分割暗通道先验邻域的单幅图像去雾算法

利用暗原色先验进行单幅图像去雾时,需采取高计算复杂度的细化程序,否则其估计的传输率易在边界处造成光晕。对导致边界处产生光晕现象的原因进行分析时发现,计算复杂度高的细化程序在去除晕轮效应时去雾过度,且传统的基于暗原色先验的单幅去雾算法在明亮区域易造成色彩失真现象。由此在原来的透射率估计时,提出一种基于色调的简单而快速的邻域分割方法。首先将原始RGB图像转换到HSI色彩空间,在H（Hue）通道中,用邻域中的点与中心点的色调的差值绝对值,来判断该邻域内的点是否属于同一区域,只使用属于同一区域的像素点来计算该区域的暗原色值;再通过修正透射率值,来校正明亮区域的色彩失真。在图像复原时,在HSI色彩空间保留色调分量不变,仅对强度分量运用修改的暗原色值进行去雾,再进行非线性增强,最后对饱和度分量进行颜色补偿。实验表明,本文的去雾算法能够显著提高场景的视觉清晰度,而且不需要图像后续修补,并能获得更好的色彩视觉保真。

The Algorithm of Segmenting the Prior Neighborhood of Dark Channel in the Single Image Dehazing

A refinement program of high computational complexity is needed to dehaze an image by using dark channel prior. It will avoid haloes at boundaries which is related to the transmission rate. In analyzing halo phenomenon at boundaries, it is founded that highly computational complexity of refinement procedures usually dehaze excessively, and the traditional methods based on dark channel prior for a single image dehazing may cause the color distortion in bright regions. Therefore, a simple and fast neighborhood segmentation method based on the hue is proposed during estimation of original transmittance. Firstly, the source RGB images are converted to HIS color space, In H (Hue) channel, differences in neighborhood of point and center point of the tone of absolute value determine whether those pixels in the neighborhood belong to the same region. Only those pixels belonging to the same areas are used to calculate Dark Channel. Then, transmission value corrects the color of bright region. When the image is restored, hue component remains unchanged in HIS color space. Only the intensity component is defogged using the modified dark channel values. Then, the nonlinear enhancement is performed. Finally, the saturation component is compensated by the color. Experiments show that the proposed algorithm can significantly improve the visual clarity of scenes and get better color fidelity without subsequent image repairing.