CN113205470A - Infrared medium-short wave double-color fusion method based on hue saturation mapping - Google Patents
Infrared medium-short wave double-color fusion method based on hue saturation mapping Download PDFInfo
- Publication number
- CN113205470A CN113205470A CN202110298194.8A CN202110298194A CN113205470A CN 113205470 A CN113205470 A CN 113205470A CN 202110298194 A CN202110298194 A CN 202110298194A CN 113205470 A CN113205470 A CN 113205470A
- Authority
- CN
- China
- Prior art keywords
- medium
- image
- dif
- short wave
- hue
- 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.)
- Granted
Links
- 238000013507 mapping Methods 0.000 title claims abstract description 48
- 238000007500 overflow downdraw method Methods 0.000 title claims abstract description 17
- 238000000034 method Methods 0.000 claims abstract description 51
- 238000004364 calculation method Methods 0.000 claims description 25
- 230000008569 process Effects 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 230000004927 fusion Effects 0.000 abstract description 14
- 238000004422 calculation algorithm Methods 0.000 abstract description 11
- 230000005855 radiation Effects 0.000 abstract description 8
- 239000003086 colorant Substances 0.000 abstract description 5
- 230000000694 effects Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013506 data mapping Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000004438 eyesight Effects 0.000 description 1
- 229940088597 hormone Drugs 0.000 description 1
- 239000005556 hormone Substances 0.000 description 1
- 238000003331 infrared imaging Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 230000016776 visual perception 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
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- 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
- G06T2207/10024—Color image
-
- 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
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention relates to an infrared medium-short wave two-color fusion method based on hue saturation mapping, and belongs to the technical field of image fusion. The method comprises the following steps: inputting medium wave and short wave infrared images; sending the short wave image into a color space brightness channel; the difference between each pixel gray value of the medium wave image and the mean value of the whole medium wave image is obtained; performing color mapping on the hue saturation color plane of the difference in the color space; and finally, converting the used color space of the fused image into an RGB color space for output. The method of the invention fully reserves the abundant details of the short wave infrared image, and simultaneously displays the radiation information intensity of the medium wave infrared image in abundant colors. The method belongs to the color mapping algorithm, and the algorithm is simple, fast in operation and easy to popularize and use.
Description
Technical Field
The invention belongs to the field of image processing hormones, and relates to an infrared medium-short wave two-color fusion method based on hue saturation mapping.
Background
In the technical field of infrared medium-short wave color fusion, the current methods are mainly divided into four types, namely color mapping, color transfer and color lookup tables and neural network methods, typical color mapping methods comprise NRL, TNO and the like, the methods are simple and easy to implement, and the data mapping process does not have a lifting dimension process, but has color distortion and serious color cast in partial scenes. The color transfer enables the fused image to obtain a color effect close to that of the reference image, but the fused effect is greatly influenced by the reference image, and the loss of detail characteristics is easily caused in the transfer process. The color lookup table has high running speed, colors have certain naturalness and do not need to refer to images, but the color stability is poor in actual video fusion, and the conditions of color change and identification degree reduction are easy to occur along with the time. The effect of the neural network method is greatly influenced by the network framework, the scale quality of training data and the like, and the requirement level on hardware is high in engineering implementation.
The wavelength range of short wave infrared is 0.75-3 mu m, the received radiation information comprises solar radiation and reflection, the imaging effect is similar to that of a visible light image, more texture details are possessed, and the visual effect is more suitable for human eye observation. The wavelength range of medium wave infrared is 3-5 mu m, the medium wave infrared imaging device mainly depends on self radiation imaging of an object, the detection capability is stronger in a damp and hot environment, and various kinds of rich thermal information of a scene can be received.
The HSV color space is a color space widely used internationally, like the RGB color space, in which colors (H channel and S channel) are separated from luminance (V channel) and described in terms of hue and saturation, and a hue saturation color plane composed of the H channel and the S channel is referred to as an H-S color plane. A color space similar to the HSV color space is also the HSL color space, etc.
Disclosure of Invention
The invention aims to solve the defects of the prior art and the existing algorithm exposed in engineering application, and provides a medium-short wave dual-band color image fusion method which simultaneously retains rich details of an infrared short-wave image and heat radiation information of a medium-wave image scene and has extremely low calculation complexity.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the infrared medium-short wave two-color fusion method based on hue saturation mapping comprises the following steps:
step 2, sending the short wave image obtained in the step 1 into a brightness channel;
step 3, calculating the gray average value of the medium wave image obtained in the step 1;
step 4, obtaining a difference value between each pixel value of the medium wave image obtained in the step 1 and the gray level mean value of the medium wave image obtained in the step 3;
step 5, performing color mapping on the hue-saturation plane on the difference value obtained in the step 4;
and 6, converting the brightness channel data obtained in the step 2 and the hue channel and saturation channel data obtained in the step 5 into an RGB color space for output.
Further, it is preferable that the specific method of step 2 is: sending the short wave image obtained in the step 1 into a brightness channel, wherein the short wave image is FSW；
If it is V channel, the brightness channel image is FV，FVThe calculation formula is shown below;
FV＝FSW；
if the L channel is selected, the luminance channel image is FL，FLThe calculation formula is shown below;
FL＝FSW。
further, it is preferable that the specific method of step 3 is: calculating the medium wave obtained in the step 1The mean value of the whole image and the medium wave image are recorded as FMWThe mean value of the medium wave image is denoted as A, and the calculation mode is as follows:
wherein i and j are image pixel coordinate values, m and n are the total number of rows and columns of the whole image, i is greater than or equal to 0 and less than or equal to m, and j is greater than or equal to 0 and less than or equal to n.
Further, it is preferable that the specific method of step 4 is:
the difference between each pixel of the medium wave image obtained in the step 1 and the mean value of the medium wave image obtained in the step 3 is obtained,recording the differenceIs difP(i, j) with a negative difference of difN(i, j), which is calculated as follows:
difP(i，j)＝FMW(i，j)-A，difN(i, j) is 0, if FMW(i，j)＞A；
difN(i，j)＝A-FMW(i，j)，difP(i, j) is 0, if FMW(i，j)＜A；
difP(i，j)＝difN(i, j) is 0, if FMW(i，j)＝A。
Where i and j are image pixel coordinate values.
Further, it is preferable that the specific method of step 5 is: and (4) carrying out color mapping on the difference value obtained in the step (4) in a hue-saturation plane, wherein the calculation process is as follows:
FS(i，j)＝difN(i，j)+difP(i，j)
wherein, FSFor HSV color space saturation channel data, FHIs the hue channel data of HSV color space, whose value is the uniform mapping from hue angle 0-360 degrees to value 0-1.
Further, in step 6, preferably, a color space conversion method of HSV color space or HSL color space is used for conversion.
In step 5, color mapping is performed on the hue-saturation plane in the color space for the difference value between each pixel of the medium wave image and its own mean value, the mapping formula is not limited to the formula in the disclosure of the present invention, and the mapped color space may be any color space including a hue channel and a saturation channel.
The method used by the invention is not limited to the bit width of the image data, and can be used in each data bit width.
Because the sensitivity of human eyes to brightness change is far higher than the sensitivity to color change, detailed information can be fully highlighted in a brightness channel, and scene temperature radiation information accords with the visual perception of human eyes through cold and warm tone display, the core idea of the invention is that short-wave images are mapped to the brightness channel, and medium-wave image heat radiation information is mapped to a color channel. The method of the invention fully reserves the abundant details of the short wave infrared image, and simultaneously displays the radiation information intensity of the medium wave infrared image in abundant colors. The method belongs to the color mapping algorithm, and the algorithm is simple, fast in operation and easy to popularize and use.
Compared with the prior art, the invention has the beneficial effects that:
(1) compared with other non-color mapping type color fusion algorithms, the method has extremely low computational complexity, high realizability and high stability, the algorithm is realized on the FPGA, only 20 pixel clocks are delayed, for example, the pixel clock is 10Mhz, the time is only 0.002ms, and the time sequence simulation diagram and the comprehensive resource occupation of the algorithm after the comprehensive realization in the FPGA are shown in FIG. 4;
(2) compared with other color mapping type color fusion algorithms, the color fusion algorithm has richer colors which are in line with human vision, has no color cast problem, fully reserves rich detail information of short-wave images, and explains two implementation methods, and the effect is shown in fig. 3;
(3) the invention is specially designed for infrared medium-short wave fusion, and most of the current color fusion algorithms lack pertinence or are designed for visible light-infrared fusion or infrared medium-long wave fusion.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph of the medium and short wave images actually acquired by a medium short wave dual band thermal imager, wherein (a) is the medium wave image and (b) is the short wave image;
FIG. 3 illustrates the actual fusion effect of the present invention using two mapping methods in two color spaces; wherein, (a) is a fusion result graph of the application example 1 method, and (b) is a fusion result graph of the application example 2 method;
FIG. 4 is a timing simulation diagram and hardware resource occupation after the present invention is implemented on an FPGA-based hardware platform.
Detailed Description
The present invention will be described in further detail with reference to examples.
It will be appreciated by those skilled in the art that the following examples are illustrative of the invention only and should not be taken as limiting the scope of the invention. The examples do not specify particular techniques or conditions, and are performed according to the techniques or conditions described in the literature in the art or according to the product specifications. The materials or equipment used are not indicated by manufacturers, and all are conventional products available by purchase.
The image acquisition equipment used in the specific implementation mode is a homologous common-optical-axis medium-short wave band thermal infrared imager, so that the two wave band images do not need to be subjected to image fusion preprocessing operations such as registration and the like.
Example 1
The infrared medium-short wave two-color fusion method based on hue saturation mapping comprises the following steps:
step 2, sending the short wave image obtained in the step 1 into a brightness channel;
step 3, calculating the gray average value of the medium wave image obtained in the step 1;
step 4, obtaining a difference value between each pixel value of the medium wave image obtained in the step 1 and the gray level mean value of the medium wave image obtained in the step 3;
step 5, performing color mapping on the hue-saturation plane on the difference value obtained in the step 4;
and 6, converting the brightness channel data obtained in the step 2 and the hue channel and saturation channel data obtained in the step 5 into RGB color space for output.
Example 2
The infrared medium-short wave two-color fusion method based on hue saturation mapping comprises the following steps:
step 2, sending the short wave image obtained in the step 1 into a brightness channel;
step 3, calculating the gray average value of the medium wave image obtained in the step 1;
step 4, obtaining a difference value between each pixel value of the medium wave image obtained in the step 1 and the gray level mean value of the medium wave image obtained in the step 3;
step 5, performing color mapping on the hue-saturation plane on the difference value obtained in the step 4;
and 6, converting the brightness channel data obtained in the step 2 and the hue channel and saturation channel data obtained in the step 5 into an RGB color space for output.
The specific method of the step 2 comprises the following steps: sending the short wave image obtained in the step 1 into a brightness channel, wherein the short wave image is FSW；
If it is V channel, the brightness channel image is FV，FVThe calculation formula is shown below;
FV＝FSW。
the specific method of the step 3 comprises the following steps: calculating the mean value of the whole medium wave image obtained in the step 1, and recording the medium wave image as FMWThe mean value of the medium wave image is denoted as A, and the calculation mode is as follows:
wherein i and j are image pixel coordinate values, m and n are the total number of rows and columns of the whole image, i is greater than or equal to 0 and less than or equal to m, and j is greater than or equal to 0 and less than or equal to n.
The specific method of the step 4 comprises the following steps:
the difference between each pixel of the medium wave image obtained in the step 1 and the mean value of the medium wave image obtained in the step 3 is obtained,recording the differenceIs difP(i, j) with a negative difference of difN(i, j), which is calculated as follows:
difP(i，j)＝FMW(i，j)-A，difN(i, j) is 0, if FMW(i，j)＞A；
difN(i，j)＝A-FMW(i，j)，difP(i, j) is 0, if FMW(i，j)＜A；
difP(i，j)＝difN(i, j) is 0, if FMW(i，j)＝A。
Where i and j are image pixel coordinate values.
The specific method of the step 5 comprises the following steps: and (4) carrying out color mapping on the difference value obtained in the step (4) in a hue-saturation plane, wherein the calculation process is as follows:
FS(i，j)＝difN(i，j)+difP(i，j)
wherein, FSFor HSV color space saturation channel data, FHIs the hue channel data of HSV color space, whose value is the uniform mapping from hue angle 0-360 degrees to value 0-1.
In step 6, a color space conversion method of HSV color space is adopted for conversion.
Example 3
The infrared medium-short wave two-color fusion method based on hue saturation mapping comprises the following steps:
step 2, sending the short wave image obtained in the step 1 into a brightness channel;
step 3, calculating the gray average value of the medium wave image obtained in the step 1;
step 4, obtaining a difference value between each pixel value of the medium wave image obtained in the step 1 and the gray level mean value of the medium wave image obtained in the step 3;
step 5, performing color mapping on the hue-saturation plane on the difference value obtained in the step 4;
and 6, converting the brightness channel data obtained in the step 2 and the hue channel and saturation channel data obtained in the step 5 into an RGB color space for output.
The specific method of the step 2 comprises the following steps: sending the short wave image obtained in the step 1 into a brightness channel, wherein the short wave image is FSW；
If the L channel is selected, the luminance channel image is FL，FLThe calculation formula is shown below;
FL＝FSW。
the specific method of the step 3 comprises the following steps: calculating the mean value of the whole medium wave image obtained in the step 1, and recording the medium wave image as FMWThe mean value of the medium wave image is denoted as A, and the calculation mode is as follows:
wherein i and j are image pixel coordinate values, m and n are the total number of rows and columns of the whole image, i is greater than or equal to 0 and less than or equal to m, and j is greater than or equal to 0 and less than or equal to n.
The specific method of the step 4 comprises the following steps:
the difference between each pixel of the medium wave image obtained in the step 1 and the mean value of the medium wave image obtained in the step 3 is obtained,recording the differenceIs difP(i, j) with a negative difference of difN(i, j), which is calculated as follows:
difP(i，j)＝FMW(i，j)-A，difN(i, j) is 0, if FMW(i，j)＞A；
difN(i，j)＝A-FMW(i，j)，difP(i, j) is 0, if FMW(i，j)＜A；
difP(i，j)＝difN(i, j) is 0, if FMW(i，j)＝A。
Where i and j are image pixel coordinate values.
The specific method of the step 5 comprises the following steps: and (4) carrying out color mapping on the difference value obtained in the step (4) in a hue-saturation plane, wherein the calculation process is as follows:
FS(i，j)＝difN(i，j)+difP(i，j)
wherein, FSFor HSV color space saturation channel data, FHIs the hue channel data of HSV color space, whose value is the uniform mapping from hue angle 0-360 degrees to value 0-1.
In step 6, a color space conversion method of an HSL color space is adopted for conversion.
Examples of the applications
FIG. 1 is a technical flow diagram of the present invention; fig. 2(a) and 2(b) are medium wave and short wave images actually acquired by a medium-short wave dual-band thermal imager, respectively, and the two images are used for explaining the embodiment of the invention, and the sizes of the two images are both 320 × 256.
Application example 1
With reference to fig. 1, the infrared medium-short wave two-color fusion method based on hue saturation mapping includes the following steps:
step 2, sending the short wave image obtained in the step 1 into a V channel, wherein the short wave image is FSWAnd the V channel image is FV，FVThe calculation formula is shown below;
FV＝FSW
step 3, calculating the mean value of the whole medium wave image obtained in the step 1, and recording the medium wave image as FMWIn, in
The mean wave image value is denoted as a and is calculated as follows:
wherein i and j are image pixel coordinate values, m is 320, n is 256, i is greater than or equal to 0 and less than or equal to m, and j is greater than or equal to 0 and less than or equal to n;
step 4, calculating the difference between each pixel of the medium wave image obtained in the step 1 and the mean value of the medium wave image obtained in the step 3,note the book Positive differenceIs difPWith a negative difference of difNThe calculation method is as follows:
difP(i，j)＝FMW(i，j)-A，difN(i，j)＝0(FMW(i，j)＞A)
difN(i，j)＝A-FMW(i，j)，difP(i，j)＝0(FMW(i，j)＜A)
difP(i，j)＝difN(i，j)＝0(FMW(i，j)＝A)
wherein i and j are image pixel coordinate values, m is 320, n is 256, i is greater than or equal to 0 and less than or equal to m, and j is greater than or equal to 0 and less than or equal to n;
and 5, performing spiral line mapping on the positive and negative difference characteristics of the medium wave image obtained in the step 4 on an H-S plane, mapping the negative difference to a half shaft of 90-270 degrees, mapping the positive difference to an opposite half shaft, and performing the following calculation process:
FS(i，j)＝difN(i，j)+difP(i，j)
wherein, FSFor an S channel image of HSV color space, FHIs HSV colorThe value of the color space H channel image is the uniform mapping from the spiral angle (0-360 degrees) to the numerical value (0-1).
And 6, converting the V channel data obtained in the step 3 and the H and S channel data obtained in the step 5 into RGB color space data for output. The calculation process is as follows:
p(i，j)＝FV(i，j)×(1-FS(i，j))
q(i，j)＝FV(i，j)×(1-f(i，j)×FS(i，j))
t(i，j)＝FV(i，j)×(1-(1-f(i，j))×FS(i，j))
wherein, FR、FGAnd FBI is greater than or equal to 1 and less than or equal to 320, j is greater than or equal to 1 and less than or equal to 256, and the final output fused image is as shown in fig. 3 (a).
Application example 2
The real-time mode of the invention is not limited to HSV color space, and the color mapping equation is not limited to the above formula, the same fused image is input, another method in HSL space can be adopted, and the infrared medium and short wave two-color fusion method based on hue saturation mapping is combined with the graph 1, and comprises the following steps:
step 2, sending the short wave image obtained in the step 1 into an L channel, wherein the short wave image is FSWL channelThe image is FL，FLThe calculation formula is shown below;
FL(i，j)＝FSW(i，j)
wherein i and j are image pixel coordinate values, i is more than or equal to 0 and less than or equal to 320, and j is more than or equal to 0 and less than or equal to 256;
step 3, calculating the mean value of the whole medium wave image obtained in the step 1, and recording the medium wave image as FMWThe mean value of the medium wave image is denoted as A, and the calculation mode is as follows:
wherein i and j are image pixel coordinate values, m is 320, n is 256, i is greater than or equal to 0 and less than or equal to m, and j is greater than or equal to 0 and less than or equal to n;
step 4, calculating the difference between each pixel of the medium wave image obtained in the step 1 and the mean value of the medium wave image obtained in the step 3,note the book Positive differenceIs difPWith a negative difference of difNThe calculation method is as follows:
difP(i，j)＝FMW(i，j)-A，difN(i，j)＝0(FMW(i，j)＞A)
difN(i，j)＝A-FMW(i，j)，difP(i，j)＝0(FMW(i，j)＜A)
difP(i，j)＝difN(i，j)＝0(FMW(i，j)＝A)
wherein i and j are image pixel coordinate values, m is 320, n is 256, i is greater than or equal to 0 and less than or equal to m, and j is greater than or equal to 0 and less than or equal to n;
and 5, performing spiral line mapping on the positive and negative difference characteristics of the medium wave image obtained in the step 4 on an H-S plane, mapping the negative difference to a half shaft of 90-270 degrees, mapping the positive difference to an opposite half shaft, and performing the following calculation process:
FS(i，j)＝difN(i，j)+difP(i，j)
wherein, FSFor an S channel image of HSV color space, FHIs an H-channel image in HSV color space, whose value is a uniform mapping of helix angle (0-360 degrees) to numerical value (0-1).
And 6, converting the L channel data obtained in the step 2 and the H and S channel data obtained in the step 5 into RGB color space data for output. The calculation process is as follows:
p(i，j)＝2×FL(i，j)-q(i，j)
tG(i，j)＝FH(i，j)
ift(i，j)＜0，ts(i，j)＝t(i，j)+1
ift(i，j)＞1，ts(i，j)＝t(i，j)-1
else，ts(i，j)＝t(i，j)
wherein, FR(i，j)、FG(i, j) and FB(i, j) are R channel image, G channel image and B channel image of RGB color space respectively, i is more than or equal to 1 and less than or equal to 384, j is more than or equal to 1 and less than or equal to 288, and the calculation modes of t (i, j) value in RGB three channels respectively correspond to tR(i，j)，tG(i，j)，tB(i, j), the final output fused image is shown in FIG. 3.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The infrared medium-short wave two-color fusion method based on hue saturation mapping is characterized by comprising the following steps of:
step 1, reading images to be fused, respectively reading in medium wave images and short wave images, and sending the medium wave images and the short wave images into a color space containing hue and saturation channels to obtain the short wave images and the medium wave images;
step 2, sending the short wave image obtained in the step 1 into a brightness channel;
step 3, calculating the gray average value of the medium wave image obtained in the step 1;
step 4, obtaining a difference value between each pixel value of the medium wave image obtained in the step 1 and the gray level mean value of the medium wave image obtained in the step 3;
step 5, performing color mapping on the hue-saturation plane on the difference value obtained in the step 4;
and 6, converting the brightness channel data obtained in the step 2 and the hue channel and saturation channel data obtained in the step 5 into an RGB color space for output.
2. The infrared medium-short wave two-color fusion method based on hue saturation mapping according to claim 1, characterized in that the specific method of step 2 is: sending the short wave image obtained in the step 1 into a brightness channel, wherein the short wave image is FSW；
If it is V channel, the brightness channel image is FV，FVThe calculation formula is shown below;
FV＝FSW；
if the L channel is selected, the luminance channel image is FL，FLThe calculation formula is shown below;
FL＝FSW。
3. the infrared medium-short wave two-color fusion method based on hue saturation mapping according to claim 1 or 2, characterized in that the specific method of step 3 is: calculating the mean value of the whole medium wave image obtained in the step 1, and recording the medium wave image as FMWThe mean value of the medium wave image is denoted as A, and the calculation mode is as follows:
wherein i and j are image pixel coordinate values, m and n are the total number of rows and columns of the whole image, i is greater than or equal to 0 and less than or equal to m, and j is greater than or equal to 0 and less than or equal to n.
4. The infrared medium-short wave two-color fusion method based on hue saturation mapping according to claim 3, characterized in that the specific method of step 4 is:
the difference between each pixel of the medium wave image obtained in the step 1 and the mean value of the medium wave image obtained in the step 3 is obtained,recording the differenceIs difP(i, j) with a negative difference of difN(i, j), which is calculated as follows:
difp(i，j)＝FMW(i，j)-A，difN(i, j) is 0, if FMW(i，j)＞A；difN(i，j)＝A-FMW(i，j)，difp(i, j) is 0, if FMW(i，j)＜A；
difp(i，j)＝difN(i, j) is 0, if FMW(i，j)＝A。
Where i and j are image pixel coordinate values.
5. The infrared medium-short wave two-color fusion method based on hue saturation mapping according to claim 4, characterized in that the specific method of step 5 is: and (4) carrying out color mapping on the difference value obtained in the step (4) in a hue-saturation plane, wherein the calculation process is as follows:
FS(i，j)＝difN(i，j)+difp(i，j)
wherein, FSFor HSV color space saturation channel data, FHIs the hue channel data of HSV color space, whose value is the uniform mapping from hue angle 0-360 degrees to value 0-1.
6. The infrared medium short wave two-color fusion method based on hue saturation mapping according to claim 1 or 5, characterized in that in step 6, the conversion is performed by using a color space conversion method of HSV color space or HSL color space.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110298194.8A CN113205470B (en) | 2021-03-19 | 2021-03-19 | Infrared medium-short wave double-color fusion method based on hue saturation mapping |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110298194.8A CN113205470B (en) | 2021-03-19 | 2021-03-19 | Infrared medium-short wave double-color fusion method based on hue saturation mapping |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113205470A true CN113205470A (en) | 2021-08-03 |
| CN113205470B CN113205470B (en) | 2022-08-30 |
Family
ID=77025618
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110298194.8A Active CN113205470B (en) | 2021-03-19 | 2021-03-19 | Infrared medium-short wave double-color fusion method based on hue saturation mapping |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113205470B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102609927A (en) * | 2012-01-12 | 2012-07-25 | 北京理工大学 | Foggy visible light/infrared image color fusion method based on scene depth |
| CN111062378A (en) * | 2019-12-23 | 2020-04-24 | 重庆紫光华山智安科技有限公司 | Image processing method, model training method, target detection method and related device |
| CN111161308A (en) * | 2019-12-19 | 2020-05-15 | 上海航天控制技术研究所 | Dual-band fusion target extraction method based on key point matching |
| CN111695407A (en) * | 2020-04-23 | 2020-09-22 | 西安电子科技大学 | Gender identification method, system, storage medium and terminal based on multispectral fusion |
| CN111815548A (en) * | 2020-07-07 | 2020-10-23 | 昆明物理研究所 | Medium-long wave dual-waveband infrared image fusion method |
| US20200357147A1 (en) * | 2015-10-09 | 2020-11-12 | Zhejiang Dahua Technology Co., Ltd. | Methods and systems for fusion display of thermal infrared and visible image |
| CN112233024A (en) * | 2020-09-27 | 2021-01-15 | 昆明物理研究所 | Medium-long wave dual-waveband infrared image fusion method based on difference characteristic color mapping |
-
2021
- 2021-03-19 CN CN202110298194.8A patent/CN113205470B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102609927A (en) * | 2012-01-12 | 2012-07-25 | 北京理工大学 | Foggy visible light/infrared image color fusion method based on scene depth |
| US20200357147A1 (en) * | 2015-10-09 | 2020-11-12 | Zhejiang Dahua Technology Co., Ltd. | Methods and systems for fusion display of thermal infrared and visible image |
| CN111161308A (en) * | 2019-12-19 | 2020-05-15 | 上海航天控制技术研究所 | Dual-band fusion target extraction method based on key point matching |
| CN111062378A (en) * | 2019-12-23 | 2020-04-24 | 重庆紫光华山智安科技有限公司 | Image processing method, model training method, target detection method and related device |
| CN111695407A (en) * | 2020-04-23 | 2020-09-22 | 西安电子科技大学 | Gender identification method, system, storage medium and terminal based on multispectral fusion |
| CN111815548A (en) * | 2020-07-07 | 2020-10-23 | 昆明物理研究所 | Medium-long wave dual-waveband infrared image fusion method |
| CN112233024A (en) * | 2020-09-27 | 2021-01-15 | 昆明物理研究所 | Medium-long wave dual-waveband infrared image fusion method based on difference characteristic color mapping |
Non-Patent Citations (1)
| Title |
|---|
| 张润琦: "弱环境辐射下中短波双波段红外图像融合方法", 《红外技术》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113205470B (en) | 2022-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104200470B (en) | Blue screen image-matting method | |
| CN105409211B (en) | For the automatic white balance positive with skin-color adjustment of image procossing | |
| CN109447922B (en) | Improved IHS (induction heating system) transformation remote sensing image fusion method and system | |
| CN103854261B (en) | The bearing calibration of colour cast image | |
| CN109754375B (en) | Image processing method, system, computer device, storage medium and terminal | |
| CN108230407B (en) | Image processing method and device | |
| CN103440674A (en) | Method for rapidly generating crayon special effect of digital image | |
| CN105427268B (en) | A kind of middle long wave dual-band infrared image feature level Color Fusion | |
| CN113132696B (en) | Image tone mapping method, image tone mapping device, electronic equipment and storage medium | |
| CN104410786B (en) | The control method of image processing equipment and image processing equipment | |
| CN106507130A (en) | Method and the watermark stacking apparatus of watermark superpose on yuv frame | |
| CN110211077A (en) | A kind of more exposure image fusion methods based on Higher-order Singular value decomposition | |
| CN108711160B (en) | Target segmentation method based on HSI (high speed input/output) enhanced model | |
| CN103559712B (en) | Black melon seed color selection method | |
| CN111815548B (en) | Medium-long wave double-band infrared image fusion method | |
| CN112233024B (en) | Medium-long wave double-band infrared image fusion method based on difference characteristic color mapping | |
| CN109102457B (en) | Intelligent color changing system and method based on convolutional neural network | |
| CN113205470B (en) | Infrared medium-short wave double-color fusion method based on hue saturation mapping | |
| Kumar et al. | A Robust Approach for Image Super-Resolution using Modified Very Deep Convolution Networks | |
| CN111105383B (en) | Three-color vision-oriented image fusion color enhancement method | |
| CN104732507A (en) | Fusion method for two images of different illuminations based on texture information reconstruction | |
| CN108965843B (en) | Method for extracting wide color gamut data by spectrum segmentation determination matrix coefficient method | |
| CN114627016A (en) | Industrial defect detection preprocessing method based on color migration strategy | |
| CN107316040A (en) | A kind of color of image spatial transform method of illumination invariant | |
| CN203219431U (en) | Image processing module |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |