基于双融合Unet抑光曲线估计的夜间交通场景增强算法

A night traffic scene enhancement algorithm based on double fusion Unet light suppression curve estimation

为解决现有增强方法在处理光源多而杂,亮度分布不均匀的夜间交通图像时易出现过度曝光、图像模糊的问题,提出了一种基于双融合Unet抑光曲线估计的夜间交通图像增强算法。首先,引入辉光分解模型对输入图像进行抑光操作,去除人造光源影响的同时会抑制图像噪声;其次,使用双融合Unet网络,设计的双融合模块在编解码过程中能够融合更多层次的特征信息,在提取光照信息时保留了更多的图像细节,预测出更贴切于输入图像的光照分布图;最后,将抑光图像,原始夜间交通图像以及其网络提取的光照参数图作为输入,利用改进的曲线估计算法对输入的夜间交通图像进行增强迭代,使得图像得到更好的视觉增强。实验结果表明,该算法在主观和客观比较下均取得最好的效果,证明了本文方法的有效性,尤其是在光源多且分布不均匀的情况下。

The existing enhancement methods were found to be inadequate for dealing with night traffic images characterized by multiple and complex light sources and uneven brightness distribution,and were prone to overexposure and image blurring.To address this problem,a night traffic image enhancement algorithm for light suppression curve estimation based on double fusion Unet was proposed.First,the glow decomposition model was introduced to suppress the light of the input image,suppressing the noise of the image while removing the influence of artificial light sources.Secondly,the double fusion Unet network was utilized,where the designed double fusion module could integrate more layers in the encoding and decoding process.The feature information preserved more image details when extracting illumination information,thereby predicting the illumination distribution map better suited for the input image.Finally,the suppression image,the original night traffic image,and the illumination parameter map extracted by the network served as input,and the improved curve estimation algorithm was applied,thus enhancing the input night traffic image and improving visual quality of the image.Experimental results showed that the proposed algorithm could outperform its counterparts in both subjective and objective comparisons,proving its effectiveness,particularly in the cases of many light sources with uneven distribution.