CN112419212A - Infrared and visible light image fusion method based on side window guide filtering - Google Patents
Infrared and visible light image fusion method based on side window guide filtering Download PDFInfo
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Abstract
The invention discloses an infrared and visible light image fusion method based on side window guide filtering, which belongs to the technical field of image processing and comprises the following steps: respectively carrying out fuzzy processing on input infrared and visible light source images by using a Gaussian filter to obtain a base layer with large scale; subtracting the image of the basic layer from the image of the infrared and visible light sources to obtain a detailed layer image containing small-scale information; calculating the correlation coefficient of the infrared and visible light source images to obtain the fusion weight coefficient of the base layer, and fusing the base layer images; obtaining an initial detail layer weight map by using a maximum absolute value rule, guiding filtering and optimizing the initial detail layer weight map by using a side window, and fusing detail layer images; and performing secondary fusion by using the fused base layer image and the fused detail layer image to obtain a final fusion image. The invention can realize the fusion of the infrared and visible light images, and the fused images can keep good contrast.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a method for fusing an infrared light image and a visible light image based on side window guide filtering.
Background
For the same application scene, different bands of image sensors may reflect different scene information. Image fusion of different spectral bands is one of the research hotspots in the fields of computer vision and image processing. Infrared light and visible light have different imaging principles. Infrared imaging sensors capture thermal radiation emitted by an object and are extremely sensitive to hot targets, but lack background texture detail. Visible light image sensors can capture more scene detail and texture information, but are susceptible to interference from the imaged scene. Such as lighting conditions, fog, occlusion, etc., can severely affect image quality. The infrared and visible light image fusion can provide more complementary information, and is more beneficial to human eye observation or computer vision analysis. In recent years, infrared and visible light image fusion is widely applied in the fields of video fusion, night vision, biological identification, remote sensing, military, agriculture and the like.
In the past decades, a large number of image fusion methods have been proposed and applied in different fields, among which methods based on multi-scale decomposition, methods based on sparse representation, methods based on principal component analysis, the three most commonly used. At present, the method based on multi-scale decomposition is the hottest and the most widely used method. However, these methods generally have the disadvantages of low computational efficiency, low contrast of fused images, insufficient target projection, easy occurrence of halo and artifact phenomena, and the like.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an infrared and visible light image fusion method based on side window guide filtering. Firstly, carrying out multi-scale decomposition on input infrared and visible light images by using Gaussian filtering to obtain a base layer image containing large-scale information and a detail layer image containing small-scale information. Then, different fusion strategies are used in the different upscaled images. The weight coefficient of the fusion of the basic layer is obtained by calculating the correlation coefficient of the input infrared and visible light images, and the fused images are ensured to have enough contrast. And obtaining an initial weight map by the weight map of the detail layer through a maximum absolute value rule, and obtaining a fused weight map of the detail layer by using side window guided filtering optimization. And finally, obtaining a final fusion image through linear addition.
The invention provides an infrared and visible light image fusion method based on a side window guide filtering optimization weight graph by utilizing a Gaussian filtering multi-scale image decomposition and side window guide filtering optimization weight graph method, which mainly comprises the following steps of:
1: and a multi-scale decomposition tool of the Gaussian filter is utilized, so that the scale image can be effectively separated. Decomposing an input source image into a base layer containing large-scale information and a detail layer containing small-scale information, and fusing at different scale layers by adopting different fusion strategies. Meanwhile, the fused image is reconstructed by linear addition, so that the richness of fused image information is promoted, the algorithm complexity is reduced, and the efficiency is improved.
2: the weight map for detail layer image fusion can be effectively optimized by adopting an infrared and visible light image fusion algorithm based on side window filtering. The weight coefficient of the basic layer is obtained by calculating the correlation coefficient of the input infrared and visible light images, so that the overall contrast of the fused image can be maintained, a better visual effect is obtained, and the robustness of the algorithm is improved. And simultaneously, optimizing the detail layer fusion initial weight obtained by the maximum absolute value by using side window guide filtering. The original input image is used as a guide image of side window guide filtering, and the initial weight optimization effect of the detail layer with different roughness degrees can be obtained by setting the filtering radius and the standard deviation parameter of the side window guide filter, so that the significant information of the detail layer image can be retained to the maximum extent.
An infrared and visible light image fusion method based on side window guide filtering comprises the following steps:
step 1: obtaining the base layer by multi-scale decomposition
For input infrared source image
And visible light source images
Obtaining a processed source image by using a Gaussian filter to obtain a base layer image containing large-scale information, and subtracting the source image from the base layer image to obtainTo a detail layer image containing small-scale information, the process is as follows:
wherein the content of the first and second substances,
which represents a gaussian filtering operation, is shown,
and
respectively, the radius and standard deviation of the gaussian filter.
And
respectively corresponding to the multi-scale decomposed infrared and visible light base layer images.
The Gaussian filter is a filter commonly used in the field of digital image processing and is characterized by simple calculation and good smoothing effect. The input infrared and visible light images are subjected to multi-scale decomposition by using Gaussian filtering, and different scale information can be effectively separated by setting different filtering radiuses and standard deviation parameters of the Gaussian filter. Meanwhile, the image edge information of different scales can be kept in the decomposition process, and the final fusion effect is promoted.
Step 2: obtaining detail layer by layer through multi-scale decomposition
Infrared source image
And visible light source images
Subtracting the base layer image to obtain a detail layer image containing small-scale informationThe process is as follows:
wherein the content of the first and second substances,
and
corresponding to the multi-scale decomposed infrared and visible light detail layer images, respectively.
And 3, step 3: base layer weight coefficient acquisition
Infrared source image input by calculation
And visible light source images
The weight coefficient of the base layer image fusion is obtained by the following process:
wherein the content of the first and second substances,
representing the correlation coefficients of the input source image. Then, respectively distributing the weight coefficients of the infrared and visible light base layer image fusion according to the correlation coefficients of the source image:
wherein the content of the first and second substances,
and
respectively corresponding to the weight coefficients of the infrared and visible light base layer image fusion. The fusion coefficient of the basic layer is obtained by calculating the correlation coefficient, so that the final fusion image can obtain good contrast, and the robustness of the whole fusion algorithm is improved.
And 4, step 4: base layer weight coefficient acquisition
And (4) obtaining an initial weight map of the detail layer by utilizing a maximum absolute value rule for the infrared and visible light detail layer images obtained in the step (4), wherein the process is represented as follows:
wherein the content of the first and second substances,
a detail layer initial weight map representing an infrared image,
a detail layer initial weight map representing a visible light image. Then, the initial weights of the detail layer are optimized using side window guidance, and the process is expressed as follows:
wherein the content of the first and second substances,
a side-window guided filtering operation is shown,
the size of the side window guide filter is indicated.
Indicating the standard deviation of the side window guide filter, the degree of blurring can be controlled.
And
are the input infrared and visible source images, here as the side window guide filtered guide image.
And
the weight map is respectively corresponding to the fusion of the infrared and visible light detail layer images.
In the process of extracting the initial weight map of the detail layer, the maximum absolute value rule is utilized, the saliency information of the input infrared and visible light images can be effectively extracted, and the target object is highlighted. The side window guide filtering is used for optimizing the initial weight map of the detail layer, so that the visual effect of the fused image can be greatly improved, and the phenomena of halation and artifacts are avoided. The original input image is used as a guide image for side window guide filtering, and the initial weight optimization effect of the detail layer with different roughness degrees can be obtained by setting the filtering radius and the standard deviation parameter of the side window guide filter, so that the final fusion effect is promoted.
And 5, step 5: preliminarily merging base layer and detail layer
And (3) preliminarily fusing the base layer and the detail layer by using the weights of the infrared and visible light base layers and the detail layers obtained in the steps (3) and (4), wherein the process is as follows:
wherein the content of the first and second substances,
a base layer fused image representing the infrared and visible images,
a detail layer fused image representing the infrared and visible images.
And 6, step 6: weighted reconstruction of fused images
And 5, obtaining a final fusion image by adding the two image layers of the fusion image of the basic layer and the detail layer obtained in the step 5, wherein the process is as follows:
wherein the content of the first and second substances,
and the final fusion result.
The invention has the beneficial effects that: aiming at the infrared and visible light image fusion technology, Gaussian filtering is utilized to carry out multi-scale decomposition on the input infrared and visible light source images to obtain a basic layer containing large-scale information and a detail layer containing small-scale information. In the fusion process of the basic layer, fusion is carried out by utilizing the correlation coefficient of the source image, and the contrast and the robustness of the finally fused image are ensured. The fusion of the detail layers is combined with the side window guide filtering to optimize the initial weight of the detail layers obtained by the maximum absolute value rule, so that the visual effect of the fused image can be greatly improved, and the phenomena of halation and artifacts are avoided. In the invention, as long as the infrared and visible light images of the same scene are input, effective multi-scale fusion can be carried out, and a high-quality fusion image can be obtained.
Drawings
FIG. 1 is a flow chart of an algorithm;
FIG. 2(a) is an input infrared image;
FIG. 2(b) is an input visible light image;
FIG. 3(a) is a base layer for infrared image multi-scale decomposition;
FIG. 3(b) is a detail layer of the infrared image multi-scale decomposition;
FIG. 4(a) is a base layer for multi-scale decomposition of a visible light image;
FIG. 4(b) is a detail layer of a multi-scale decomposition of a visible light image;
FIG. 5(a) is an initial weight of an infrared image detail layer;
FIG. 5(b) is an initial weight of a visible image detail layer;
FIG. 6(a) is a fusion weight graph of infrared images;
FIG. 6(b) is a fusion weight graph of visible light images;
FIG. 7(a) is a base layer fusion map;
FIG. 7(b) is a detail layer fusion diagram;
fig. 8 shows the result of image fusion between infrared light and visible light.
Detailed Description
The technical solution of the present invention is described in detail and fully with reference to the accompanying drawings.
Figure 1 shows a flow chart of the present invention.
Fig. 2 is an example of a set of infrared and visible light images of the same scene, where fig. 2(a) is an input infrared image and fig. 2(b) is an input visible light image.
Fig. 3-4 show layers of a multi-scale decomposition with gaussian filtering. Fig. 3(a) and fig. 4(a) are the basic layers obtained by decomposing the infrared and visible light source images, respectively, and mainly contain large-scale information in the images. Fig. 3(b) and fig. 4(b) are detail layers obtained by decomposing images of infrared and visible light sources, respectively, and mainly contain small-scale information in the images. Fig. 5(a) and 5(b) are initial weights for infrared and visible image detail layers, respectively, reflecting the regions of most interest to human vision in the respective images. Fig. 6(a) and 6(b) are detail layer fusion weight maps obtained after the initial weight maps of the infrared and visible light images are subjected to side window guide filtering, which are helpful for overcoming artifact halo and have better subjective visual effect.
See Yin H, Gong Y, Qiu G, et al, Side window guided filtering [ J ]. Signal Processing, 2019: 315-.
In this embodiment, when performing scale decomposition on the input infrared and visible light source images by using gaussian filtering, settings are made
，
And respectively obtaining a corresponding base layer image and a corresponding detail layer image.
The fusion coefficient of the basic layer is obtained by calculating the correlation coefficient between the input infrared and visible light source images, and the fusion image of the basic layer is obtained by linear weighted summation of the infrared and visible light source images according to the numerical value of the correlation coefficient.
In this embodiment, the initial weight map of the detail layer is obtained by the maximum absolute value rule, and then the initial weight map of the detail layer is optimized by using side window guided filtering to obtain the fusion weight map of the detail layer. Wherein the input infrared and visible light source images are used as guide images of the side window guide filter, and setting is performed
，
。
And obtaining the infrared and visible light detail layer fusion images by means of linear weighted summation of the detail layer fusion weight image and the detail layer image.
And finally, the final fusion image is obtained by the linear addition of the infrared and visible light base layer fusion image and the detail layer fusion image, the final fusion result is shown in fig. 8, the fusion image has better contrast, meanwhile, the saliency information in the infrared and visible light images is better kept, and the subjective visual effect is good.
Claims (9)
1. An infrared and visible light image fusion method based on side window guide filtering is characterized by comprising the following steps:
the method comprises the following steps: multiscale decomposition versus input infrared source images
And visible light source images
Obtaining a base layer image containing large-scale information by using a Gaussian filter;
step two: multiscale decomposition vs. infrared source images
And visible light source images
Subtracting the base layer image to obtain a detail layer image containing small-scale information;
step three: infrared source image input by calculation
And visible light source images
Obtaining a weight coefficient of the image fusion of the base layer by the correlation coefficient between the base layer and the image fusion of the base layer;
step four: respectively distributing weight coefficients for fusing the infrared basic layer image and the visible light basic layer image according to the correlation coefficients of the source image;
step five: obtaining a detail layer initial weight graph by utilizing a maximum absolute value rule for the detail layer image containing the small-scale information obtained in the step two;
step six: optimizing the initial weight of the detail layer by using side window guidance on the obtained initial weight graph of the detail layer in the step five;
step seven: preliminarily fusing the base layer and the detail layer by using the weights of the infrared base layer and the visible light base layer obtained in the fourth step and the sixth step and the weights of the infrared detail layer and the visible light detail layer;
step eight: and adding the base layer and the detail layer fusion images obtained in the step seven by utilizing the two image layers to obtain a final fusion image result.
2. The infrared and visible light image fusion method based on side window guide filtering as claimed in claim 1, wherein in the step one, the process is:
wherein the content of the first and second substances,
which represents a gaussian filtering operation, is shown,
and
respectively representing the radius and standard deviation of Gaussian filtering;
and
respectively corresponding to the multi-scale decomposed infrared and visible light base layer images.
3. The infrared and visible light image fusion method based on side window guide filtering as claimed in claim 1, wherein in the second step, the process is:
wherein the content of the first and second substances,
and
corresponding to the multi-scale decomposed infrared and visible light detail layer images, respectively.
4. The infrared and visible light image fusion method based on side window guide filtering as claimed in claim 1, wherein said step three, the process is:
wherein the content of the first and second substances,
representing the correlation coefficients of the input source image.
5. The infrared and visible light image fusion method based on side window guide filtering as claimed in claim 1, wherein said step four, the process is:
wherein the content of the first and second substances,
and
respectively corresponding to the weight coefficients of the infrared and visible light base layer image fusion.
6. The infrared and visible light image fusion method based on side window guide filtering as claimed in claim 1, wherein in the fifth step, the process is:
wherein the content of the first and second substances,
a detail layer initial weight map representing an infrared image,
a detail layer initial weight map representing a visible light image.
7. The infrared and visible light image fusion method based on side window guide filtering as claimed in claim 1, wherein in step six, the process is:
wherein the content of the first and second substances,
a side-window guided filtering operation is shown,
represents the size of the side window guide filter;
the standard deviation of the side window guide filter is represented, and the fuzzy degree can be controlled;
and
is the input infrared and visible light source images, here as the guide image of the side window guide filter;
and
the weight map is respectively corresponding to the fusion of the infrared and visible light detail layer images.
8. The infrared and visible light image fusion method based on side window guide filtering as claimed in claim 1, wherein in the seventh step, the process is:
wherein the content of the first and second substances,
a base layer fused image representing the infrared and visible images,
a detail layer fused image representing the infrared and visible images.
9. The infrared and visible light image fusion method based on side window guide filtering as claimed in claim 1, wherein in step eight, the process is:
wherein the content of the first and second substances,
and the final fusion result.
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| CN113793318A (en) * | 2021-09-13 | 2021-12-14 | 中国空气动力研究与发展中心超高速空气动力研究所 | Multi-region complex damage defect characteristic comprehensive analysis method |
| CN113935922A (en) * | 2021-10-21 | 2022-01-14 | 燕山大学 | Infrared and visible light image feature enhancement fusion method |
| CN114092369A (en) * | 2021-11-19 | 2022-02-25 | 中国直升机设计研究所 | Image fusion method based on visual saliency mapping and least square optimization |
| CN114757912A (en) * | 2022-04-15 | 2022-07-15 | 电子科技大学 | Material damage detection method, system, terminal and medium based on image fusion |
| CN115578304A (en) * | 2022-12-12 | 2023-01-06 | 四川大学 | Multi-band image fusion method and system combining saliency region detection |
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| CN113129243A (en) * | 2021-03-10 | 2021-07-16 | 同济大学 | Blood vessel image enhancement method and system based on infrared and visible light image fusion |
| CN113763368A (en) * | 2021-09-13 | 2021-12-07 | 中国空气动力研究与发展中心超高速空气动力研究所 | Large-size test piece multi-type damage detection characteristic analysis method |
| CN113793318A (en) * | 2021-09-13 | 2021-12-14 | 中国空气动力研究与发展中心超高速空气动力研究所 | Multi-region complex damage defect characteristic comprehensive analysis method |
| CN113793318B (en) * | 2021-09-13 | 2023-04-07 | 中国空气动力研究与发展中心超高速空气动力研究所 | Multi-region complex damage defect characteristic comprehensive analysis method |
| CN113935922A (en) * | 2021-10-21 | 2022-01-14 | 燕山大学 | Infrared and visible light image feature enhancement fusion method |
| CN114092369A (en) * | 2021-11-19 | 2022-02-25 | 中国直升机设计研究所 | Image fusion method based on visual saliency mapping and least square optimization |
| CN114757912A (en) * | 2022-04-15 | 2022-07-15 | 电子科技大学 | Material damage detection method, system, terminal and medium based on image fusion |
| CN115578304A (en) * | 2022-12-12 | 2023-01-06 | 四川大学 | Multi-band image fusion method and system combining saliency region detection |
| CN115578304B (en) * | 2022-12-12 | 2023-03-10 | 四川大学 | Multi-band image fusion method and system combining saliency region detection |
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