined to be k (P0.75-P0.25), where k is obtained from a normal distribution, and since the standard normal distribution quartile interval is-0.6745 to 0.6745, the width is 0.6745 x 2 1.3490, and since k is 1/1.3490 0.7513, the quartile interval of a group data (the quartile interval of a group data is P0.75-P0.25) multiplied by 0.7513 corresponds to the group data standard deviation. The minimum estimated value min of certain data is P0.75+1.5 s, and the maximum estimated value max is P0.25-1.5 s. Therefore, the average value of a certain set of data can be approximately replaced by (max + min)/2.
s ═ k (P0.75-P0.25), where s represents the standard deviation of the ranking data; k represents the reciprocal of the standard normal quartile range. The minimum estimated value min of the sequencing data is P0.75+1.5 s, and the maximum estimated value max of the sequencing data is P0.25-1.5 s; the new value is equal to (max + min)/2. The new value is the averaged value.
Step S3: and mapping according to the new value and the original image to obtain a final image. The new value, the original cumulative histogram (initial cumulative histogram) and the original image are combined for grayscale mapping, thereby obtaining a final image. And combining the previous result, the initial cumulative histogram and the original image to perform gray mapping so as to obtain a final result.
The mapping relation is as follows:
final(i，j)＝result(Chist(image(i，j)+1，j)，j)。
wherein, (i, j) represents the ith row and the jth column, image represents an original image, and Chist represents an original accumulated histogram image; result represents the first four steps and final represents the final image.
As shown in fig. 3, the comparison graph of the original image, the original algorithm effect image and the algorithm effect image of the present invention is shown, the original image is shown at the top, and the original algorithm effect image is shown at the middle; the lower part is the effect algorithm image of the invention. As can be seen from the result graph, the improved algorithm effect is slightly reduced compared with the original algorithm effect, but the process is greatly simplified.
The invention aims to optimize the traditional median histogram de-fringe algorithm process, is convenient to realize in fpga, and can not influence the original effect. Acquiring a cumulative histogram value of a single-row (or single-column) image in a stripe direction; using a plurality of adjacent cumulative histogram values of the window (step S1 is to find the cumulative histogram of all columns, and 5 adjacent columns are used in each calculation, and these 5 adjacent columns are called adjacent columns, i.e. adjacent cumulative histogram values), the new value is calculated according to the histogram inverse transformation and the averaging algorithm.
Assuming that the original image has 100 columns, a cumulative histogram is first found for each column. Then, histogram averaging is performed every 5 columns. For the first time: taking the 1 st column to the 5 th column, and averaging to obtain a new value of the 3 rd column; and (3) for the second time: taking the 2 nd to 6 th columns, and averaging to obtain a new value of the 4 th column; and thirdly: taking the 3 rd column to the 7 th column, and averaging to obtain a new value of the 5 th column; … …, last time: the new value in column 98 is averaged from column 96 to column 100. The 5 columns taken each time are all adjacent columns.
Calculating a new value by utilizing a plurality of adjacent cumulative histogram values in the window according to the inverse transformation of the histogram and an improved average algorithm; the above results are combined with the original image and the original cumulative histogram to perform gray mapping, thereby obtaining the final image. The experimental result shows that the method can keep the effect of the original median histogram algorithm and the process is more convenient to realize on fpga.
The embodiment of the invention discloses an infrared image non-uniformity correction system based on fpga, which comprises the following modules as shown in fig. 1 and fig. 2: module M1: and acquiring a cumulative histogram value of the original image, and obtaining the original cumulative histogram value. Module M1 includes the following modules: module M1.1: histogram values of each row of the original image or each column of the original image are acquired.
Module M1.2: calculating a cumulative histogram value of each row of original images according to the histogram value of each row of original images of the module M1.1; alternatively, the cumulative histogram value of each column of original images is calculated from the histogram value of each column of original images of module M1.1.
Module M2: a new value is calculated from the cumulative histogram values. Module M2 includes the following modules: module M2.1: and grouping the cumulative histogram values, and determining the row number or the column number of the cumulative histogram values participating in calculation.
Module M2.2: a new value is obtained by determining the cumulative histogram value for the number of rows or columns. And performing cumulative histogram value inverse transformation on the cumulative histogram values with the determined row number or column number, and averaging the cumulative histogram values subjected to inverse transformation by using a quartile method to obtain a new value. And sequencing the inversely transformed cumulative histogram values to obtain sequencing data.
The sorted data is equally divided by Q1, Q2, and Q3, wherein each section contains equally divided sorted data; obtaining the calculation formulas of Q1, Q2 and Q3:
wherein n represents the number of sorted data; q1 represents a position corresponding to a quartile P0.25; q2 represents a position corresponding to a quartile P0.5; q3 represents a position corresponding to a quartile P0.75; p0.25 represents data at the fourth quarter position of the sorted data; p0.5 represents data at the second half position of the sorted data; p0.75 represents data at the three-fourth position of the sorted data.
s ═ k (P0.75-P0.25), where s represents the standard deviation of the ranking data; k represents the reciprocal of the standard normal quartile range; the minimum estimated value min of the sequencing data is P0.75+1.5 s, and the maximum estimated value max of the sequencing data is P0.25-1.5 s; the new value is equal to (max + min)/2.
Step S3: and mapping according to the new value and the original image to obtain a final image. Carrying out gray mapping by combining the new value, the original cumulative histogram and the original image so as to obtain a final image;
the mapping relation is as follows:
final(i，j)＝result(Chist(image(i，j)+1，j)，j)。
wherein, (i, j) represents the ith row and the jth column, image represents an original image, and Chist represents an original accumulated histogram image; result represents the first four steps and final represents the final image.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. An infrared image nonuniformity correction method based on fpga is characterized by comprising the following steps:
step S1: acquiring a cumulative histogram value of an original image;
step S2: calculating a new value according to the cumulative histogram value;
step S3: and mapping according to the new value and the original image to obtain a final image.
2. The fpga-based infrared image non-uniformity correction method of claim 1, wherein said step S1 comprises the steps of:
step S1.1: acquiring a histogram value of each row of original images or each column of original images;
step S1.2: calculating a cumulative histogram value of each row of original images according to the histogram value of each row of original images in the step S1.1; alternatively, the first and second electrodes may be,
and calculating the cumulative histogram value of each column of original images according to the histogram value of each column of original images in the step S1.1.
3. The fpga-based infrared image non-uniformity correction method of claim 1, wherein said step S2 comprises the steps of:
step S2.1: determining the number of rows or columns of the cumulative histogram values participating in the calculation;
step S2.2: a new value is obtained by determining the cumulative histogram value for the number of rows or columns.
4. The fpga-based infrared image non-uniformity correction method of claim 3, wherein in step S2.2, cumulative histogram values with determined number of rows or columns are inverse transformed, and the inverse transformed cumulative histogram values are averaged by a quartile method to obtain a new value.
5. The fpga-based infrared image non-uniformity correction method according to claim 3, wherein in said step S2.2, the inverse transformed cumulative histogram values are sorted to obtain sorted data;
the sorted data is equally divided by Q1, Q2, and Q3, wherein each section contains equally divided sorted data; obtaining the calculation formulas of Q1, Q2 and Q3:
wherein n represents the number of sorted data; q1 represents a position corresponding to a quartile P0.25; q2 represents a position corresponding to a quartile P0.5; q3 represents a position corresponding to a quartile P0.75; p0.25 represents data at the fourth quarter position of the sorted data; p0.5 represents data at the second half position of the sorted data; p0.75 represents data at the three-fourth position of the sorted data.
6. The fpga-based infrared image non-uniformity correction method according to claim 5, wherein in said step S2.2,
s＝k*(PO.75-P0.25)；
wherein s represents the standard deviation of the sorted data; k represents the reciprocal of the standard normal quartile range;
the minimum estimated value min of the ranking data is P0.75+1.5 s, the maximum estimated value max of the ranking data is P0.25-1.5 s, and the new value is equal to (max + min)/2.
7. The fpga-based infrared image non-uniformity correction algorithm of claim 1, wherein in said step S3, combining said new value, raw cumulative histogram and raw image to perform gray mapping to obtain a final image;
the mapping relation is as follows:
final(i，j)＝result(Chist(image(i，j)+1，j)，j)；
wherein, (i, j) represents the ith row and the jth column, image represents an original image, and Chist represents an original accumulated histogram image; result represents the first four steps and final represents the final image.
8. An infrared image nonuniformity correction system based on fpga is characterized by comprising the following modules:
module M1: acquiring a cumulative histogram value of an original image;
module M2: calculating a new value according to the cumulative histogram value;
module M3: and mapping according to the new value and the original image to obtain a final image.
9. The fpga-based infrared image non-uniformity correction system of claim 8, wherein said module M1 comprises the following modules:
module M1.1: acquiring a histogram value of each row of original images or each column of original images;
module M1.2: calculating the cumulative histogram value of each row of original images according to the histogram value of each row of original images of the module M1.1; alternatively, the first and second electrodes may be,
and calculating the cumulative histogram value of each column of original images according to the histogram value of each column of original images of the module M1.1.
10. The fpga-based infrared image non-uniformity correction system of claim 8, wherein said module M2 comprises the following modules:
module M2.1: determining the number of rows or columns of the cumulative histogram values participating in the calculation;
module M2.2: a new value is obtained by determining the cumulative histogram value for the number of rows or columns.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111222399.4A CN113888442A (en) | 2021-10-20 | 2021-10-20 | Infrared image non-uniformity correction method and system based on fpga |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111222399.4A CN113888442A (en) | 2021-10-20 | 2021-10-20 | Infrared image non-uniformity correction method and system based on fpga |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113888442A true CN113888442A (en) | 2022-01-04 |
Family
ID=79003806
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111222399.4A Pending CN113888442A (en) | 2021-10-20 | 2021-10-20 | Infrared image non-uniformity correction method and system based on fpga |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113888442A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116026780A (en) * | 2023-03-28 | 2023-04-28 | 江西中医药大学 | Method and system for online detection of coating moisture absorption rate based on series strategy wavelength selection |
| CN117239938A (en) * | 2023-11-13 | 2023-12-15 | 国网浙江省电力有限公司杭州供电公司 | Inspection control method, inspection control device, inspection control system, inspection control equipment and inspection control medium for power distribution station |
-
2021
- 2021-10-20 CN CN202111222399.4A patent/CN113888442A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116026780A (en) * | 2023-03-28 | 2023-04-28 | 江西中医药大学 | Method and system for online detection of coating moisture absorption rate based on series strategy wavelength selection |
| CN117239938A (en) * | 2023-11-13 | 2023-12-15 | 国网浙江省电力有限公司杭州供电公司 | Inspection control method, inspection control device, inspection control system, inspection control equipment and inspection control medium for power distribution station |
| CN117239938B (en) * | 2023-11-13 | 2024-02-23 | 国网浙江省电力有限公司杭州供电公司 | Inspection control method, inspection control device, inspection control system, inspection control equipment and inspection control medium for power distribution station |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113888442A (en) | Infrared image non-uniformity correction method and system based on fpga | |
| CN111814867A (en) | Defect detection model training method, defect detection method and related device | |
| CN108564549B (en) | Image defogging method based on multi-scale dense connection network | |
| CN110211056B (en) | Self-adaptive infrared image de-striping algorithm based on local median histogram | |
| CN101127908B (en) | Video image motion processing method and implementation device with global feature classification | |
| CN109934826A (en) | A kind of characteristics of image dividing method based on figure convolutional network | |
| CN111899194A (en) | Method for removing cloud and cloud shadow in remote sensing image | |
| CN114612469A (en) | Product defect detection method, device and equipment and readable storage medium | |
| CN111311512B (en) | Random noise correction method | |
| CN111179184B (en) | Fish-eye image effective region extraction method based on random sampling consistency | |
| US20130084025A1 (en) | Method for Brightness Correction of Defective Pixels of Digital Monochrome Image | |
| US20170195520A1 (en) | Image processing method, apparatus, and image forming device | |
| CN111369449A (en) | Infrared blind pixel compensation method based on generating type countermeasure network | |
| CN114140416A (en) | Glass edge detection method and system based on machine vision | |
| CN115619678B (en) | Correction method and device for image deformation, computer equipment and storage medium | |
| CN106845550B (en) | Image identification method based on multiple templates | |
| CN114418873B (en) | Dark light image noise reduction method and device | |
| CN110852966B (en) | Image noise estimation method based on deep convolutional neural network | |
| CN114998186A (en) | Image processing-based method and system for detecting surface scab defect of copper starting sheet | |
| CN111985324B (en) | Road detection method combining full convolution regression neural network and conditional random field | |
| CN112766338A (en) | Method, system and computer readable storage medium for calculating distance image | |
| Meena et al. | Review and application of different contrast enhancement technique on various images | |
| CN111612766A (en) | Image quality evaluation method and device and electronic equipment | |
| CN111523564A (en) | SAR time-sensitive target sample augmentation method for deep learning training | |
| Çam et al. | The performance evaluation of the cat and particle swarm optimization techniques in the image enhancement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |