基于非线性扩散均值漂移的Retinex 雾天图像清晰化算法

针对传统Retinex 算法在处理雾天图像时，易在明暗对比强烈处产生光晕 现象以及处理后图像存在的色彩失真问题，论文提出了一种基于非线扩散均值漂移平滑的 Retinex 雾天图像清晰化算法。首先，采用小波变换调整景物信息在图像中的动态范围分布； 然后，采用局部非线性扩散均值漂移滤波器对图像进行平滑来估算入射光照信息，进而获得 景物的反射光照信息；最后，在lαβ 彩色空间利用处理前后图像的色饱和度差异来进一步补 偿由于图像平滑所造成的色饱和度损失。实验结果表明，提出的算法能够明显提高图像的清 晰度，并有效克服色彩失真和光晕伪影现象。

Clearing Approach for Foggy Image Using Local NonlinearDiffusion Mean Shift Retinex

To solve the problem of halo artifacts in the presence of high contrast edges and color distortion when using traditional Retinex algorithm to deal with foggy images, a kind of nonlinear diffusion mean shift filtering-based Retinex enhancement algorithm is proposed. Firstly, the dynamic domain of the foggy image is adjusted by means of wavelet analysis. And then, the incident light information is estimated by use of nonlinear diffusion mean shift filter-based smoothing, thus the approximate scenery reflectance can be obtained. Finally, by adjusting the saturation difference between before and after processing, compensation for saturation loss because of image smoothing is done within lαβ color space. Experimental results have shown that the proposed algorithm can not only improve the degree of clearness of foggy images, but also overcome the color distortion and halo phenomena.