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作 者:邹群艳 孙小迎[2] ZOU Qunyan;SUN Xiaoying(School of Information and Artificial Intelligence,Nanchang Institute of Science and Technology,Nanchang,Jiangxi 330108,China;Development Planning Division,Nanchang Institute of Science and Technology,Nanchang,Jiangxi 330108,China)
机构地区:[1]南昌工学院信息与人工智能学院,江西南昌330108 [2]南昌工学院发展规划处,江西南昌330108
出 处:《光电子.激光》2024年第8期810-816,共7页Journal of Optoelectronics·Laser
基 金:江西省教育厅科学技术研究项目(GJJ212520,GJJ191095,GJJ191101,GJJ202509);江西省教育科学“十四五”规划2023年度一般课题(23YB327)资助项目。
摘 要:为了进一步有效地提升低照图像的亮度、对比度和清晰度,提出了加权平台直方图均衡化的低照图像增强方法。该方法充分利用HSV颜色空间中明度分量V与色调H和饱和度S的独立性,将图像转换到HSV颜色空间;用具有良好边缘保持能力的双边滤波,将明度分量V通过Retinex算法分解为光照图像L和反射图像R;对光照图像L进行加权双平台直方图均衡化,其中,上、下平台阈值由正态分布的3σ原则自适应地确定,加权系数反比于灰度级对应的直方图频次。实验结果显示,相对于部分现有方法,本文方法增强后的图像效果较好,对应的信息熵和平均梯度分别比现有方法高出0.35和12以上,证明了本文方法具有更优的低照图像增强性能。To further improve the brightness,contrast and definition of the low light image effectively,a low light image enhancement method based on weighted plateau histogram equalization is proposed.Taking full advantage of that the lightness component V is independent of the hue H and the saturation S in the HSV color space,this method converts the image to HSV color space,and by the bilateral filtering which has good edge-preserving ability,the lightness component V is decomposed into illumination image L and reflection image R with Retinex algorithm.The illumination image L is subjected to the weighted dual plateau histogram equalization,in which,the upper plateau threshold and the lower plateau threshold are determined adaptively by the principle of 3σ in normal distribution,and the weighting coefficient is inversely proportional to the histogram frequency of the gray level.The experimental results show that compared with some existing methods,the effect of image enhanced by the proposed method is better,and the corresponding information entropy and average gradient are higher than the existing methods by more than O.35 and 12,respectively,which proves that the proposed method has better low light image enhancement performance.
关 键 词:低照图像增强 加权平台直方图 HSV空间 3σ原则 RETINEX
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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