CN115713474B - Infrared image correction method and system and electronic equipment - Google Patents
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Abstract
The embodiment of the invention discloses an infrared image correction method, an infrared image correction system and electronic equipment, which are applied to an FPGA processor of the infrared image correction system, wherein the method comprises the following steps: acquiring continuously input infrared image data; identifying a bad cluster region in the infrared image data, wherein the bad cluster region comprises a preset number of bad pixels; constructing a target matrix, wherein the target matrix comprises the bad cluster region; and carrying out preset replacement processing on the central point of the target matrix until all the dead pixels finish the preset replacement processing. The infrared image correction method provided by the invention can automatically identify the bad cluster region in the infrared image, and carry out integral correction on the bad cluster region based on the mode of constructing the target matrix, so that the imaging effect of the infrared image detection equipment is effectively improved under the condition of lower cost.
Description
Technical Field
The present invention relates to the field of infrared imaging technologies, and in particular, to an infrared image correction method, an infrared image correction system, and an electronic device.
Background
At present, the uncooled infrared detector has the advantage of miniaturization with low cost, and is widely applied to the fields of military and civil engineering. However, the infrared detector has defects in a plurality of links such as process, production, transportation and storage in the manufacturing process, so that an infrared image acquired by the infrared image sensor has abnormal points, namely dead points.
A plurality of continuous dead pixels form a dead cluster, and the large-scale dead clusters can cause poor visual effect of an infrared image acquired by an infrared image sensor. In the FPGA image processing scheme of the infrared detector, the processing process of the bad clusters is very critical, and the imaging quality and performance of the infrared detector are directly determined. In the conventional FPGA image processing scheme, an infrared image correction scheme for integrally processing large-scale bad clusters is not provided.
Therefore, there is a need for an infrared image correction scheme that can process bad clusters as a whole.
Disclosure of Invention
In order to solve the above technical problems, embodiments of the present application provide an infrared image correction method, an infrared image correction system, and an electronic device, where the specific scheme is as follows:
in a first aspect, an embodiment of the present application provides an infrared image correction method, applied to an FPGA processor of an infrared image correction system, including:
acquiring continuously input infrared image data;
identifying a bad cluster region in the infrared image data, wherein the bad cluster region comprises a preset number of bad pixels;
constructing a target matrix, wherein the target matrix comprises the bad cluster region;
and carrying out preset replacement processing on the central point of the target matrix until all the dead pixels finish the preset replacement processing.
According to a specific implementation manner of the embodiment of the present application, the FPGA processor includes a preset number of first-in first-out buffers, and the step of constructing the target matrix includes:
caching the continuously input infrared image data through the first-in first-out buffer;
the target matrix is constructed based on the output data of all the first-in first-out buffers and the raw data of the infrared image data.
According to a specific implementation manner of the embodiment of the present application, the number of the first-in first-out buffers is greater than or equal to 4, and the target matrix is at least a 5×5 matrix.
According to a specific implementation manner of the embodiment of the present application, the target matrix includes a preset number of matrix points, each matrix point has a corresponding weight, where the weight of a center matrix point is highest, and the weight of a matrix point is lower the farther from the center matrix point;
the step of performing preset replacement processing on the center point of the target matrix includes:
determining good point conditions and bad point conditions of all matrix points in the target matrix except the center point;
calculating a first correction value according to the good point condition and the bad point condition;
calculating a second correction value according to the pixel values and weights of all the good points;
and replacing the center point based on a target correction value, wherein the target correction value is obtained by dividing the second correction value by the first correction value.
According to a specific implementation manner of the embodiment of the present application, after the step of obtaining the target matrix, the method further includes:
constructing a preset replacement matrix according to the position of a bad cluster area in the target matrix, wherein the preset replacement matrix is identical to the central point of the target matrix, and the specification of the preset replacement matrix is smaller than that of the target matrix;
the step of performing preset replacement processing on the center point of the target matrix includes:
and carrying out preset replacement processing on the center point of the target matrix based on the preset replacement matrix.
According to a specific implementation manner of the embodiments of the present application, the step of performing a preset replacement process on the center point of the target matrix based on the preset replacement matrix includes:
determining good point conditions and bad point conditions of all matrix points adjacent to the central point;
calculating pixel replacement values of all dead pixels according to a first correction algorithm;
calculating the pixel replacement value of the center point according to the second correction algorithm, the pixel replacement value of each bad point and the pixel value of each good point;
and carrying out replacement processing on the center point based on the pixel replacement value of the center point.
According to a specific implementation manner of the embodiment of the present application, the preset replacement matrix is at least a 3×3 matrix.
In a second aspect, embodiments of the present application provide an infrared image correction system, the system comprising: the infrared detector, the AD conversion chip and the FPGA processor are connected in sequence;
the infrared detector is used for acquiring analog data of an infrared image;
the AD conversion chip is used for converting the analog data into digital data so as to obtain the infrared image data;
the FPGA processor is used for acquiring continuously input infrared image data; identifying a bad cluster region in the infrared image data, wherein the bad cluster region comprises a preset number of bad pixels; constructing a target matrix, wherein the target matrix comprises the bad cluster region; and carrying out preset replacement processing on the central point of the target matrix until all the dead pixels finish the preset replacement processing.
According to a specific implementation manner of the embodiment of the present application, the FPGA processor is further configured to construct a preset replacement matrix according to a position of a bad cluster area in the target matrix, where the preset replacement matrix is the same as a center point of the target matrix, and a specification of the preset replacement matrix is smaller than a specification of the target matrix;
and carrying out preset replacement processing on the center point of the target matrix based on the preset replacement matrix.
In a third aspect, an embodiment of the present application provides an electronic device, where the electronic device includes the infrared image correction system described in the foregoing second aspect and any embodiment of the second aspect.
The embodiment of the application provides an infrared image correction method, an infrared image correction system and electronic equipment, which are applied to an FPGA processor of the infrared image correction system, wherein the method comprises the following steps: acquiring continuously input infrared image data; identifying a bad cluster region in the infrared image data, wherein the bad cluster region comprises a preset number of bad pixels; constructing a target matrix, wherein the target matrix comprises the bad cluster region; and carrying out preset replacement processing on the central point of the target matrix until all the dead pixels finish the preset replacement processing. The infrared image correction method provided by the invention can automatically identify the bad cluster region in the infrared image, and carry out integral correction on the bad cluster region based on the mode of constructing the target matrix, so that the imaging effect of the infrared image detection equipment is effectively improved under the condition of low cost consumption.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are required for the embodiments will be briefly described, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope of the present invention. Like elements are numbered alike in the various figures.
Fig. 1 is a schematic flow chart of a method for correcting an infrared image according to an embodiment of the present application;
fig. 2 shows one of application scenario diagrams of an infrared image correction method according to an embodiment of the present application;
fig. 3 shows a second application scenario of an infrared image correction method according to an embodiment of the present application;
fig. 4 illustrates a third application scenario of an infrared image correction method according to an embodiment of the present application;
fig. 5 shows a fourth application scenario of an infrared image correction method according to an embodiment of the present application;
fig. 6 shows a fifth application scenario of an infrared image correction method according to an embodiment of the present application;
fig. 7 shows a sixth application scenario of an infrared image correction method according to an embodiment of the present application;
fig. 8 is a schematic diagram of a system module of an infrared image correction system according to an embodiment of the present application.
Summarizing the reference numerals:
an infrared image correction system-800; an FPGA processor-810; an infrared detector-820; AD conversion chip-830.
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.
The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
The terms "comprises," "comprising," "including," or any other variation thereof, are intended to cover a specific feature, number, step, operation, element, component, or combination of the foregoing, which may be used in various embodiments of the present invention, and are not intended to first exclude the presence of or increase the likelihood of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the invention belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having a meaning that is the same as the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in connection with the various embodiments of the invention.
Referring to fig. 1, a method flow diagram of an infrared image correction method provided in an embodiment of the present application is provided, where the infrared image correction method provided in the embodiment of the present application is applied to an FPGA processor of an infrared image correction system, as shown in fig. 1, and the infrared image correction method includes:
step S101, acquiring continuously input infrared image data;
in a specific embodiment, the infrared image correction system further comprises an infrared image acquisition device such as an infrared detector, an infrared sensor or an infrared camera, and an AD converter for converting analog signals acquired by the infrared image acquisition device into digital signals.
Specifically, the infrared image data is processed digital data.
In a specific implementation process, as shown in fig. 2, the infrared image data is Pixel information continuously collected by the infrared detector, where each grid of pixels 11-55 represents energy data of one infrared Pixel.
Step S102, identifying a bad cluster area in the infrared image data, wherein the bad cluster area comprises a preset number of bad pixels;
in a specific embodiment, the infrared image correction system in this embodiment further includes a CPU processor. In this embodiment, a processor with a suitable specification may be selected according to an actual application scenario to perform the step of identifying the bad cluster area, for example, the specification of the CPU processor may use embedded Cortex-M3. Specifically, the CPU processor is electrically connected to the FPGA processor, and in this embodiment, the CPU processor may be regarded as a subunit of the FPGA processor.
After the infrared image data are acquired, the bad point conditions in the infrared image data are automatically identified through a CPU processor, and each bad point is marked in a mode of adding marking bits. Specifically, the dead pixel condition is information related to the dead pixel, such as the dead pixel, the dead pixel number, and the like.
When the number of the bad points in the infrared image data exceeds a preset threshold value, determining that the infrared image comprises a bad cluster area, and marking the bad cluster area by adding a marking bit.
In a specific implementation process, the infrared image correction method provided in this embodiment may further determine whether to continue to execute step S103 according to a dead pixel condition in the infrared image data.
In an embodiment, if it is identified that the number of dead points in the infrared image data is smaller than the preset number threshold, the infrared image correction process in this embodiment may be suspended when no imaging effect is caused on the infrared image, and the dead points of the infrared image may be corrected by a conventional 9-point correction method.
It should be noted that the above case is merely an example, and when the number of bad points in the infrared image data is smaller than the preset number threshold, further processing may be performed by the infrared image correction method provided in the present embodiment.
If it is identified that the infrared image data includes a bad cluster region, step S103 is continuously performed.
Step S103, constructing a target matrix, wherein the target matrix comprises the bad cluster region;
specifically, the correction processing is performed on the bad clusters in the infrared image by constructing the target matrix in this embodiment, and the target matrix constructed in this embodiment may be as shown in fig. 2 to 7.
According to a specific implementation manner of the embodiment of the present application, the FPGA processor includes a preset number of first-in first-out buffers, and the step of constructing the target matrix includes:
caching the continuously input infrared image data through the first-in first-out buffer;
the target matrix is constructed based on the output data of all the first-in first-out buffers and the raw data of the infrared image data.
Specifically, the FPGA processor provided in this embodiment includes a preset number of first-in first-out (First In First Out, abbreviated as FIFO) buffers, and matrix construction with any specification can be implemented by adding raw data of infrared image data to the buffering action of the preset number of FIFO buffers.
Specifically, the preset number and specific specification of the FIFO buffers may be adaptively set according to the requirement of building the target matrix in the actual application scenario, which is not limited only herein.
As shown in fig. 2, in this embodiment, 4 FIFO buffers may be provided, the continuously input infrared image data is buffered by the 4 FIFO buffers, and five-line data may be formed by the output of the 4 FIFO buffers and the original infrared image data, thereby forming one 5Target matrix of 5 specifications.
According to a specific implementation manner of the embodiment of the present application, the number of the first-in first-out buffers is greater than or equal to 4, and the target matrix is at least a 5×5 matrix.
In a specific embodiment, the infrared image correction methods provided in this embodiment all adopt target matrices greater than or equal to 5×5 matrices to implement correction processing for bad clusters in the infrared image. The target matrix of the 5 multiplied by 5 matrix can not cause too high requirements on an FPGA processor of the detector while ensuring the imaging effect of the infrared image improvement, and can realize the efficient infrared imaging improvement effect while consuming smaller cost.
Step S104, performing preset replacement processing on the central point of the target matrix until all the dead points complete the preset replacement processing.
In a specific embodiment, after all the bad points in the bad cluster area are replaced based on the target matrix, the correction processing on the infrared image is completed.
In this embodiment, the correction processing for the bad clusters in the infrared image is implemented by performing bad point correction on the center point of the target matrix. In the practical application process, as shown in FIG. 3, whenPixel133, pixel134, pixel143, and Pixel Pixel144When they are all dead points, firstly, the method is toPixel133Correction is performed, after the next infrared image data is acquired, pixel134 is moved to the center point of the target matrix, at this time, forPixel134Correction is performed.
It is to be noted that, when the point of the center point position of the target matrix is a good point, the preset replacement processing is not performed.
According to a specific implementation manner of the embodiment of the present application, after the step of obtaining the target matrix, the method further includes:
constructing a preset replacement matrix according to the position of a bad cluster area in the target matrix, wherein the preset replacement matrix is identical to the central point of the target matrix, and the specification of the preset replacement matrix is smaller than that of the target matrix;
the step of performing preset replacement processing on the center point of the target matrix includes:
and carrying out preset replacement processing on the center point of the target matrix based on the preset replacement matrix.
In a specific embodiment, the present embodiment performs algorithm correction on each bad point in the bad cluster area by constructing a matrix with smaller specification in the target matrix, so as to achieve imaging improvement on the infrared image.
Specifically, in this embodiment, the target matrix is at least 5×5 matrix, and the preset replacement matrix is at least 33 matrix. It is to be noted that the target matrix may also be 6 +.>6 or 7->7 matrix, the preset replacement matrix can also be 4 +.>4 or 5->5 matrix, the specific specifications of the target matrix and the preset replacement matrix are not limited in this embodiment, and a suitable matrix can be selected according to the actual application scenarioThe matrix specification is replaced.
According to a specific implementation manner of the embodiments of the present application, the step of performing a preset replacement process on the center point of the target matrix based on the preset replacement matrix includes:
determining good point conditions and bad point conditions of all matrix points adjacent to the central point;
calculating pixel replacement values of all dead pixels according to a first correction algorithm;
calculating the pixel replacement value of the center point according to the second correction algorithm, the pixel replacement value of each bad point and the pixel value of each good point;
and carrying out replacement processing on the center point based on the pixel replacement value of the center point.
In a specific embodiment, the method for determining the adjacency of the center point is eight adjacencies, namely, the method comprises the steps of up-down, left-right adjacency and diagonal adjacency.
For the way the center point pixel is calculated, reference may be made to the following example.
Referring to FIG. 3, a bad pixel cluster is formed by 4 bad pixels, and 3×3 matrix is selected from 5×5 matricesPixel33Correction is performed. As shown in the figure 3 of the drawings,Pixel33、Pixel34、Pixel43andPixel44all are dead pixels.
For a pair ofPixel33The specific calculation formula for correction is as follows:
Pixel33=(Pixel22+Pixel23+Pixel24+Pixel32+Pixel34+Pixel42+Pixel43+ Pixel44)/8wherein, the method comprises the steps of, wherein,
Pixel34= （Pixel23 + Pixel24 + Pixel25 + Pixel35 + Pixel45）/5，
Pixel44= （Pixel35 + Pixel45 + Pixel53 + Pixel54 + Pixel55）/5，
Pixel43= （Pixel32 + Pixel42 + Pixel52 + Pixel53 + Pixel54）/5。
referring to FIG. 4, a cluster of 5 bad pixels is shown at 55 is selected from the matrix of 3->3 matrix of pairsPixel33Correction is performed. As shown in figure 4 of the drawings,Pixel33、Pixel34、Pixel43、Pixel44andPixel153all are dead pixels.
For a pair ofPixel33The specific calculation formula for correction is as follows:
Pixel33=(Pixel22+Pixel23+Pixel24+Pixel32+Pixel34+Pixel42+Pixel43+ Pixel44)/8wherein, the method comprises the steps of, wherein,
Pixel34= （Pixel23 + Pixel24 + Pixel25 + Pixel35 + Pixel45）/5，
Pixel44= （Pixel35 + Pixel45 + Pixel54 + Pixel55）/4，
Pixel43= （Pixel32 + Pixel42 + Pixel52 + Pixel54）/4。
referring to FIG. 5, a bad point cluster is formed by 6 bad points, at this time, 3 are selected from a 5×5 matrix3 matrix of pairsPixel33Correction is performed. As shown in figure 5 of the drawings,Pixel33、Pixel34、Pixel43、Pixel44、Pixel153andPixel154all are dead pixels.
For a pair ofPixel33The specific calculation formula for correction is as follows:
Pixel33=(Pixel22+Pixel23+Pixel24+Pixel32+Pixel34+Pixel42+Pixel43+ Pixel44)/8wherein, the method comprises the steps of, wherein,
Pixel34= （Pixel23 + Pixel24 + Pixel25 + Pixel35 + Pixel45）/5，
Pixel44= （Pixel35 + Pixel45 + Pixel55）/3，
Pixel43= （Pixel32 + Pixel42 + Pixel52）/3。
referring to FIG. 6, a bad point cluster is formed by 9 bad points, at this time, 3 are selected from a 5×5 matrix3 matrix of pairsPixel33Correction is performed. As shown in the figure 3 of the drawings,Pixel22、Pixel23、Pixel24、Pixel32、Pixel34、Pixel42、 pixel43 and Pixel44All are dead pixels.
For a pair ofPixel33The specific calculation formula for correction is as follows:
Pixel33=(Pixel22+Pixel23+Pixel24+Pixel32+Pixel34+Pixel42+Pixel43+ Pixel44)/8wherein, the method comprises the steps of, wherein,
Pixel22 = （Pixel11 + Pixel12 + Pixel13 + Pixel21 + Pixel31）/5，
Pixel23 = （Pixel12 + Pixel13 + Pixel14 ）/3，
Pixel24= （ Pixel13 + Pixel35 + Pixel45+ Pixel25 + Pixel35）/5，
Pixel32= （Pixel21 + Pixel31 + Pixel41）/3，
Pixel34 = （Pixel25 + Pixel35 + Pixel45 ）/3，
Pixel42= （Pixel31 + Pixel41 + Pixel51 + Pixel52 + Pixel53）/5，
Pixel43 = （Pixel52+ Pixel53 + Pixel54 ）/3，
Pixel44= （Pixel35 + Pixel45 + Pixel55 + Pixel53 + Pixel54）/5。
in the implementation process, if the specifications of the target matrix and the preset replacement matrix are adjusted along with the actual application scenario, the first correction algorithm and the second correction algorithm provided in this embodiment may also be adaptively adjusted according to the algorithm logic, which is not described in detail herein.
In addition, the above calculation formulas are all exemplary and not specifically limited, and the specific calculation formulas can be adjusted according to the distribution condition of each bad point in the bad cluster area in the actual application scenario, which is not described in detail herein.
In this embodiment, in addition to the process of implementing the correction replacement for the bad cluster by using the preset replacement matrix, the correction process for the bad cluster may also be implemented directly by the target matrix.
According to a specific implementation manner of the embodiment of the present application, the target matrix includes a preset number of matrix points, each matrix point has a corresponding weight, where the weight of a center matrix point is highest, and the weight of a matrix point is lower the farther from the center matrix point;
the step of performing preset replacement processing on the center point of the target matrix includes:
determining good point conditions and bad point conditions of all matrix points in the target matrix except the center point;
calculating a first correction value according to the good point condition and the bad point condition;
calculating a second correction value according to the pixel values and weights of all the good points;
and replacing the center point based on a target correction value, wherein the target correction value is obtained by dividing the second correction value by the first correction value.
In a specific embodiment, as shown in fig. 7, each matrix point of the target matrix has a corresponding weight, where in this embodiment, the weight of the center matrix point is the highest 8. The weight of the matrix points adjacent to the center matrix point in the vertical and horizontal directions is 6, the weight of the matrix points adjacent to the diagonal direction of the center matrix point is 4, and the weight of the matrix points decreases in sequence as the distance from the center matrix point is farther.
In the implementation process, the good point condition and the return electricity condition of all matrix points in the target matrix can be counted, for example, if the bad point distribution condition in fig. 6 is used for calculation.
Wherein,Pixel33 = total_Pixel / num_totalwherein, the method comprises the steps of, wherein,total_Pixelfor representing the second correction value in question,num_totalfor representing said first correction value.
The first correction value is obtained by identifying all dead pixels through marks, wherein the dead pixels do not participate in calculation, and the first correction value is calculated according to the positions and weights of the dead pixels in the dead pixel condition. For example, such asAs shown in figure 6 of the drawings,num_total=1 + 2 + 4 + 2 + 1 + 2 + 2 + 4 + 4 + 2 + 2 + 1 + 2 + 4 + 2 + 1。
the second correction value is obtained by accumulating pixel values of all good points according to the proportion value of weight distribution.
In a specific embodiment, the good points and the bad points in the target matrix can be distinguished by setting different marking bits, which is not particularly limited herein.
In summary, the embodiment provides an infrared image correction method, which can correct bad clusters in an infrared image by constructing a target matrix with 5×5 specifications, so that the FPGA processor is effectively ensured to have enough processing performance to cope with the bad clusters with larger scale, and the defect of processing the infrared image by using a 9-point correction method conventionally is effectively overcome. In addition, the embodiment can also realize correction and replacement of the dead pixel of the center point of the target matrix by constructing a preset replacement matrix with the specification of 3 multiplied by 3 or directly using a weight marking mode, so that the imaging quality of an infrared image can be effectively improved while the efficiency is ensured, and the product quality of the infrared detector is improved.
Referring to fig. 8, a schematic diagram of a system module of an infrared image correction system 800 according to an embodiment of the present application is provided, where, as shown in fig. 8, the infrared image correction system 800 provided in the embodiment of the present application includes: the infrared detector 830, the AD conversion chip 820 and the FPGA processor 810 are sequentially connected;
the infrared detector 830 is configured to obtain analog data of an infrared image;
the AD conversion chip 820 is configured to convert the analog data into digital data to obtain the infrared image data;
the FPGA processor 810 is configured to obtain continuously input infrared image data; identifying a bad cluster region in the infrared image data, wherein the bad cluster region comprises a preset number of bad pixels; constructing a target matrix, wherein the target matrix comprises the bad cluster region; and carrying out preset replacement processing on the central point of the target matrix until all the dead pixels finish the preset replacement processing.
In a specific embodiment, the type of the infrared detector 830 may be a non-refrigeration type infrared detector, and the product type may be a gwir_03_18_x2a infrared detector; the FPGA processor 810 may be an FPGA model M2S060-FCS 325.
It should be noted that, in this embodiment, specific models and specifications of the infrared detector 830, the AD conversion chip 820, and the FPGA processor 810 are not limited, and a suitable product model may be selected for use according to an actual application scenario.
According to a specific implementation manner of the embodiment of the present application, the FPGA processor 810 is further configured to construct a preset replacement matrix according to a position of a bad cluster area in the target matrix, where the preset replacement matrix is the same as a center point of the target matrix, and a specification of the preset replacement matrix is smaller than a specification of the target matrix;
and carrying out preset replacement processing on the center point of the target matrix based on the preset replacement matrix.
In addition, the embodiment of the application also provides electronic equipment, which comprises the infrared image correction system provided by the embodiment of the system.
Specifically, the electronic device provided in the embodiment may be any type of infrared detection device, which is not limited herein.
The specific implementation process of the infrared image correction system and the electronic device mentioned in the above embodiments may refer to the specific implementation process of the above method embodiments, which is not described herein in detail.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners as well. The apparatus embodiments described above are merely illustrative, for example, of the flow diagrams and block diagrams in the figures, which illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules or units in various embodiments of the invention may be integrated together to form a single part, or the modules may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a smart phone, a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention.
Claims (8)
1. An infrared image correction method, characterized by being applied to an FPGA processor of an infrared image correction system, the method comprising:
acquiring continuously input infrared image data;
identifying a bad cluster region in the infrared image data, wherein the bad cluster region comprises a preset number of bad pixels;
constructing a target matrix, wherein the target matrix comprises the bad cluster region; the center point of the target matrix is a bad point of the bad cluster area;
the method comprises the following steps of:
determining good point conditions and bad point conditions of all matrix points adjacent to the central point;
calculating pixel replacement values of all dead pixels according to a first correction algorithm;
calculating the pixel replacement value of the center point according to the second correction algorithm, the pixel replacement value of each bad point and the pixel value of each good point;
performing replacement processing on the center point based on the pixel replacement value of the center point;
or, performing preset replacement processing on the center point of the target matrix, wherein the steps include:
determining good point conditions and bad point conditions of all matrix points in the target matrix except the center point;
calculating a first correction value according to the good point condition and the bad point condition;
the target matrix comprises a preset number of matrix points, each matrix point has a corresponding weight, wherein the weight of a center matrix point is highest, and the weight of a matrix point is lower when the matrix point is far from the center matrix point; calculating a second correction value according to the pixel values and weights of all the good points;
performing replacement processing on the center point based on a target correction value, wherein the target correction value is obtained by dividing a second correction value by a first correction value;
after the preset replacement processing is carried out on the central point of the current target matrix, the target matrix is moved so that the central point of the target matrix is another bad point of the bad cluster area, and the preset replacement processing is carried out on the central point of the moved target matrix until all the bad points complete the preset replacement processing.
2. The method of claim 1, wherein the FPGA processor includes a predetermined number of first-in first-out buffers, and the step of constructing the target matrix includes:
caching the continuously input infrared image data through the first-in first-out buffer;
the target matrix is constructed based on the output data of all the first-in first-out buffers and the raw data of the infrared image data.
3. The method of claim 2, wherein the number of first-in first-out buffers is greater than or equal to 4, and the target matrix is at least a 5 x 5 matrix.
4. The method of infrared image correction according to claim 1, wherein after the step of constructing the target matrix, the method further comprises:
constructing a preset replacement matrix according to the position of a bad cluster area in the target matrix, wherein the preset replacement matrix is identical to the central point of the target matrix, and the specification of the preset replacement matrix is smaller than that of the target matrix;
the step of performing preset replacement processing on the center point of the target matrix includes:
and carrying out preset replacement processing on the center point of the target matrix based on the preset replacement matrix.
5. The method of claim 4, wherein the predetermined replacement matrix is at least a 3 x 3 matrix.
6. An infrared image correction system, the system comprising: the infrared detector, the AD conversion chip and the FPGA processor are connected in sequence;
the infrared detector is used for acquiring analog data of an infrared image;
the AD conversion chip is used for converting the analog data into digital data so as to obtain the infrared image data;
the FPGA processor is used for acquiring continuously input infrared image data; identifying a bad cluster region in the infrared image data, wherein the bad cluster region comprises a preset number of bad pixels; constructing a target matrix, wherein the target matrix comprises the bad cluster region; the center point of the target matrix is a bad point of the bad cluster area; after the central point of the current target matrix is subjected to preset replacement processing, the target matrix is moved so that the central point of the target matrix is another bad point of the bad cluster area, and the central point of the moved target matrix is subjected to preset replacement processing until all the bad points complete the preset replacement processing;
the method comprises the following steps of:
determining good point conditions and bad point conditions of all matrix points adjacent to the central point;
calculating pixel replacement values of all dead pixels according to a first correction algorithm;
calculating the pixel replacement value of the center point according to the second correction algorithm, the pixel replacement value of each bad point and the pixel value of each good point;
performing replacement processing on the center point based on the pixel replacement value of the center point;
or, performing preset replacement processing on the center point of the target matrix, wherein the steps include:
determining good point conditions and bad point conditions of all matrix points in the target matrix except the center point;
calculating a first correction value according to the good point condition and the bad point condition;
the target matrix comprises a preset number of matrix points, each matrix point has a corresponding weight, wherein the weight of a center matrix point is highest, and the weight of a matrix point is lower when the matrix point is far from the center matrix point; calculating a second correction value according to the pixel values and weights of all the good points;
and replacing the center point based on a target correction value, wherein the target correction value is obtained by dividing the second correction value by the first correction value.
7. The infrared image correction system of claim 6, wherein the FPGA processor is further configured to construct a preset replacement matrix according to a position of a bad cluster region in the target matrix, wherein the preset replacement matrix is identical to a center point of the target matrix, and a specification of the preset replacement matrix is smaller than a specification of the target matrix;
and carrying out preset replacement processing on the center point of the target matrix based on the preset replacement matrix.
8. An electronic device comprising the infrared image correction system of claim 6 or 7.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103106927A (en) * | 2011-11-15 | 2013-05-15 | 恒景科技股份有限公司 | Dead pixel correction method and device |
| CN105812756A (en) * | 2016-03-16 | 2016-07-27 | 沈阳尚贤微创医疗器械股份有限公司 | Capsule endoscope wireless transmission data loss restoration and compensation method |
| CN112596965A (en) * | 2020-12-14 | 2021-04-02 | 上海集成电路研发中心有限公司 | Digital image bad cluster statistical method and automatic integrated circuit tester |
| CN112911174A (en) * | 2021-01-18 | 2021-06-04 | 珠海全志科技股份有限公司 | Image dead pixel cluster correction method, computer device and computer readable storage medium |
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| CN115526828A (en) * | 2022-07-28 | 2022-12-27 | 西安大医集团股份有限公司 | Image dead pixel cluster detection method, computer device and readable storage medium |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103106927A (en) * | 2011-11-15 | 2013-05-15 | 恒景科技股份有限公司 | Dead pixel correction method and device |
| CN105812756A (en) * | 2016-03-16 | 2016-07-27 | 沈阳尚贤微创医疗器械股份有限公司 | Capsule endoscope wireless transmission data loss restoration and compensation method |
| CN112596965A (en) * | 2020-12-14 | 2021-04-02 | 上海集成电路研发中心有限公司 | Digital image bad cluster statistical method and automatic integrated circuit tester |
| CN112911174A (en) * | 2021-01-18 | 2021-06-04 | 珠海全志科技股份有限公司 | Image dead pixel cluster correction method, computer device and computer readable storage medium |
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