基于边界限制加权最小二乘法滤波的雾天图像增强算法

Fog Image Enhancement Algorithm Based on Boundary-Limited Weighted Least Squares Filtering

针对经典的暗通道理论算法在处理雾天图像时天空区域出现光晕和亮度损失的问题,提出了一种基于边界限制加权最小二乘法滤波的雾天图像增强算法。该方法根据雾天图像的直方图特性,分割出天空区域,并求解出了全局大气背景光;根据辐射立方体法则推导出边界限制条件,得到了初始的透射率,运用加权最小二乘法滤波方法和容差机制,对透射率进行了平滑处理;利用暗通道理论的模型,求取了增强后的图像。研究结果表明,在去雾效果和图像的可视度方面,所提算法优于现有的暗通道算法。

Aiming at the problems of image hue and brightness distortion in sky regions when dealing with fog images by the classic dark channel theory algorithm,we propose a fog image enhancement algorithm based on the boundary constraint weighted least squares filtering.According to the histogram property of fog image,we reduce the boundary condition and obtain the initial transmittance.The transmission is smoothed by weighted least squares filtering method and tolerance mechanism.The enhanced image is obtained by using the model of dark channel theory.The research results show that the proposed algorithm is better than the existing dark channel algorithm in terms of dehazing effect and image visibility.