基于景深信息的自适应Retinex图像去雾算法

Adaptive Retinex Image Defogging Algorithm Based onDepth-of-Field Information

针对传统图像增强Retinex去雾算法未考虑有雾图像景深信息,整幅图采用同一尺度复原而导致的局部颜色失真、图像细节丢失等问题,提出一种基于景深信息的自适应Retinex图像去雾算法。从场景雾的浓度与景深密切相关出发:首先利用BTS深度学习模型得到有雾图像的景深估计;然后以图像的平均梯度作为最优评价标准,对有雾图像分块处理并采取不同的高斯滤波尺度进行Retinex增强,统计出最优高斯滤波尺度与其对应的景深估计平均值;接着通过梯度下降法对统计数据进行拟合,得出景深估计与高斯滤波尺度的参数模型并将模型应用到单尺度Retinex去雾算法对有雾图像进行分块处理;最后通过计算均值和均方差,再加上一个控制图像动态的参数来实现无色偏的自适应对比度拉伸以及使用双线性插值映射使图像分块边缘过渡更加连续,从而得到增强的去雾图像。实验结果表明,经过所提算法去雾处理后的图像标准差、平均亮度、信息熵、平方梯度等评价指标均高于对比算法,实际效果对比度较高,图像细节保持完好,且抑制了过度增强。基于景深信息自适应Retinex图像去雾算法能够有效保留图像细节,颜色自然,符合人眼视觉特性,自适应程度高,明显优于传统Retinex去雾算法。

Conventional Retinex defogging algorithm does not consider the depth-of-field information of fogged images and restores the entire image at the same scale,resulting in local color distortion and loss of image details.An adaptive Retinex image defogging algorithm that uses depth-of-field information to remedy these disadvantages is proposed.As fog concentration and depth of field are closely related,the depth-of-field of foggy images is estimated using the BTS depth learning model.The average gradient of the image is considered the optimal evaluation standard,following which the foggy image is processed in blocks.The Retinex enhancement adopts different Gaussian filtering scales,and the optimal Gaussian filtering scale as well as the corresponding average depth-of-field are estimated.The parameter models of depthof-field estimation and Gaussian filtering scale are obtained via the gradient descent method and applied to the single-scale Retinex defogging algorithm to process the fogged images in blocks.Finally,by calculating the mean value and mean square deviation,and defining a parameter to control the image dynamics,we can realize adaptive contrast stretching without color deviation.Moreover,bilinear interpolation mapping can also be applied to increase the continuity of the image block edges and obtain an enhanced defogging image.Experimental results show that the standard deviation,average brightness,information entropy,square gradient,and other evaluation indicators after defogging using the proposed algorithm are better than those of the contrast algorithm.In practice,the defogged image has higher contrast,the image details remain intact,and excessive enhancement is suppressed.The adaptive Retinex image defogging algorithm based on depth-of-field information proposed in this paper has a high degree of adaptation and can effectively retain image details with natural color that conforms to the characteristics of human vision,making it superior to the conventional Retinex defogging algorithm.