改进的多光谱双边滤波图像融合

针对低照度或夜晚条件下彩色图像信噪比低、图像细节不够清晰;而近红外相机在该条件下能够得到纹理、边缘等细节信息丰富的图像,但缺乏色彩信息的问题,提出一种改进的双边滤波图像融合算法,实现在低照度条件下得到成像清晰的彩色图像。算法对双边滤波的核函数重新设计,用幂函数取代指数函数,取消人为设计参数;在像素相似度项选择上,采用不同源图像中像素间差异大的差值作为相似度项,避免了融合图像的纹理、边缘被平滑掉。应用本文算法及其他几种典型的融合算法对低照度下采集的彩色图像及近红外图像进行测试,实验结果表明,该算法同其他融合算法相比得到的彩色图像清晰度更高,颜色更贴近源图像,且运算速度要比Eric等人提出的双边滤波融合算法快6倍多。

Improved multispectral bilateral filter video fusion algorithm

In low light or night conditions, the signal-to-noise ratio is low and the details are not clear in color images. Although near infrared cameras can get image with rich texture, the edge details lacks color under this condidtion. In order to get a clear color image, we propose an improved bilateral-filter image-fusion algorithm in low light conditions in this paper. In the algorithm, the kernel function is re-designed using the power function to replace the exponential function, and canceling the design parameter. In order to avoid image texture and edge smoothing, the large difference of pixels is used as a pixel similarity in the color image and near IR image for image fusion. By using of a color image and an infrared image, this method and several other typical fusion methods are tested under low light condition. The experimental results show that the color images have higher definition compared with other fusion algorithm. The color is more close to the source image. The computation speed is more than 6 times compared faster to the Eric.P algorithm.