CN113902659A - Infrared and visible light fusion method based on significant target enhancement - Google Patents
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Abstract
The invention belongs to the field of image processing and computer vision, and relates to an infrared and visible light fusion method based on significant target enhancement. The system is easy to construct, and the acquisition of input data can be completed by respectively using a three-dimensional binocular infrared camera and a visible light camera; the program is simple and easy to realize; by utilizing different principles of infrared and visible light camera imaging, an input image is decomposed into a background layer and a detail layer through filtering decomposition, a salient pixel-based enhanced fusion method is arranged for the background layer, an image gradient-based enhanced fusion algorithm is designed for the detail layer, the algorithm effectively enhances the quality of a fusion image and effectively retains the salient information of a bilateral image, and finally the real-time effect is achieved through GPU acceleration.
Description
Technical Field
The invention belongs to the field of image processing and computer vision, and relates to an infrared and visible light fusion method based on significant target enhancement.
Background
The binocular stereoscopic vision technology based on the visible light wave band is developed more and more mature, and the imaging of the visible light camera has rich color information and detail texture, so that the stereoscopic matching information between binocular images can be quickly and accurately obtained, and more accurate scene depth information can be obtained. However, the visible light band imaging has the defects that the imaging quality is greatly reduced and the matching precision is greatly reduced under the conditions of insufficient illumination conditions, rainstorm, heavy fog and the like. Therefore, the establishment of a color image fusion system by utilizing the complementarity of different wave band information sources is an effective way for realizing more credible image perception under the extreme environment. If a multiband stereoscopic vision system is formed by the visible light band binocular camera and the infrared band binocular camera, the advantage that infrared imaging is not affected by fog, rain, snow and illumination is utilized, the imaging deficiency of the visible light band is made up, and therefore more complete and accurate fusion information is obtained.
The multi-modal image fusion technology is an image processing framework which is high in reliability and friendly to vision and is obtained by utilizing the advantages of different sensors and adopting a specific algorithm or rule for fusion. Compared with the homomorphic fusion image unicity, the multimode image fusion can acquire more image information, and gradually becomes an indispensable method for solving forest fire alarm monitoring, unmanned driving, military monitoring and lunar exploration. The method aims to utilize the imaging difference and complementarity of sensors in different modes to extract image information of each mode to the maximum extent, and use source images in different modes to fuse a synthetic image with rich information and high fidelity. Multi-modal image fusion will therefore yield a more comprehensive understanding of the images and a more accurate localization. In recent years, most of fusion methods are based on transform domain research and design, and do not consider multi-scale detail information of images, which results in detail loss in fused images, such as an infrared and visible light fusion system and an image fusion device disclosed in patent publication CN208240087U [ chinese ]. Therefore, the infrared and visible light image optimization method carries out optimization solution after mathematical modeling is carried out on the infrared and visible light image, and realizes detail enhancement and artifact removal on the basis of keeping effective information of the infrared and visible light image.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a real-time multi-modal image fusion algorithm based on significant background and target enhancement. The method comprises the steps of performing filtering decomposition on infrared and visible light images to obtain a background layer and a detail layer, performing fusion of significant pixel distribution enhancement on the background layer, performing fusion of target gradient enhancement on the detail layer, and finally realizing real-time multi-modal image fusion through a GPU.
The specific technical scheme of the invention comprises the following steps:
an infrared and visible light fusion method based on significant target enhancement comprises the following steps:
the first step is as follows: acquiring registered infrared and visible light images:
1-1) respectively calibrating each lens and the respective system of the visible light binocular camera and the infrared binocular camera;
1-2) respectively calibrating each infrared camera and each visible light camera by using a Zhangyingyou calibration method to obtain internal parameters such as a focal length and a principal point position of each camera and external parameters such as rotation and translation;
1-3) calculating the position relation of the same plane in the visible light image and the infrared image by using the RT obtained by combined calibration and the detected checkerboard angular points and performing registration from the visible light image to the infrared image by using a homography matrix.
The second step is that: converting a color space of a visible light image, converting an RGB image into an HSV image, extracting lightness information of a color image as input of image fusion, and keeping original hue and saturation of the color image;
2-1) aiming at the problem that the visible light image is RGB three-channel, converting the RGB color space into HSV color space, extracting V (brightness) information of the visible light image, fusing the V (brightness) information with the infrared image, and reserving H (hue) and S (saturation) of the visible light image, wherein the specific conversion is as follows:
R′＝R/255G′＝G/255B′＝B/255
Cmax＝max(R′,G′,B′)
Cmin＝min(R′，G′，B′)
Δ＝Cmax-Cmin
V＝Cmax
2-2) extracting a V (brightness) channel as the input of visible light, reserving H (hue), and reserving color information for the color restoration after the fusion from S (saturation) to the corresponding matrix.
The third step: carrying out mutual-guiding filtering decomposition on an input infrared image and a visible light image subjected to color space conversion, and respectively decomposing the image into a background layer and a detail layer, wherein structural information of the image is described on the background layer, and gradient and texture information is described on the detail layer;
B＝M(I,V),D＝(I,V)-B
wherein B represents a background layer, D represents a detail layer, and M represents a mutual-direction filtering;
the fourth step: a method based on a saliency map is designed to fuse a background layer, differences are made between each pixel point and all the pixel points of the whole world, absolute values are taken, and then accumulation is carried out, wherein the formula is as follows:
S(p)＝|I(p)-I1|+|I(p)-I2|+|I(p)-I3|+…+|I(p)-I(N)|
wherein S (p) represents the significant value of the pixel point, N represents the sum of the pixel values in the image, M represents the histogram statistical formula, and I represents the pixel point in the image;
based on the saliency value s (p), the saliency value of each pixel can be obtained, and the saliency value update calculation formula is as follows:
wherein, the calculation parameters of the D generation color distance can obtain the weight of the saliency map based on the background layer fusion according to the obtained saliency values:
wherein W represents weight, Sj represents corresponding pixel value, then the decomposed infrared image and visible light image are fused based on linear weighting of the weight of the saliency map, and the following formula is calculated:
B＝0.5*(0.5+I*(W1-W2)*0.5)+0.5*(0.5+V*(W2-W1)*0.5)
wherein I, V represent the input infrared image and visible light image, respectively, and W1, W2 represent the significant weights taken on the infrared image and visible light image, respectively;
the fifth step: then, carrying out a pixel fusion strategy of gradient enhancement on a detail layer obtained after object differentiation, designing a gradient enhancement algorithm, and inputting detail images phi I and phi V of an infrared image and a visible light image to realize gradient enhancement; e is the gradient enhancement operator, i and j are the locations of the pixels; max is the operator taking the maximum;
E(ΦI)＝max(max(ΦI(i+1,j+1),ΦI(i,j)))
E(ΦV)＝max(max(ΦV(i+1,j+1),ΦV(i,j)))
the fusion result D of the layers of detail can be expressed as:
D＝E(ΦI)+E(ΦV)
and a sixth step: and finally, linearly weighting the background layer and the detail layer to obtain:
f ═ B + D where F represents the fusion result, B and D represent the background layer fusion result and the detail layer fusion result;
the seventh step: updating the fused image by storing (lightness V) information, and combining the reserved (hue H) and (saturation S) to restore HSV to RGB color space;
the specific formula is as follows:
C＝V×S
X＝C×(1-|(H/60°)mod2-1|)
m＝V-C
R′,G′,B′＝((R′+m)×255，(G′+m)×255，(B′+m)×255)
wherein C is the result of lightness and saturation, and m is the difference between lightness and C.
Eighth step: performing color correction and enhancement on the restored image to generate a three-channel picture which accords with observation and detection; and respectively carrying out color enhancement on the R channel, the G channel and the B channel.
In the eighth step, color enhancement is respectively performed on the R channel, the G channel, and the B channel, as shown in the following formula:
Rout＝(Rin)1/gamma
Rdisplay＝(Rin (1/gamma))gamma
Gout＝(Gin)1/gamma
G＝(Gin (1/gamma))gamma
Bout＝(Bin)1/gamma
Bdisplay＝(Bin (1/gamma))gamma。
the invention has the beneficial effects that:
the invention designs a method for fusing infrared binocular stereo cameras and visible light binocular stereo cameras in real time. The image is decomposed into a background layer and a detail layer by using a filter decomposition strategy, different target-oriented fusion side rates are respectively carried out on the background layer and the detail layer, bilateral information is effectively obtained, and the pixel distribution of the whole target and the image is enhanced, and the method has the following characteristics:
(1) the system is easy to construct, and the acquisition of input data can be completed by using a stereo binocular camera;
(2) the program is simple and easy to realize;
(3) the image is decomposed by filtering, so that the fusion of different target directions is realized;
(4) the structure is completed, multi-thread acceleration and operation can be carried out, and the program has robustness;
(5) and realizing the significant target enhancement of the fused image through the significant enhancement and the image gradient enhancement.
Drawings
Fig. 1 is a flow chart of a visible light and infrared fusion algorithm.
Fig. 2 is a graph showing the results of decomposition of visible light and infrared light into different layers.
Fig. 3 is the result of the fusion of the background and detail layers of visible and infrared light.
Fig. 4 is a final fused image.
Detailed Description
The invention provides a method for real-time image fusion by using an infrared camera and a visible light camera, which is described in detail by combining the accompanying drawings and an embodiment as follows:
the binocular stereo camera is placed on a fixed platform, the image resolution of the experimental camera is 780 multiplied by 340, the field angle is 45.4 degrees, and NVIDIATX2 is used for calculation in order to guarantee real-time performance. On the basis, a real-time infrared and visible light fusion method is designed, and the method comprises the following steps:
1) acquiring registered infrared and visible light images:
1-1) respectively calibrating each lens and the respective system of the visible light binocular camera and the infrared binocular camera;
1-2) calibrating each infrared camera and each visible light camera respectively by using a Zhang Zhengyou calibration method to obtain internal parameters such as focal length and principal point position of each camera and external parameters such as rotation and translation.
1-3) calculating the position relation of the same plane in the visible light image and the infrared image by using the RT obtained by combined calibration and the detected checkerboard angular points and performing registration from the visible light image to the infrared image by using a homography matrix.
2) Image color space conversion
2-1) aiming at the problem that the visible light image is RGB three-channel, converting the RGB color space into HSV color space, extracting V (brightness) information of the visible light image, fusing the V (brightness) information with the infrared image, and reserving H (hue) and S (saturation) of the visible light image, wherein the specific conversion is as follows:
R′＝R/255G′＝G/255B′＝B/255
Cmax＝max(R′,G′,B′)
Cmin＝min(R′，G′，B′)
Δ＝Cmax-Cmin
V＝Cmax
2-2) extracting a V (brightness) channel as the input of visible light, reserving H (hue), and reserving color information for the color restoration after the fusion from S (saturation) to the corresponding matrix.
3) The method comprises the steps of carrying out mutual guiding filtering decomposition on an input infrared image and a visible light image subjected to color space conversion, and decomposing the image into a background layer and a detail layer respectively, wherein structural information of the image is described on the background layer, and gradient and texture information are described on the detail layer.
B＝M(I,V),D＝(I,V)-B
Where B represents the background layer, D represents the detail layer, and M represents the mutual-steering filtering.
4) A method based on a saliency map is designed to fuse a background layer, differences are made between each pixel point and all the pixel points of the whole world, absolute values are taken, and then accumulation is carried out, wherein the formula is as follows:
S(p)＝|I(p)-I1|+|I(p)-I2|+|I(p)-I3|+…+|I(p)-I(N)|
wherein S (p) represents the significant value of the pixel point, N represents the sum of the pixel values in the image, M represents the histogram statistical formula, and I represents the pixel point in the image.
Based on the saliency value s (p), the saliency value of each pixel can be obtained, and the saliency value update calculation formula is as follows:
wherein, the calculation parameter of the D generation color distance can obtain the weight of the saliency map based on the background layer fusion according to the obtained saliency value:
wherein W represents weight, Sj represents corresponding pixel value, then the decomposed infrared image and visible light image are fused based on linear weighting of the weight of the saliency map, and the following formula is calculated:
B＝0.5*(0.5+I*(W1-W2)*0.5)+0.5*(0.5+V*(W2-W1)*0.5)
where I, V represent the input infrared image and visible light image, respectively, and W1, W2 represent the significant weights taken on the infrared image and visible light image, respectively.
5) And then, carrying out a gradient enhancement pixel fusion strategy on a detail layer obtained after the object difference, designing a gradient enhancement algorithm, and inputting detail images phi I and phi V of the infrared image and the visible light image to realize gradient enhancement. E is the gradient enhancement operator and i and j are the locations of the pixels. Max is the operator that takes the maximum.
E(ΦI)＝max(max(ΦI(i+1，j+1)，ΦI(i，j)))
E(ΦV)＝max(max(ΦV(i+1，j+1)，ΦV(i，j)))
The fusion result D of the layers of detail can be expressed as:
D＝E(ΦI)+E(ΦV)
6) and finally, linearly weighting the background layer and the detail layer to obtain:
F＝B+D
where F represents the fusion result, and B and D represent the background layer fusion result and the detail layer fusion result.
7-1) updating by storing the fused image into (lightness V) information, and combining the previously retained (hue H) and (saturation S) to perform HSV-to-RGB color space restoration. The specific formula is as follows:
C＝V×S
X＝C×(1-|(H/60°)mod2-1|)
m＝V-C
R′,G′,B′＝((R′+m)×255，(G′+m)×255，(B′+m)×255)
wherein C is the result of lightness and saturation, and m is the difference between lightness and C.
7-2) carrying out color correction and enhancement on the restored image obtained in the step 7-1 to generate a three-channel picture which accords with observation and detection; respectively carrying out color enhancement on an R channel, a G channel and a B channel, wherein the color enhancement is specifically shown by the following formula:
Rout＝(Rin)1/gamma
Rdisplay＝(Rin (1/gamma))gamma
Gout＝(Gin)1/gamma
G＝(Gin (1/gamma))gamma
Bout＝(Bin)1/gamma
Bdisplay＝(Bin (1/gamma))gamma。
Claims (7)
1. an infrared and visible light fusion method based on significant target enhancement is characterized by comprising the following steps:
the first step is as follows: acquiring registered infrared and visible light images:
the second step is that: converting a color space of a visible light image, converting an RGB image into an HSV image, extracting lightness information of a color image as input of image fusion, and keeping original hue and saturation of the color image;
the third step: carrying out mutual-guiding filtering decomposition on an input infrared image and a visible light image subjected to color space conversion, and respectively decomposing the image into a background layer and a detail layer, wherein structural information of the image is described on the background layer, and gradient and texture information is described on the detail layer;
B＝M(I，V)，D＝(I，V)-B
wherein B represents a background layer, D represents a detail layer, and M represents a mutual-direction filtering;
the fourth step: a method based on a saliency map is designed to fuse a background layer, differences are made between each pixel point and all the pixel points of the whole world, absolute values are taken, and then accumulation is carried out, wherein the formula is as follows:
S(p)＝|I(p)-I1|+|I(p)-I2|+|I(p)-I3|+…+|I(p)-I(N)|
wherein S (p) represents the significant value of the pixel point, N represents the sum of the pixel values in the image, M represents the histogram statistical formula, and I represents the pixel point in the image;
based on the saliency value s (p), the saliency value of each pixel can be obtained, and the saliency value update calculation formula is as follows:
wherein, the calculation parameters of the D generation color distance can obtain the weight of the saliency map based on the background layer fusion according to the obtained saliency values:
wherein W represents weight, Sj represents corresponding pixel value, then the decomposed infrared image and visible light image are fused based on linear weighting of the weight of the saliency map, and the following formula is calculated:
B＝0.5*(0.5+I*(W1-W2)*0.5)+0.5*(0.5+V*(W2-W1)*0.5)
wherein I, V represent the input infrared image and visible light image, respectively, and W1, W2 represent the significant weights taken on the infrared image and visible light image, respectively;
the fifth step: then, carrying out a pixel fusion strategy of gradient enhancement on a detail layer obtained after object differentiation, designing a gradient enhancement algorithm, and inputting detail images phi I and phi V of an infrared image and a visible light image to realize gradient enhancement; e is the gradient enhancement operator, i and j are the locations of the pixels; max is the operator taking the maximum;
E(ΦI)＝max(max(ΦI(i+1，j+1)，ΦI(i，j)))
E(ΦV)＝max(max(ΦV(i+1，j+1)，ΦV(i，j)))
the fusion result D of the layers of detail can be expressed as:
D＝E(ΦI)+E(ΦV)
and a sixth step: and finally, linearly weighting the background layer and the detail layer to obtain:
f ═ B + D where F represents the fusion result, B and D represent the background layer fusion result and the detail layer fusion result;
the seventh step: updating the fused image by storing (lightness V) information, and combining the reserved (hue H) and (saturation S) to restore HSV to RGB color space;
eighth step: performing color correction and enhancement on the restored image to generate a three-channel picture which accords with observation and detection; and respectively carrying out color enhancement on the R channel, the G channel and the B channel.
2. The infrared and visible light fusion method based on significant target enhancement as claimed in claim 1, wherein said first step is specifically operated as follows:
1-1) respectively calibrating each lens and the respective system of the visible light binocular camera and the infrared binocular camera;
1-2) respectively calibrating each infrared camera and each visible light camera by using a Zhangyingyou calibration method to obtain internal parameters such as a focal length and a principal point position of each camera and external parameters such as rotation and translation;
1-3) calculating the position relation of the same plane in the visible light image and the infrared image by using the RT obtained by combined calibration and the detected checkerboard angular points and performing registration from the visible light image to the infrared image by using a homography matrix.
3. A method for infrared and visible light fusion based on significant object enhancement as claimed in claim 1 or 2, wherein the second step is specifically operated as follows:
2-1) aiming at the problem that the visible light image is RGB three-channel, converting the RGB color space into HSV color space, extracting V (brightness) information of the visible light image, fusing the V (brightness) information with the infrared image, and reserving H (hue) and S (saturation) of the visible light image, wherein the specific conversion is as follows:
R′＝R/255G′＝G/255B′＝B/255
Cmax＝max(R′，G′，B′)
Cmin＝min(R′，G′，B′)
Δ＝Cmax-Cmin
V＝Cmax
2-2) extracting a V (brightness) channel as the input of visible light, reserving H (hue), and reserving color information from S (saturation) to a corresponding matrix for the color restoration after the fusion.
4. The infrared and visible light fusion method based on significant target enhancement as claimed in claim 1 or 2, wherein the seventh step is specifically operated as follows:
the specific formula is as follows:
C＝V×S
X＝C×(1-|(H/60°)mod2-1|)
m＝V-C
R′，G′，B′＝((R′+m)×255，(G′+m)×255，(B′+m)×255)
wherein C is the result of lightness and saturation, and m is the difference between lightness and C.
5. The infrared and visible light fusion method based on significant object enhancement as claimed in claim 1 or 2, characterized in that in the eighth step, color enhancement is performed on R channel, G channel, and B channel respectively, as shown in the following formula:
Rout＝(Rin)1/gamma
Rdisplay＝(Rin (1/gamma))gamma
Gout＝(Gin)1/gamma
G＝(Gin (1/gamma))gamma
Bout＝(Bin)1/gamma
Bdisplay＝(Bin (1/gamma))gamma。
6. the infrared and visible light fusion method based on significant object enhancement as claimed in claim 3, wherein in the eighth step, color enhancement is performed on the R channel, the G channel, and the B channel respectively, as shown in the following formula:
Rout＝(Rin)1/gamma
Rdisplay＝(Rin(1/gamma))gamma
Gout＝(Gin)1/gamma
G＝(Gin (1/gamma))gamma
Bout＝(Bin)1/gamma
Bdisplay＝(Bin (1/gamma))gamma。
7. the infrared and visible light fusion method based on significant object enhancement as claimed in claim 4, wherein in the eighth step, color enhancement is performed on the R channel, the G channel, and the B channel respectively, as shown in the following formula:
Rout＝(Rin)1/gamma
Rdisplay＝(Rin (1/gamma))gamma
Gout＝(Gin)1/gamma
G＝(Gin (1/gamma))gamma
Bout＝(Bin)1/gamma
Bdisplay＝(Bin (1/gamma))gamma。
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