首页> 美国卫生研究院文献>Computational and Mathematical Methods in Medicine >A Fusion Algorithm for GFP Image and Phase Contrast Image of Arabidopsis Cell Based on SFL-Contourlet Transform
【2h】

A Fusion Algorithm for GFP Image and Phase Contrast Image of Arabidopsis Cell Based on SFL-Contourlet Transform

机译:基于SFL-Contourlet变换的拟南芥细胞GFP图像和相衬图像融合算法

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

A hybrid multiscale and multilevel image fusion algorithm for green fluorescent protein (GFP) image and phase contrast image of Arabidopsis cell is proposed in this paper. Combining intensity-hue-saturation (IHS) transform and sharp frequency localization Contourlet transform (SFL-CT), this algorithm uses different fusion strategies for different detailed subbands, which include neighborhood consistency measurement (NCM) that can adaptively find balance between color background and gray structure. Also two kinds of neighborhood classes based on empirical model are taken into consideration. Visual information fidelity (VIF) as an objective criterion is introduced to evaluate the fusion image. The experimental results of 117 groups of Arabidopsis cell image from John Innes Center show that the new algorithm cannot only make the details of original images well preserved but also improve the visibility of the fusion image, which shows the superiority of the novel method to traditional ones.
机译:针对拟南芥细胞的绿色荧光蛋白(GFP)图像和相衬图像,提出了一种混合的多尺度多层次图像融合算法。该算法结合了强度-色相饱和度(IHS)变换和清晰的频率局部Contourlet变换(SFL-CT),针对不同的详细子带使用了不同的融合策略,其中包括邻域一致性测量(NCM),可以自适应地找到色彩背景和背景之间的平衡。灰色结构。同时考虑了基于经验模型的两种邻域类别。引入视觉信息保真度(VIF)作为客观标准来评估融合图像。来自John Innes Center的117组拟南芥细胞图像的实验结果表明,该新算法不仅可以很好地保留原始图像的细节,而且可以提高融合图像的可见度,这表明该方法比传统方法更具优势。 。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号