通过分析大气粒子对光线的多次散射作用，利用大气点扩散函数作为卷积核，基于暗原色图像复原理论，建立了基于多次散射的雾天成像模型，并以函数形状相似性为依据，利用广义高斯分布定量估计出大气点扩散函数核函数在图像域下的相关参数.针对传统暗原色理论以固定大小图像区域估计透射率的不足，提出了基于超像素分割获得景深一致的图像分块方案，通过区域合并，获得更为精准的天空检测效果；基于暗原色先验理论分别估计天空和非天空区域的透射率，并对天空区域的透射率进行修正，不但减少了天空色彩失真，同时也消除了复原结果的光晕现象.本文从主观和客观两个方面将所提出的去雾方法和其他算法进行了对比，结果表明，本文提出的去雾算法能够在较短的运行时间内获得对比度较高、细节信息丰富的去雾结果，具有较好的鲁棒性.

The formation pattern of hazy image based on multiple scattering, which is described by atmosphere point spread function (APSF), is modeled. The generalized Gaussian distribution was adopted to approximately formula the expression for APSF through the similarity in the shape and proper convolution operation. Aiming at enhancing the quality with reasonable time consuming, an improved dehazing method was proposed based on dark channel prior (DCP). The approach applies the superpixel algorithm to obtain image patches with similar depth for halo prevention, and based on this, the sky region was also detected more accurately by region merging. Therefore the transmission on the sky and non-sky region was separately estimated, the color distortion reduction in the sky region was achieved. Experimental results show that the proposed method can robustly recover a high-quality haze-free image with abundant details from both subjective and objective image-quality assessment.