结合WLS滤波与还原控制因子的图像去雾

雾天退化图像的复原过程中,针对大气光幕和大气亮度估计不准确导致光晕效应、偏色现象和对比度不足等问题,提出一种结合WLS (weighted least square)滤波与还原控制因子的去雾算法.首先分析WLS滤波器的原理和性能,并用于大气光幕的有效提取;其次利用Sobel算子检测二值化图像边缘,将边缘数目与像素均值同时作为四叉树空间索引的依据,提高大气亮度的估计准确性;最后分析天空出现颜色失衡现象的原因,引入还原控制因子改善视觉效果.实验结果表明,去雾后图像的平均梯度整体提高58.03%,信息熵提高2.88%,运行时间节省50%以上.该方法对含有浓雾、薄雾以及天空等深度复杂的远景图像、近景图像均能得到高对比度、可视度和色彩保真度的恢复效果.

Image Dehazing Based on WLS Filtering and Restorative Controlling Factor

As the inaccurate estimation of atmospheric light curtain and atmospheric light result in the halo effect, color distortion, and low contrast in the process of haze image restoration, a dehazing algorithm based on weighted least square (WLS) filtering and restorative controlling factor is proposed. Firstly, this study analyzes the principle and performance of the WLS filter, which can be utilized to effectively estimate the atmospheric light curtain. Secondly, with the assistance of the Sobel operator, the binary image edges are detected. The number of edges and the mean value of pixels are taken as the bases of the quad-tree space index, which improves the estimation accuracy of the atmospheric light. Finally, according to the causes of color distortion in the sky area, a restorative controlling factor is introduced to improve visual effects. Experimental results show that the mean gradient obtained by this method increases by 58.03%, and the information entropy increases by 2.88%. In particular, the running time relatively decreases by more than 50%. The proposed method achieves better restoration in terms of contrast, visibility, and color fidelity of the haze image containing complicated near and distant scenes that mixed dense haze, mist, and sky area.