基于非抽样形态小波与视觉显著计算的图像融合的研究

项目名称： 基于非抽样形态小波与视觉显著计算的图像融合的研究

项目编号： No.61271420

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 张基宏

作者单位： 深圳信息职业技术学院

项目金额： 76万元

中文摘要： 本项目以可见光与红外图像融合为研究对象，基于非抽样形态小波和视觉显著计算进行图像融合的研究。提出一种具有实时性的非抽样形态小波的构造方法，克服了形态小波的移变性；构造一种不可分离S-变换的非抽样形态小波，兼顾图像平滑和边缘保持特性；提出一种适应不同图像类别的视觉显著目标提取方法，最大限度地保留图像中的显著信息；构造基于非抽样形态小波和视觉显著计算的图像融合框架，结合视觉显著计算和PCNN，用目标的显著度构造PCNN模型中的链接强度；结合视觉显著计算和变分PDE，构造变分PDE的能量函数并求最优解；引入模糊神经网络，选择边缘保持度，区域结构相似度和可感知差异等三个指标构建反映视觉感知特性的评价指标体系，使客观评价与主观感知尽量保持一致；搭建实验平台，从主客观两个方面进行图像融合质量评价的实验研究。本项目的研究成果能够改善图像融合的视觉效果和量化指标，在军事和民用领域均有重要的应用价值。

中文关键词： 图像融合；非抽样形态小波；视觉显著计算；图像融合质量评价；

英文摘要： This project focuses on the research of visual and infrared image fusion based on undecimated morphological wavelets and visual saliency computation. It presents a method for constructing real-time undecimated morphological wavelets with the purpose to solve the problem of shift-variant property in wavelets; proposes a nonseparable undecimated morphological wavelet based on S-transform for a smooth image without the loss of edge detail; proposes a method for extracting saliency objects adaptable to various image categories, while maximizing the saliency information. A framework of image fusion is proposed based on undecimated morphological wavelet and visual saliency computation. Visual saliency computation is combined with pulse coupled neural networks(PCNN) by improving the linking strength in the PCNN model through the object's saliency. Meanwhile, visual saliency computation is combined with variant PDE to construct the energy function of variant PDE and the optimal solution is found. Using fuzzy neural network, three metrics, edge preservation, regional structural similarity and noticeable difference are combined to construct an evaluation system, which is able to reflect the visual perception characteristics, with the purpose to bring the objective evaluation into correspondence with subjective perception.

英文关键词： image fusion；undecimated morphological wavelet；visual saliency computation；image fusion quality evaluation；