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基于分层差分表达理论的图像视觉增强

耿爱辉 万春明 李毅 张云峰 曹立华 冯强

耿爱辉, 万春明, 李毅, 张云峰, 曹立华, 冯强. 基于分层差分表达理论的图像视觉增强[J]. 电子与信息学报, 2017, 39(4): 922-929. doi: 10.11999/JEIT161070
引用本文: 耿爱辉, 万春明, 李毅, 张云峰, 曹立华, 冯强. 基于分层差分表达理论的图像视觉增强[J]. 电子与信息学报, 2017, 39(4): 922-929. doi: 10.11999/JEIT161070
GENG Aihui, WAN Chunming, LI Yi, ZHANG Yunfeng, CAO Lihua, FENG Qiang. Image Visual Enhancement Based on Layered Difference Representation[J]. Journal of Electronics & Information Technology, 2017, 39(4): 922-929. doi: 10.11999/JEIT161070
Citation: GENG Aihui, WAN Chunming, LI Yi, ZHANG Yunfeng, CAO Lihua, FENG Qiang. Image Visual Enhancement Based on Layered Difference Representation[J]. Journal of Electronics & Information Technology, 2017, 39(4): 922-929. doi: 10.11999/JEIT161070

基于分层差分表达理论的图像视觉增强

doi: 10.11999/JEIT161070
基金项目: 

国家自然科学基金(61205143),吉林省科技厅重点项目(20110329)

Image Visual Enhancement Based on Layered Difference Representation

Funds: 

The National Natural Science Foundation of China (61205143), The Science and Technology Department of Jilin Province Research Funding (20110329)

  • 摘要: 视觉注意机制表明人眼趋向于关注感兴趣区域。为了实现图像视觉增强,该文提出基于2维直方图分层差分表达理论的图像增强方法。该算法首先检测图像的显著性区域,并对该区域进行分割,得到更适合人眼观测的显著图。然后统计原图中显著图对应区域的2维差分直方图,依据分层差分表达理论和考虑各层之间的内在联系,通过解线性优化问题得到显著性区域差分向量。定义原始差分向量代表原图特征,将两个向量加权相加后得到全局变换函数,重建得到视觉增强图像。实验结果表明:该方法有效地增强图像中人眼感兴趣区域对比度,提升细节信息。客观评价指标表明:与其他5种方法比较,该方法处理结果在3组实验中在保持全局亮度、提升峰值信噪比及人眼视觉系统敏感度信噪比指标优势明显。该方法增强后图像显著性区域的EME值适中,有利于视觉观测。客观指标与主观观察结果一致,表明该方法能有效改善图像视觉效果。
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出版历程
  • 收稿日期:  2016-10-12
  • 修回日期:  2017-02-15
  • 刊出日期:  2017-04-19

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