Infrared and visible light image fused automobile anti-blooming video image processing method
An image fusion and video image technology, applied in image data processing, image enhancement, image analysis and other directions, can solve the problems of low image brightness and lack of detail information in dark places, improve image quality, enrich detailed information in dark places, and easily the effect of observation
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Embodiment 1
[0029] Through the analysis of traffic safety accidents caused by halo at night, it is found that most of the accidents occur when the driver avoids the oncoming vehicle, and the collision objects are mostly vehicles and pedestrians in the shade around the vehicle in front. Therefore, if the detail information in the dark part of the image cannot be improved, and the effect of eliminating halo can only be improved, the occurrence of traffic safety accidents cannot be completely and effectively avoided.
[0030] For above-mentioned present situation, the present invention proposes a kind of automobile anti-halation video image processing method of fusion of infrared and visible light image, see figure 1 , the automobile anti-halation video image processing method of infrared and visible light image fusion comprises the following steps:
[0031] Step 1 The vehicle-mounted infrared thermal camera and the ordinary visible light camera collect the infrared image and the visible lig...
Embodiment 2
[0042] The automobile anti-halation video image processing method of infrared and visible light image fusion is the same as embodiment 1, wherein, the visible light image is enhanced by the MSRCR image enhancement algorithm described in step 3, including the following processing steps:
[0043] 3.1 According to the following formula, determine the Gaussian surround function to calculate the corresponding Gaussian template, and select the scale C corresponding to each template;
[0044] F(x,y)=μ·exp((-(x 2 +y 2 ) 2 ) / C 2 )&∫∫F(x,y)dxdy=1 (1)
[0045] In the formula: F(x, y) represents the selected Gaussian surround function model; (x, y) represents the coordinates of the pixel point; μ represents the normalization constant of the corresponding channel; C represents the scale constant of F(x, y) , the larger the scale C is, the greater the dynamic range compression of the image is, and the corresponding image details are more prominent, and the smaller the scale C is, the be...
Embodiment 3
[0057] The automobile anti-halation video image processing method of infrared and visible light image fusion is the same as embodiment 1-2, wherein described in step 4 carries out enhancement processing to infrared image by MSR image enhancement algorithm, comprises following processing steps:
[0058] 4.1 According to the following formula, determine the Gaussian surround function to calculate the corresponding Gaussian template, and select the scale number C corresponding to each template;
[0059] F(x,y)=μ·exp((-(x 2 +y 2 ) 2 ) / C 2 )&∫∫F(x,y)dxdy=1 (1)
[0060] 4.2 According to the following formula, compare the infrared image to a color channel of the visible light image, calculate the weighted average of the results obtained under the three scales, the value of the weight coefficient W, and obtain the enhanced infrared image according to each weight;
[0061] R M S R ( ...
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