基于偏振度优化与大气光校正的图像去雾

Image dehazing based on polarization optimization and atmosphere light correction

为提高偏振去雾算法对雾气场景的恢复能力,提出一种偏振度优化与大气光校正的偏振图像去雾算法。首先,依据雾气场景亮度分布,使用导向滤波将雾气图像分解为亮面残差和暗面残差;其次,扩大亮面残差对应的偏振度值,削减暗面残差对应的偏振度值以优化偏振度,该偏振度可将大气光图像模糊;最后,利用偏振度在亮面和暗面残差上的差异,对大气光强度进行校正,以使其随雾气的变化规律满足大气退化模型。实验结果表明:本文算法的去雾图像相较原雾气图像,对比度提高3.07倍、信息熵提高9.21%、标准差提高61.86%。且在不同浓度模拟雾气环境中,本文算法都有较为优异的SSIM、PSNR和CIEDE2000。相较于现有先进图像去雾算法,本文算法去雾效果明显,可以有效地复原雾气中场景的细节信息。

To improve the recovery ability of polarization dehazing algorithms in fog scenes,a polarization image dehazing algorithm based on polarization optimization and atmospheric light correction is proposed.First,according to the brightness distribution of the fog scene,the fog image was decomposed into bright residuals and dark residuals via guided filtering.Second,to optimize the degree of polarization,the degrees of polarization corresponding to the bright and dark residuals were increased and decreased,respectively.This optimized degree of polarization can blur the atmospheric light image.The difference value of the degree of polarization in the residuals was used to correct the atmospheric light for ensuring its intensity range met the atmospheric degradation model.Experiments indicated that the contrast ratio was 3.07 times that in original hazy images after dehazing and that the entropy and standard deviation of dehazed images were increased by 9.21%and 61.86%,respectively.In environments with different concentrations of simulated fog,the proposed algorithm achieved excellent SSIM,CIEDE2000,and PSNR values.Compared with the state-of-art dehazing algorithms,the effect of the proposed algorithm was obvious,and it recovered the scene details efficiently.