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CN102298769A - Colored fusion method of night vision low-light image and infrared image based on color transmission - Google Patents

Colored fusion method of night vision low-light image and infrared image based on color transmissionDownload PDF

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CN102298769A
CN102298769ACN2011101559584ACN201110155958ACN102298769ACN 102298769 ACN102298769 ACN 102298769ACN 2011101559584 ACN2011101559584 ACN 2011101559584ACN 201110155958 ACN201110155958 ACN 201110155958ACN 102298769 ACN102298769 ACN 102298769A
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color
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night vision
fusion
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金学波
鲍佳
杜晶晶
包晓敏
张水英
严国红
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Zhejiang University of Technology ZJUT
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Zhejiang University of Technology ZJUT
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Abstract

Translated fromChinese

本发明公开一种基于色彩传递的夜视弱光图像与红外图像的彩色融合方法。本发明首先将可见光与红外图像进行线性融合，得到灰度融合图像的

Figure 2011101559584100004DEST_PATH_IMAGE002
、
Figure 2011101559584100004DEST_PATH_IMAGE004
、
Figure 2011101559584100004DEST_PATH_IMAGE006
分量。利用参考图像的亮度和颜色分布对灰度融合图像的
Figure 130480DEST_PATH_IMAGE002
、
Figure 166569DEST_PATH_IMAGE004
、进行调整，将调整后的图像数据由YUV空间反变换回RGB空间得到最终彩色融合图像，利用本发明方法得到的融合图像与彩色参考图像亮度分布类似，具有良好的视觉效果。

Figure 201110155958

The invention discloses a color fusion method of a night vision low-light image and an infrared image based on color transfer. The present invention first linearly fuses visible light and infrared images to obtain gray scale fusion images

Figure 2011101559584100004DEST_PATH_IMAGE002
,
Figure 2011101559584100004DEST_PATH_IMAGE004
,
Figure 2011101559584100004DEST_PATH_IMAGE006
portion. Using the brightness and color distribution of the reference image to grayscale fusion image
Figure 130480DEST_PATH_IMAGE002
,
Figure 166569DEST_PATH_IMAGE004
, Adjustment is carried out, and the adjusted image data is converted fromYUV space back toRGB space to obtain a final color fusion image. The fusion image obtained by the method of the present invention is similar to the brightness distribution of the color reference image, and has good visual effects.

Figure 201110155958

Description

Translated fromChinese
基于色彩传递的夜视弱光图像与红外图像的彩色融合方法Color fusion method of night vision weak light image and infrared image based on color transfer

技术领域technical field

本发明属于图像处理领域，涉及的是一种基于色彩和亮度传递的可见光图像与红外图像融合方法。The invention belongs to the field of image processing, and relates to a fusion method of a visible light image and an infrared image based on color and brightness transfer.

背景技术Background technique

红外与可见光异源图像融合能提高目标探测能力，揭示出目标在单一图像中观察时无法探测特征，在安防、辅助驾驶等领域有重要的应用价值。尤其是夜间驾驶辅助系统，灯光图像地有效处理可大大扩展驾驶员的可视距离，提高安全性。改善图像的颜色信息，将融合图像显示成适合人眼观察的自然颜色，可明显改善人眼的识别性能，减小操作者的疲劳感。The fusion of infrared and visible light heterogeneous images can improve the target detection ability and reveal the features of the target that cannot be detected when observed in a single image, which has important application value in the fields of security and assisted driving. Especially in the night driving assistance system, the effective processing of light images can greatly expand the driver's visual distance and improve safety. Improve the color information of the image and display the fused image in a natural color suitable for human observation, which can significantly improve the recognition performance of the human eye and reduce the operator's fatigue.

基于颜色空间变换的专利有：基于颜色传递的多色调图像统一调整方法（CN200810162135.2）公开了一种基于颜色传递的多色调彩色图像统一调整方法，基于自适应聚类颜色传递的雾天图像清晰化方法（CN200810018174.5）公开了一种基于自适应聚类颜色传递的雾天图像清晰化方法，但上述两种方法只适用于单可见光图像。一种基于彩色传递与熵信息的红外与彩色可见光图像融合方法（CN200810017443.6）提出一种非采样Contourlet变换对灰度可见光图像及红外图像进行分解，采用基于lαβ颜色空间的彩色传递方法将可见光图像的彩色信息传递到融合图像中，得到彩色融合图像。该方法需要利用可见光的色彩，因此不适用于在汽车灯光下的夜视弱光图像与红外图像的彩色融合。Patents based on color space transformation include: Unified adjustment method for multi-tone color images based on color transfer (CN200810162135.2) discloses a unified adjustment method for multi-tone color images based on color transfer, foggy images based on adaptive clustering color transfer The clearing method (CN200810018174.5) discloses a foggy image clearing method based on adaptive clustering color transfer, but the above two methods are only applicable to single visible light images. A fusion method of infrared and color visible light images based on color transfer and entropy information (CN200810017443.6) proposes a non-sampling Contourlet transform to decompose grayscale visible light images and infrared images, and adopts a color transfer method based on lαβ color space to convert visible light The color information of the image is transferred to the fused image to obtain a color fused image. This method needs to use the color of visible light, so it is not suitable for the color fusion of night vision low-light images and infrared images under car lights.

发明内容Contents of the invention

本发明针对现有技术的不足，提供了一种基于色彩传递的夜视弱光图像与红外图像的彩色融合方法。Aiming at the deficiencies of the prior art, the present invention provides a color fusion method of night vision weak light images and infrared images based on color transfer.

本发明方法的步骤如下：The steps of the inventive method are as follows:

步骤1.将参考图像T转换到YUV空间。Step 1. Convert the reference imageT toYUV space.

YUV空间是另一种颜色表示法，Y为亮度，U、V为图像的色度，分别表示蓝色和红色与Y的差异R-Y、B-Y，也称色差信号。利用YUV空间和RGB之间的转换关系得到参考图像T的YUV空间：YUV space is another color representation,Y is the brightness,U ,V are the chromaticity of the image, respectively represent the difference between blue and red andYRY ,BY , also known as the color difference signal. Use the conversion relationship betweenYUV space andRGB to get theYUV space of the reference imageT :

Figure 2011101559584100002DEST_PATH_IMAGE001
     （1）
Figure 2011101559584100002DEST_PATH_IMAGE001
(1)

步骤2.夜视弱光图像与红外图像融合得到融合图像P。Step 2. The night vision low-light image is fused with the infrared image to obtain a fused imageP .

首先将夜视弱光图像进行单色化，得到单色可见光图像的灰度值

Figure 570173DEST_PATH_IMAGE002
First, the night vision low-light image is monochromatized to obtain the gray value of the monochromatic visible light image
Figure 570173DEST_PATH_IMAGE002

Figure 2011101559584100002DEST_PATH_IMAGE003
        （2）
Figure 2011101559584100002DEST_PATH_IMAGE003
(2)

式中

Figure 67538DEST_PATH_IMAGE004
、、
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是夜视弱光图像的R、G、B分量的灰度值。再将单色可见光图像的灰度值
Figure 4456DEST_PATH_IMAGE002
和红外图像的灰度值
Figure 2011101559584100002DEST_PATH_IMAGE007
融合得到融合图像P的伪Y、U、V分量，方法为：In the formula
Figure 67538DEST_PATH_IMAGE004
, ,
Figure 145084DEST_PATH_IMAGE006
is the gray value ofthe R ,G , andB components of the night vision low-light image. Then the gray value of the monochromatic visible light image
Figure 4456DEST_PATH_IMAGE002
and the gray value of the infrared image
Figure 2011101559584100002DEST_PATH_IMAGE007
Fusion to obtain the pseudoY ,U ,V components of the fusion imageP , the method is:

  

Figure 737926DEST_PATH_IMAGE008
          （3）
Figure 737926DEST_PATH_IMAGE008
(3)

式中

Figure 2011101559584100002DEST_PATH_IMAGE009
，
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为正有理数，通常的取值范围为
Figure 2011101559584100002DEST_PATH_IMAGE011
，
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，
Figure 2011101559584100002DEST_PATH_IMAGE013
。In the formula
Figure 2011101559584100002DEST_PATH_IMAGE009
,
Figure 973735DEST_PATH_IMAGE010
is a positive rational number, and the usual value range is
Figure 2011101559584100002DEST_PATH_IMAGE011
,
Figure 106120DEST_PATH_IMAGE012
,
Figure 2011101559584100002DEST_PATH_IMAGE013
.

步骤3.根据参考图像T的Y、U、V分量的均值和方差，调整融合图像P伪Y、U、V分量（3）的均值和方差，将参考图像的亮度分布传递到融合图像P中，得到彩色融合图像C。具体包括以下步骤：Step 3. According to the mean and variance ofthe Y ,U ,V components of the reference imageT , adjust the mean and variance of the pseudoY ,U ,V components (3) of the fused imageP , and transfer the brightness distribution of the reference image to the fused imageP , get the color fusion imageC . Specifically include the following steps:

3-1.将融合图像P的Y、U、V分量分别减去其均值，消除背景对传递效果的影响。3-1.The Y ,U , andV components of the fused imageP are respectively subtracted from their mean values to eliminate the influence of the background on the transfer effect.

3-2.对处理后的图像像素值根据其与参考图像的标准方差之比进行缩放，并加上参考图像的均值，即：3-2. Scale the pixel value of the processed image according to its ratio to the standard deviation of the reference image, and add the mean value of the reference image, namely:

Figure 198710DEST_PATH_IMAGE014
      （4）
Figure 198710DEST_PATH_IMAGE014
(4)

Figure 2011101559584100002DEST_PATH_IMAGE015
       （5）
Figure 2011101559584100002DEST_PATH_IMAGE015
(5)

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         （6）
Figure 481793DEST_PATH_IMAGE016
(6)

其中

Figure 2011101559584100002DEST_PATH_IMAGE017
、、
Figure 2011101559584100002DEST_PATH_IMAGE019
表示融合图像P每个像素的
Figure 373099DEST_PATH_IMAGE020
、
Figure 2011101559584100002DEST_PATH_IMAGE021
、分量。
Figure 2011101559584100002DEST_PATH_IMAGE023
，
Figure 344652DEST_PATH_IMAGE024
表示参考图像T的Y、U、V分量的标准偏差和均值。
Figure 2011101559584100002DEST_PATH_IMAGE025
，
Figure 653755DEST_PATH_IMAGE026
表示融合图像P的Y、U、V分量的标准偏差和均值。
Figure 2011101559584100002DEST_PATH_IMAGE027
为比例缩放系数，用于调节融合后图像的亮度，通常取值范围为。、、
Figure 2011101559584100002DEST_PATH_IMAGE031
表示彩色融合图像C每个像素的、
Figure 384502DEST_PATH_IMAGE021
、
Figure 224282DEST_PATH_IMAGE022
分量。in
Figure 2011101559584100002DEST_PATH_IMAGE017
, ,
Figure 2011101559584100002DEST_PATH_IMAGE019
Indicates the value of each pixel of the fused imageP
Figure 373099DEST_PATH_IMAGE020
,
Figure 2011101559584100002DEST_PATH_IMAGE021
, portion.
Figure 2011101559584100002DEST_PATH_IMAGE023
,
Figure 344652DEST_PATH_IMAGE024
Indicates the standard deviation and mean of the Y,U , andV componentsof the reference image T.
Figure 2011101559584100002DEST_PATH_IMAGE025
,
Figure 653755DEST_PATH_IMAGE026
Indicates the standard deviation and mean ofthe Y ,U , andV components of the fused imageP.
Figure 2011101559584100002DEST_PATH_IMAGE027
is a scaling factor, used to adjust the brightness of the fused image, usually the value range is . , ,
Figure 2011101559584100002DEST_PATH_IMAGE031
represents the color fusion imageC for each pixel ,
Figure 384502DEST_PATH_IMAGE021
,
Figure 224282DEST_PATH_IMAGE022
portion.

步骤4.通过YUV逆变换获得彩色融合图像C的R、G、B值，逆变换方法：Step 4. Obtain theR ,G ,B values of color fusion imageC byYUV inverse transformation, inverse transformation method:

Figure 704942DEST_PATH_IMAGE032
Figure 704942DEST_PATH_IMAGE032

本发明的有益效果是：The beneficial effects of the present invention are:

1.利用求图像伪Y、U、V分量的方法，将彩色信息量很少的夜视微光图像和黑白的红外图像进行融合，建立了弱彩色图像、黑白图像和正常的彩色图像之间的颜色联系，为弱彩色图像、黑白图像进行彩色增强提供了必要条件。1. Using the method of calculating the pseudoY ,U andV components of the image, the night vision low-light image with little color information and the black and white infrared image are fused, and the relationship between the weak color image, the black and white image and the normal color image is established. The color connection provides the necessary conditions for color enhancement of weak color images and black and white images.

2.根据参考图像的Y、U、V各分量对融合后图像进行了色彩增强处理，使融合后图像具有与参考图像相同分布的颜色分量。2. According tothe Y ,U ,V components of the reference image, the color enhancement processing is carried out on the fused image, so that the fused image has the same distribution of color components as the reference image.

3.利用

Figure 59700DEST_PATH_IMAGE027
比例缩放系数可以根据实际情况对融合图像的亮度进行相应调整，具有更强的适应性。3. Use
Figure 59700DEST_PATH_IMAGE027
The scaling factor can adjust the brightness of the fusion image according to the actual situation, which has stronger adaptability.

附图说明Description of drawings

图1是本发明方法流程图。Fig. 1 is a flow chart of the method of the present invention.

具体实施方式Detailed ways

以下结合附图对本发明作进一步说明。The present invention will be further described below in conjunction with accompanying drawing.

如图1所示，本发明方法包括以下步骤：As shown in Figure 1, the inventive method comprises the following steps:

步骤(1)将参考图像T由RGB空间转换为YUV空间，得到参考图像T的Y、U、V空间：Step (1) Convert the reference imageT fromthe RGB space to theYUV space, and obtainthe Y ,U , andV spaces of the reference imageT :

Figure 979115DEST_PATH_IMAGE001
Figure 979115DEST_PATH_IMAGE001

步骤(2)将夜视弱光图像与红外图像的融合得到融合图像P伪Y、U、V分量，具体方法为：先将夜视弱光图像进行单色化，得到单色可见光图像的灰度值

Figure 673401DEST_PATH_IMAGE002
，再单色可见光图像的灰度值
Figure 390209DEST_PATH_IMAGE002
和红外图像的灰度值
Figure 435525DEST_PATH_IMAGE007
融合得到融合图像P的伪Y、U、V分量，方法为：Step (2) Fusion the night vision weak light image and the infrared image to obtainthe pseudoY ,U andV components of the fusion image. The specific method is: first monochromatize the night vision weak light image to obtain the grayscale degree value
Figure 673401DEST_PATH_IMAGE002
, and then the gray value of the monochromatic visible light image
Figure 390209DEST_PATH_IMAGE002
and the gray value of the infrared image
Figure 435525DEST_PATH_IMAGE007
Fusion to obtain the pseudoY ,U ,V components of the fusion imageP , the method is:

    

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Figure 830734DEST_PATH_IMAGE003
    

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式中

Figure 529886DEST_PATH_IMAGE004
、
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、
Figure 58136DEST_PATH_IMAGE006
是夜视弱光图像的R、G、B分量的灰度值；In the formula
Figure 529886DEST_PATH_IMAGE004
,
Figure 859236DEST_PATH_IMAGE005
,
Figure 58136DEST_PATH_IMAGE006
is the gray value ofthe R ,G , andB components of the night vision low-light image;

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，
Figure 782696DEST_PATH_IMAGE010
为正有理数，通常的取值范围为，
Figure 336354DEST_PATH_IMAGE012
，
Figure 859739DEST_PATH_IMAGE013
。
Figure 523753DEST_PATH_IMAGE009
,
Figure 782696DEST_PATH_IMAGE010
is a positive rational number, and the usual value range is ,
Figure 336354DEST_PATH_IMAGE012
,
Figure 859739DEST_PATH_IMAGE013
.

步骤(3).根据参考图像调节融合后图像的色彩分量，具体方法为：先将融合图像P的伪Y、U、V分量分别减去其均值，消除背景对传递效果的影响；再对处理后的图像像素值根据其与参考图像的标准方差之比进行缩放，并加上参考图像的均值，即：Step (3). Adjust the color component of the fused image according to the reference image. The specific method is: first subtract its mean value from the pseudoY ,U , andV components of the fused imageP to eliminate the influence of the background on the transfer effect; The resulting image pixel values are scaled according to their ratio to the standard deviation of the reference image and added to the mean of the reference image, namely:

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其中

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、、表示融合图像P每个像素的
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、
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、
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分量。
Figure 59307DEST_PATH_IMAGE023
，
Figure 4129DEST_PATH_IMAGE024
表示参考图像T的Y、U、V分量的标准偏差和均值。
Figure 356613DEST_PATH_IMAGE025
，表示融合图像P的Y、U、V分量的标准偏差和均值。
Figure 799413DEST_PATH_IMAGE027
为比例缩放系数，用于调节融合后图像的亮度，通常取值范围为。
Figure 692599DEST_PATH_IMAGE029
、
Figure 602786DEST_PATH_IMAGE030
、
Figure 528017DEST_PATH_IMAGE031
表示彩色融合图像C每个像素的
Figure 814642DEST_PATH_IMAGE020
、、
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分量。in
Figure 574437DEST_PATH_IMAGE017
, , Indicates the value of each pixel of the fused imageP
Figure 779657DEST_PATH_IMAGE020
,
Figure 277634DEST_PATH_IMAGE021
,
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portion.
Figure 59307DEST_PATH_IMAGE023
,
Figure 4129DEST_PATH_IMAGE024
Indicates the standard deviation and mean of the Y,U , andV componentsof the reference image T.
Figure 356613DEST_PATH_IMAGE025
, Indicates the standard deviation and mean ofthe Y ,U , andV components of the fused imageP.
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is a scaling factor, used to adjust the brightness of the fused image, usually the value range is .
Figure 692599DEST_PATH_IMAGE029
,
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,
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represents the color fusion imageC for each pixel
Figure 814642DEST_PATH_IMAGE020
, ,
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portion.

步骤(4).通过YUV逆变换获得彩色融合图像C的R、G、B值。逆变换方法：Step (4). Obtain theR ,G , andB values of the color fusion imageC throughYUV inverse transformation. Inverse transformation method:

Figure 900913DEST_PATH_IMAGE032
。
Figure 900913DEST_PATH_IMAGE032
.

Claims (1)

1. based on the night vision low light level image of color transmission and the Color Fusion of infrared image, it is characterized in that the concrete steps of this method are:
Step 1. is with reference pictureTBe transformed intoYUVThe space, detailed process is:
Figure 2011101559584100001DEST_PATH_IMAGE002
（1）
Step 2. night vision low light level image and infrared image fusion obtain fused imagesP
At first night vision low light level image is carried out monochromatization, obtain the gray-scale value of monochromatic visible light image
Figure 2011101559584100001DEST_PATH_IMAGE004
Figure 2011101559584100001DEST_PATH_IMAGE006
（2）
In the formula,
Figure 2011101559584100001DEST_PATH_IMAGE010
,
Figure 2011101559584100001DEST_PATH_IMAGE012
It is night vision low light level imageR,G,BThe gray-scale value of component; Again with the gray-scale value of monochromatic visible light image
Figure 2321DEST_PATH_IMAGE004
Gray-scale value with infrared image
Figure 2011101559584100001DEST_PATH_IMAGE014
Merge and obtain fused imagesPPuppetY,U,VComponent, detailed process is:
Figure 2011101559584100001DEST_PATH_IMAGE016
（3）
In the formula
Figure 2011101559584100001DEST_PATH_IMAGE018
,
Figure 2011101559584100001DEST_PATH_IMAGE020
Be positive rational number;
Step 3. is according to reference pictureTY,U,VThe average of component and variance are adjusted fused imagesPPseudo-Y,U,VThe average of component and variance are delivered to fused images with the reference brightness distributionPIn, obtain the color integration imageCSpecifically may further comprise the steps:
3-1. with fused imagesPY,U,VComponent deducts its average respectively, eliminates the influence of background to transmission effect;
3-2. the image pixel value after handling is carried out convergent-divergent according to its ratio with the standard variance of reference picture, and adds the average of reference picture, that is:
Figure 2011101559584100001DEST_PATH_IMAGE022
（4）
Figure 2011101559584100001DEST_PATH_IMAGE024
（5）
Figure 2011101559584100001DEST_PATH_IMAGE026
（6）
Wherein
Figure 2011101559584100001DEST_PATH_IMAGE028
,
Figure 2011101559584100001DEST_PATH_IMAGE030
,
Figure 2011101559584100001DEST_PATH_IMAGE032
Represent fused images respectivelyPEach pixel
Figure 2011101559584100001DEST_PATH_IMAGE034
,
Figure 2011101559584100001DEST_PATH_IMAGE036
,
Figure 2011101559584100001DEST_PATH_IMAGE038
Component;
Figure 2011101559584100001DEST_PATH_IMAGE040
,
Figure 2011101559584100001DEST_PATH_IMAGE042
Represent reference picture respectivelyThe Y of T,U,VThe standard deviation of component and average;
Figure 2011101559584100001DEST_PATH_IMAGE044
,
Figure 2011101559584100001DEST_PATH_IMAGE046
Represent fused images respectivelyPY,U,VThe standard deviation of component and average,Be the proportional zoom coefficient, be used to regulate the brightness of fused image;
Figure 2011101559584100001DEST_PATH_IMAGE050
,
Figure 2011101559584100001DEST_PATH_IMAGE052
,Expression color integration imageCEach pixel,
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,
Figure 349099DEST_PATH_IMAGE038
Component;
Step 4. is passed throughYUVInverse transformation obtains the color integration imageCR,G,BValue, the inverse transformation detailed process is:
Figure 2011101559584100001DEST_PATH_IMAGE056
。
CN2011101559584A2011-06-112011-06-11Colored fusion method of night vision low-light image and infrared image based on color transmissionPendingCN102298769A (en)

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CN111402306A (en)*2020-03-132020-07-10中国人民解放军32801部队Low-light-level/infrared image color fusion method and system based on deep learning
CN111476732A (en)*2020-04-032020-07-31江苏宇特光电科技股份有限公司Image fusion and denoising method and system
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CN112767298A (en)*2021-03-162021-05-07杭州海康威视数字技术股份有限公司Method and device for fusing visible light image and infrared image
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CN115034974A (en)*2022-05-062022-09-09电子科技大学 Method, device and storage medium for natural color restoration of visible light and infrared fusion images
CN118828146A (en)*2024-08-092024-10-22沈阳航空航天大学 Color night vision method based on improved GAN network and color contrast enhancement
CN119887538A (en)*2024-12-262025-04-25北京泰岳天成科技有限公司Infrared image and visible light image fusion method
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CN102547063B (en)*2012-02-082014-06-11南京航空航天大学Natural sense color fusion method based on color contrast enhancement
CN102547063A (en)*2012-02-082012-07-04南京航空航天大学Natural sense color fusion method based on color contrast enhancement
CN105917641A (en)*2013-08-012016-08-31核心光电有限公司 Slim multi-aperture imaging system with autofocus and method of use
CN108989649A (en)*2013-08-012018-12-11核心光电有限公司With the slim multiple aperture imaging system focused automatically and its application method
CN104156992B (en)*2014-07-182017-02-15北京理工大学Visible light black and white video image natural colorization method based on color transferring
CN104143183B (en)*2014-08-072017-12-12北京理工大学The gray scale fusion method of visible ray and infrared black and white video image is transmitted based on brightness
CN107005639B (en)*2014-12-102020-04-14索尼公司 Image pickup apparatus, image pickup method, and image processing apparatus
CN107005639A (en)*2014-12-102017-08-01索尼公司Image pick up equipment, image pickup method, program and image processing equipment
CN104601953B (en)*2015-01-082017-12-15中国航空无线电电子研究所A kind of video image fusion processing system
CN104601953A (en)*2015-01-082015-05-06中国航空无线电电子研究所Video image fusion-processing system
CN104796577A (en)*2015-03-202015-07-22南京理工大学Colored night vision imaging device and method based on EMCCD and single-color CCD
CN104796577B (en)*2015-03-202017-10-24南京理工大学Color night vision imaging device and method based on EMCCD and monochrome CCD
CN105046869A (en)*2015-07-062015-11-11北京理工大学Forest fire prevention monitoring system based on double-wave-band fusion theory
CN105701765A (en)*2015-09-232016-06-22河南科技学院Image-processing method and mobile terminal
CN106570850A (en)*2016-10-122017-04-19成都西纬科技有限公司Image fusion method
CN106570850B (en)*2016-10-122019-06-04成都西纬科技有限公司A kind of image interfusion method
CN106815826A (en)*2016-12-272017-06-09上海交通大学Night vision image Color Fusion based on scene Recognition
CN110651301A (en)*2017-05-242020-01-03黑拉有限责任两合公司Method and system for automatically coloring night vision images
CN107169950A (en)*2017-06-022017-09-15江苏北方湖光光电有限公司A kind of high-definition picture fusion treatment circuit
CN108040243A (en)*2017-12-042018-05-15南京航空航天大学Multispectral 3-D visual endoscope device and image interfusion method
CN110211083A (en)*2019-06-102019-09-06北京宏大天成防务装备科技有限公司A kind of image processing method and device
CN112217962A (en)*2019-07-102021-01-12杭州海康威视数字技术股份有限公司Camera and image generation method
CN112217962B (en)*2019-07-102022-04-05杭州海康威视数字技术股份有限公司Camera and image generation method
CN113362261A (en)*2020-03-042021-09-07杭州海康威视数字技术股份有限公司Image fusion method
CN113362261B (en)*2020-03-042023-08-11杭州海康威视数字技术股份有限公司Image fusion method
CN111402306A (en)*2020-03-132020-07-10中国人民解放军32801部队Low-light-level/infrared image color fusion method and system based on deep learning
CN111476732A (en)*2020-04-032020-07-31江苏宇特光电科技股份有限公司Image fusion and denoising method and system
CN112767298A (en)*2021-03-162021-05-07杭州海康威视数字技术股份有限公司Method and device for fusing visible light image and infrared image
CN112767298B (en)*2021-03-162023-06-13杭州海康威视数字技术股份有限公司Fusion method and device of visible light image and infrared image
CN115034974A (en)*2022-05-062022-09-09电子科技大学 Method, device and storage medium for natural color restoration of visible light and infrared fusion images
CN118828146A (en)*2024-08-092024-10-22沈阳航空航天大学 Color night vision method based on improved GAN network and color contrast enhancement
CN119887538A (en)*2024-12-262025-04-25北京泰岳天成科技有限公司Infrared image and visible light image fusion method
CN119887538B (en)*2024-12-262025-10-17北京泰岳天成科技有限公司Infrared image and visible light image fusion method

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