引用本文:覃贺权,宋磊,王成罡,于文彬.通道修正均衡化的水下图像增强算法[J].控制理论与应用,2022,39(11):2047~2056.[点击复制]
QIN He-quan,SONG Lei,WAMG Cheng-gang,YU Wen-bin.Underwater image enhancement algorithm based on channel correction equalization[J].Control Theory and Technology,2022,39(11):2047~2056.[点击复制]
通道修正均衡化的水下图像增强算法
Underwater image enhancement algorithm based on channel correction equalization
摘要点击 1133  全文点击 342  投稿时间：2021-11-29  修订日期：2022-04-27
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DOI编号  10.7641/CTA.2022.11165
  2022,39(11):2047-2056
中文关键词  通道修正  限制对比度自适应直方图均衡化  暗通道先验  水下图像增强
英文关键词  channel correction  contrast limited adaptive histogram equalization  dark channel prior  underwater image enhancement
基金项目  国家自然科学基金项目(61773264, 61922058, 61803261, 61801295), 上海交通大学深蓝计划项目(SL2020ZD206, SL2020MS010, SL2020MS015) 资助.
作者单位邮编
覃贺权 上海交通大学 200240
宋磊 上海交通大学 
王成罡 上海交通大学 
于文彬* 上海交通大学 200240
中文摘要
      水中介质和微粒的影响导致光波传播衰减和散射, 在成像过程中水下图像会出现模糊和色偏等情况, 这些 水下成像退化的情况给水下的目标识别、目标跟踪、特征提取等应用带来困难. 针对以上问题, 本文提出了一种基 于通道修正均衡化的暗通道先验(CCD)水下图像增强算法: 首先是对色偏的水下图像进行通道修正均衡化, 利用直 方图强度中心做一个映射, 并将映射的三通道信息融合到限制对比度自适应直方图均衡化中, 改善了图像色偏和对 比度不足的情况; 其次是通过暗通道先验算法进行去模糊, 通过水下增强图像数据集的实验表明, CCD比现有算法 更有效地应对了水下图像成像退化问题, 取得了更好的图像质量指标; 此外, 在特征检测预处理步骤中, 本文方法能 够将检测特征点数提高约1.88倍.
英文摘要
      Due to the influence of light scattering and attenuation in the underwater environment, images suffer from color deviation and blur, making underwater target recognition, target tracking, feature extraction and other applications difficult. We propose a channel correction equalization based on the dark channel prior underwater image enhancement algorithm (CCD) to deal with the aforementioned problems. Firstly, the channel correction equalization is carried out for the underwater image with color deviation. A mapping is made using the histogram intensity center subsequently, and the mapped three channel information is fused into the limited contrast adaptive histogram equalization to improve the color deviation and insufficient contrast. Secondly, a dark channel prior algorithm is used to deblur. Experimental results on processing underwater image enhancement datasets demonstrate that the CCD outperforms current algorithms. In addition, in the preprocessing step of feature extraction, the number of detected feature points can increase about 1.88 times.