基于模糊集分类的单幅图像去雾算法

针对高亮度区域导致大气光强度A 计算不准确以及复原图像颜色失真影响图像去 雾效果的问题,提出一种基于模糊集分类的单幅图像去雾算法。首先从暗通道模型出发,对 图像进行分割并采用基于模糊集理论的图像分 类算法确定符合暗通道先验理论的非明亮区域,避免了天空等高亮区域对大气光强度计算 的影响；然后利用快速双边滤波方法既具有平滑效果,又具有边缘细节保持的特性,估计大 气耗 散函数,进而精确恢复场景透射率；最后由大气散射模型复原图像,并进行基于人眼视觉的 亮 度、色调的调整,修正图像中颜色失真区域,提高视觉效果。与经典算法相比,本文算法在 细节、色彩保真度具有较大改进。

Algorithm for single image haze removing based on fuzzy classification

Due to the high-bright area may lead to a bad impact on the estimation of ambient light,and the color disto rtion m ay happen in that area,which will influence the result of restoration,an improved algorithm is proposed for single image in this paper.Fir stly,the bright area and the dark area are separated into two categories based on fuzzy classification.The ambient light is estimated using da rk channel prior theory based on these bright areas to improve the accuracy.Because the bilateral filter not only has the feature of smoothing,but also has the ability to sustain the edge details o f image,the atmospheric dissipation function can be estimated through a bilatera l filter,and then we can get the atmospheric transmittance,which will be more accurate and refined c ompared with traditional methods.At last,the brightness and tone of color distortio n zone are adjusted based on the human vision to improve the quality of the whole image restored.Compared with so me classic algorithms,the time complexity and space complexity of the algorithm are decreased,while details and color fidelity are better in bright area.Experimental results show that the prop osed algorithm is effective and feasible.