基于多尺度Retinex的道路交通模糊图像增强方法

Road Traffic Fuzzy Image Enhancement Method Based on Multi-scale Retinex

由于车速过快或成像距离较远,道路交通监控图像模糊,在提取图像时存在不完整、误提取及对比度低等问题。为此,提出一种基于多尺度Retinex的道路交通模糊图像增强方法。利用剪切波变换法将图像分解成低频分量与高频分量2个部分。采用线性映射将低频系数映射到特定区间内做正规化处理,并利用多尺度Retinex增强低频分量;利用鲁棒中值法与硬阈值收缩法除去高频分量中各尺度、各方向上的噪声和光照干扰。使用模糊对比度、线性隶属度函数融合2个部分的图像,最终得到增强后的清晰图像。实验结果表明,所提方法可以优化图像视觉效果,突出图像细节特征,具有良好抑噪性能;与对比生成式对抗网络方法、特征金字塔方法相比,所提方法增强后的图像的峰值信噪比、结构相似性、信息熵、平均梯度及空间频率均更优。

Due to the fast speed or long imaging distance,the road traffic monitoring image is blurred,and there are some problems such as incomplete,wrong extraction and low contrast when extracting the image.Therefore,a road traffic fuzzy image enhancement method based on multi-scale Retinex is proposed.The image is decomposed into low frequency component and high frequency component by Shearlet transform.Linear mapping is used to map the low-frequency coefficients to a specific interval for normalization,and multi-scale Retinex is used to enhance the low-frequency components.Robust median method and hard threshold shrinkage method are used to remove noise and illumination interference in each scale and direction of high frequency component.The fuzzy contrast and linear membership function are used to fuse the two parts of the image,and finally the enhanced clear image is obtained.The experimental results show that the proposed method can optimize the image visual effect,highlight the image detail features,and has good noise suppression performance.Compared with the generative adversarial network method and the feature pyramid method,the peak signal-to-noise ratio,structural similarity,information entropy,average gradient and spatial frequency of the image are better.