摘要： 在图像去雾过程中，对大气光透射率估计不准确，会降低去雾图像场景亮度，并导致天空区域出现光晕现象。为此，提出一种基于分块优化透射率与自适应优化场景亮度的图像去雾算法。根据图像有雾程度评判标准对透射率进行分块优化，结合大气光强度求解大气散射模型获得无雾图像，并通过局部自适应调整图像灰度值来提高图像场景亮度。实验结果表明，相较于引导图滤波和对比度增加算法，该算法去雾后的图像更清晰，保边效果明显，且视觉效果更佳，适用于交通监管、安全监控和目标识别等应用领域。

关键词: 图像去雾, 透射率, 分块优化, 场景亮度自适应优化, 大气散射模型

Abstract: In the image dehazing process,the estimation of the atmospheric light transmittance is inaccurate,which reduces the scene brightness and causes halo effects in the sky of the dehazed images.To this end,this paper proposes an image dehazing algorithm based on block optimization transmissivity and adaptive scene brightness optimization.This algorithm performs block optimization on transmissivity based on the foggy degree of the image,uses the intensity of airlight to solve the atmospheric scattering model to generate a dehazed image,and then adaptively performs partial adjustment to the gray value of the image to increase scene brightness.Experimental results show that compared with the guided image filtering algorithm and contrast enhancement algorithm,the image dehazed by the proposed algorithm is clearer.The edge-preserving effect is obvious,and the visual effect is better,which means the proposed algorithm is suitable for application fields such as traffic supervision,security monitoring and target recognition.

Key words: image dehazing, transmissivity, block optimization, adaptive scene brightness optimization, atmospheric scattering model

中图分类号: 

• TP391.41