为了对雾霾天气下的图像进行去雾处理, 多幅图像去雾算法是常用的方法之一。 多幅图像去雾算法也有多种形式, 部分算法面临硬件实现困难、 获取途径受限或者可实施性弱等问题, 而且多幅图像比对处理时常常涉及图像配准, 造成算法的实时性差、 计算复杂度高等问题。 针对以上问题, 提出的算法为多幅图像去雾提供了新的思路, 基于双目传感器硬件架构能够同时捕获近红外和可见光图像, 将近红外传感器图像作为新的数据源, 近红外传感器能够在一定程度上穿透雾霾, 在雾天捕获可见光传感器无法捕获的图像细节, 而且硬件实现简单。 可见光图像的颜色信息较丰富, 近红外传感器图像对近处场景细节的描述能力较好, 捕获的图像稍加校正就能实现完全配准, 将近红外图像与可见光图像进行融合, 在去雾的同时, 可以将近红外传感器图像中的原始细节提取融合到彩色可见光传感器图像中, 得到边缘、 轮廓等细节信息更加丰富的去雾图像。 基于上述思路, 借助近红外传感器对边缘细节的描述能力和可见光传感器对颜色信息的反映能力, 提出了一种基于近红外与可见光双通道传感器图像融合的去雾算法。 首先, 将彩色可见光图像转换到HIS彩色空间, 分别得到亮度通道图像、 色调通道图像和饱和度通道图像。 先将其亮度通道图与近红外图像进行融合去雾处理。 采用非下采样Shearlet变换（NSST）进行分解, 对得到的高频系数进行双指数边缘平滑滤波器保边滤波处理, 对低频系数进行反锐化掩蔽处理, 通过融合规则和反向变换得到新的亮度通道图像。 然后, 在对可见光图像的色彩处理中, 建立饱和度图的退化模型, 采用暗原色原理对参数进行估计, 得到估计的饱和度图。 最后, 将新的亮度通道图像, 估计的饱和度图像和原色调图像反映射到RGB空间得到去雾图像。 为了验证新算法的有效性, 特选取四组雾天拍摄的真实近红外图像与可见光图像进行融合去雾处理, 将融合结果与其他两种去雾方法对于彩色可见光图像的去雾效果进行比较。 实验结果表明, 该算法在提高图像的边缘对比度和视觉清晰度上有较好的效果。 并提出将近红外传感器图像作为新的数据源, 采用双通道图像融合方法进行去雾处理, 为图像去雾提供的新的技术思路是可行的。 该算法的优势在于: 首先提出将图像融合方法与去雾算法相结合, 得到了新的去雾算法的思路。 将彩色可见光图像转换到HSI色彩空间, 将其亮度通道图与近红外图像采用非下采样Shearlet变换方法进行融合处理, 在去雾的同时, 可以将近红外传感器图像中的原始细节提取融合到彩色可见光传感器图像中, 使得去雾图像中的边缘、 轮廓等细节信息更加丰富。 其次, 提出了在图像去雾算法中采用新的数据源——近红外传感器图像, 从图像处理的角度, 近红外传感器能够在一定程度上穿透雾霾, 对于近处场景细节的描述能力较好, 而且硬件实现简单, 捕获的图像稍加校正就能实现完全配准, 为后续的融合去雾算法带来了便利, 为图像去雾提供了新的技术途径和路线。 再次, 采用的是多幅图像去雾算法, 该算法基于双目传感器获取图像, 可见光图像的颜色信息较丰富, 近红外图像对于近处场景细节的描述能力较好, 相对于单幅图像去雾算法, 有更好的效果。 最后, 将可见光传感器图像映射到其他色彩空间, 对于每个通道的图像根据其特征有针对性地进行处理。 可见光图像的亮度通道图和近红外图像的处理采用了图像融合和增强处理, 对于可见光图像饱和度通道的处理采用了图像复原算法, 可以从整体上提升去雾效果, 对细节特征有了进一步增强。 该算法为图像去雾提供了新的技术途径和路线。

In order to defog the image under hazy weather, multiple images defogging algorithm is one of the commonly used methods. Multiple images defogging algorithm also takes many forms, some of which are usually confronted with the problems of difficult hardware implementation, limited data source achievement approaches, or poor implementation et al. Meanwhile, multiple images defogging algorithm usually needs image registration in the comparison process, causing poor real-time performance and high computation cost. For the above problems, this study supplies a new idea for multiple images defogging algorithm, the near-infrared sensor images are used as new data source. The near-infrared sensor could penetrate haze to some extent, capturing the image details that the visible light sensor could not get. Meanwhile, the hardware of dual sensor system is simple. In the dual sensor system, the visible light image has abundant color information and the near-infrared sensor image can better describe the scene details at close range. The captured images could be completely registered with little rectification. The fusion of the infrared image and visible light image could extract the image details of the near-infrared image to the color visible light image to get the defogging image with abundant edge and contour information. Therefore, this study proposed a defogging algorithm using near-infrared and visible light image fusion method based on the edge details descriptive ability of the near-infrared sensor and the expressive ability of color information. Firstly, the color visible image was transformed from RGB space to HIS color space to get the hue channel image, saturation channel image and intensity channel image. The intensity channel image and original near-infrared image were decomposed by the Non-subsampled Shearlet Transform (NSST) method to get the high frequency coefficients and the low frequency coefficients. The high frequency component was treated by the double-exponential edge smoothing filter and the low frequency component was treated by the Unsharp Masking method, then the fusion rules and the inverse NSST were adopted to get the new intensity channel image. For the color information treatment of the visible light image, the degeneration model of the saturation channel image was established and the dark channel prior was used to evaluate its parameters to get the new saturation channel image. Finally, the new intensity channel image, the new saturation channel image and the original hue channel image were inversely mapped to the RGB space to get the defogging image. In order to verify the algorithm, we adopted 4 groups of foggy near-infrared images and visible light images as the experimental data. The processed images were compared with the defogging images observed by other two defogging algorithms. The experiment results showed that the proposed algorithm has better effect in improving the edge contrast and visual clarity. This study put forward the near-infrared image as new data source and the binary channels image fusion algorithm as the defogging method, and it was verified that the new algorithm for image defogging is feasible. This algorithm has four main advantages. The first one is that we combined the image fusion method with the defogging algorithm to get a novel idea for defogging algorithm. We transformed the color visible image to HSI color space. The obtained intensity channel image and original near-infrared image were fused by the NSST method, and the image details in the near-infrared image were simultaneously extracted to the color visible image in the defogging processes. The defogged image has abundant detailed information of edge and rough. The second advantage is that this algorithm adopted near-infrared sensor image as new data source. From the perspective of image processing, the near-infrared sensor could penetrate haze to some extent, capturing the image details that the visible light sensor could not get. Meanwhile, the hardware of dual sensor system is simple. The third advantage is that we adopted multiple images defogging algorithm, which captured images by binocular sensor system, and the visible light sensor got the image with abundant color information and the near-infrared sensor got the image with good detail description ability of close shot. The fusion of the two kinds of images has better effect than the single image defogging algorithm. The fourth advantage is that the visible light image was transformed to the HIS color space, and the images of the three channels can be targetedly processed according to their data characteristics. The process of intensity channel image of visible light image and the near-infrared image adopted image fusion and enhancement methods. The process of saturation channel image of visible light image adopted image restoration method. These processing enhances the effect of defogged image on the whole. This study supplies a new technological approach and way for image defogging algorithm.