基于快速清晰度估计的多聚焦图像融合

为了准确地估计源图像的清晰区域,提高多聚焦图像融合的效率,本文提出了一种新的基于清晰度估计的图像融合方法。首先,利用基于离散小波的清晰度估计方法获取源图像的聚焦区域,然后使用均值滤波和空洞填充进一步优化该聚焦区域,最后结合清晰度估计和相似性特性,将不同聚焦区域合并生成融合图像。该方法获得的融合图像在客观评价和主观质量上都优于以往基于清晰度的图像融合方法。     

基于小波变换与局部能量的多聚焦图像融合

本文提出了一种基于区域局部能量的不同聚焦点图像融合方法。本文利用小波分解,将图像分解为低频部分和高频部分,然后选择合适的比例,削弱低频部分,减小低频部分在整个图像能量中所占的比例,相对增大高频部分的比例,再重构图像。对于重构的图像,在空域中使用区域局部能量大小判定的方法,对各幅图像中的目标进行判断,并选择其中的清晰部分生成融合图像。该方法不但适用于多聚焦图像融合,而且还可以应用于特性类似的医学图像的融合。实验结果表明,该方法可以提取出多聚焦图像中的清晰目标,生成的融合图像效果优于Laplacian塔型方法和小波变换方法。     

基于双层并行PCNN和粗集理论的图像融合

为了能更好地进行多传感器图像融合,提出了一种基于双层并行PCNN和粗集理论的图像融合方法。该方法首先对两幅图像去噪,将一幅图像作为主PCNN网络的输入,另一幅图像作为从PCNN网络的输入,计算每幅图像的清晰度,分别将每幅图像的清晰度矩阵送入主从PCNN网络处理,然后根据粗集理论对原始图像分类,最后生成融合图像。该方法不仅能保留原图像信息,而且得到的融合图像清晰度高、对比度大。仿真实验结果以及与其他融合算法的比较,表明该算法的有效性和优越性。     

曝光融合在单幅图像去雾中的应用

针对雾天拍摄图像的降质现象,提出一种简单、有效的单幅图像去雾算法。首先利用暗原色先验知识估计出大气光亮度;然后根据雾天图像的成像物理模型,对每一像素的景深进行较高精度的亚采样,生成对应的虚拟无雾图像备选序列;最后,根据曝光融合算法提出的像素曝光评价指标,利用多分辨率形式的图像融合方法从备选序列中提取出清晰的无雾图像。实验结果表明,该算法既保证了复原图像清晰度,又具有较好的实时性。     

基于互补信息特征的SAR与可见光图像融合研究

由于成像方式及波谱接收段的不同,合成孔径雷达SAR（synthetic aperture radar）与可见光图像所反映的信息有很大差异,图像之间相关性弱,且互补性明显。因此在图像融合时,应该根据其互补性信息特征各取所长。在研究了一些现有融合方法的基础上,提出了一种基于互补信息特征的SAR与可见光图像融合方法。首先通过基于像素邻域的能量统计特性融合SAR与可见光图像,将SAR图像中的重要目标信息加入到可见光图像中,然后再利用小波变换进行二次融合,充分加入原始图像的边缘细节信息。实验结果表明,该融合方法有效。     

基于离散小波框架变换的彩色多聚焦图像融合算法

提出了一种基于离散小波框架变换的彩色多聚焦图像融合算法。首先求取各彩色多聚焦图像的灰度分量,再对各灰度分量进行离散小波框架变换,根据离散小波框架变换系数求取各图像中像素的清晰度指标,然后根据各图像中像素的清晰度指标对属于清晰区域的像素进行组合,从而得到融合后的图像。试验结果表明本文所提出的算法能够较好地解决彩色多聚焦图像融合问题。     

基于相对清晰度的多焦距图像融合算法

针对现有图像域融合算法难以合理度量区域清晰度,导致融合结果出现"振铃"现象的问题,提出一种基于图像区域相对清晰度的多焦距图像融合算法.该算法定义了图像区域的相对清晰度判定指标,并根据多焦距图像像素邻域的相对清晰度来逐个选取聚焦清晰的像素点从而构成融合图像.实验表明,所提算法能取得优于其它算法的融合效果;特别是,它能较好保留源图像包含的细节信息,从而显著减弱融合结果中的"振铃"现象.     

一种新的基于清晰度的多聚焦图像融合规则

针对多聚焦图像,融合过程采用计算单个象素的清晰度时考虑以该象素为中心的邻域,通过对两幅输入图像中的对应部分进行加权组合来形成融合图像。该文提出了一种新的基于清晰度的图像融合规则,即采用保形有理三次插值样条函数来构造权值进行融合,试验结果表明该文提出的方法对于严格配准的多聚焦图像的融合效果比基于多分辨率的小波分解的融合算法好。     

基于清晰度和非下采样多聚焦图像融合

根据多聚焦图像的特点提出一种基于清晰度的NSCT图像融合算法。在清晰的区域,低频系数和高频系数全部采用清晰区域的系数,而从清晰到模糊过渡的区域,低频系数则取区域方差值最大,高频子带系数取区域能量值最大。该算法与梯度金字塔算法、小波融合算法和Contourlet融合算法进行比较,实验结果证明该方法融合后的图像与源图像具有最小均方差。     

结合NSCT和压缩感知的红外与可见光图像融合

目的 红外成像传感器只敏感于目标场景的辐射,对热目标的探测性能较好,但其对场景的成像清晰度低;可见光图像只敏感于目标场景的反射,场景图像较为清晰,但目标不易被清晰观察.因而将两者图像进行融合,生成具有较好目标指示特性和可见光图像的清晰场景信息,有利于提高目标识别的准确性和降低高分辨图像传感器研究的技术难度.方法 结合非下采样contourlet变换 (NSCT)和压缩感知的优点,研究一种新的红外与可见光图像融合方法.首先对两源图像进行NSCT变换,得到一个低频子带和多个不同方向、尺度的高频子带.然后对两低频子带采用压缩感知理论获得测量向量,利用方差最大的方法对测量向量进行融合,再进行稀疏重建;高频子带采用区域能量最大的方法进行融合.最后利用NSCT逆变换获得融合图像.结果 为了验证本文方法的有效性,与其他几种方法相比较,并利用主观和客观的方法对融合结果进行评价.提出的新方法融合结果的熵、空间频率、方差明显优于其他几种方法,运行时间居中.主观上可以看出,融合结果在较好地显示目标的基础上,能够较为清晰地保留场景图像的信息.结论 实验结果表明,该方法具有较好的目标检测能力,并且方法简单,具有较强的适应性,可应用于航空、遥感图像、目标识别等诸多领域.     

基于SML特征检测的RPCA域多聚焦图像融合

针对多聚焦图像融合中难以有效检测聚焦点的问题,提出了一种基于鲁棒主成分分析（RPCA）和区域检测的多聚焦图像融合方法。将RPCA理论运用到多聚焦图像融合中,把源图像分解为稀疏图像和低秩图像;对稀疏矩阵采用区域检测的方法得到源图像的聚焦判决图;对聚焦判决图进行三方向一致性和区域生长法处理得到最终决策图;根据最终决策图对源图像进行融合。实验结果表明,在主观评价方面,所提出的方法在对比度、纹理清晰度、亮度等几方面都有显著的提高;在客观评价方面,用标准差、平均梯度、空间频率和互信息四项评价指标说明了该方法的有效性。     

Optimized ensemble decision-based multi-focus imagefusion using binary genetic Grey-Wolf optimizer in camera sensor networks

Modern developments in image technology enabled easy access to an innovative type of sensor-based networks, Camera or Visual Sensor Networks (VSN). Nevertheless, more sensor data sources bring about the problem of overload information. To solve this problem, some researchers have been carried out on the techniques to counteract the data overload caused by sensors without losing useful data. The aim of fusion in each application is to combine images from several sensors, which leads to the decreased amount of input image data, producing an image with more accurate data. This paper proposes a noisy feature removal scheme for multi-focus image fusion combining the decision information of optimized individual features. The proposed scheme is developed in two main steps. In the first step, the diverse types of features are extracted from each block of input blurred images. The useful information of these individual features indicates which image block is more focused among corresponding blocks in source images. After that, noisy features are removed using binary Genetic Grey wolf optimizer (GGWO) algorithm. The ensemble decision based on individual features is employed to fuse blurred images in the second step. The experimentation is evaluated on different multi-focus images and it reveals that GGWO based proposed method performs better visual quality than other methods.     

Surface area-based focus criterion for multi-focus image fusion

Nowadays image processing and machine vision fields have become important research topics due to numerous applications in almost every field of science. Performance in these fields is critically dependent to the quality of input images. In most of the imaging devices, optical lenses are used to capture images from a particular scene. But due to the limited depth of field of optical lenses, objects in different distances from focal point will be captured with different sharpness and details. Thus, important details of the scene might be lost in some regions. Multi-focus image fusion is an effective technique to cope with this problem. The main challenge in multi-focus fusion is the selection of an appropriate focus measure. In this paper, we propose a novel focus measure based on the surface area of regions surrounded by intersection points of input source images. The potential of this measure to distinguish focused regions from the blurred ones is proved. In our fusion algorithm, intersection points of input images are calculated and then input images are segmented using these intersection points. After that, the surface area of each segment is considered as a measure to determine focused regions. Using this measure we obtain an initial selection map of fusion which is then refined by morphological modifications. To demonstrate the performance of the proposed method, we compare its results with several competing methods. The results show the effectiveness of our proposed method.     

Image matting for fusion of multi-focus images in dynamic scenes

In this paper, we address the problem of fusing multi-focus images in dynamic scenes. The proposed approach consists of three main steps: first, the focus information of each source image obtained by morphological filtering is used to get the rough segmentation result which is one of the inputs of image matting. Then, image matting technique is applied to obtain the accurate focused region of each source image. Finally, the focused regions are combined together to construct the fused image. Through image matting, the proposed fusion algorithm combines the focus information and the correlations between nearby pixels together, and therefore tends to obtain more accurate fusion result. Experimental results demonstrate the superiority of the proposed method over traditional multi-focus image fusion methods, especially for those images in dynamic scenes.     

利用局部极值点特性的多聚焦图像快速融合

目的：多聚焦图像融合技术一个关键问题是如何准确地判断待融合图像的清晰度。本文提出了基于归一化结构极值点数目的清晰度判断准则。方法：本文基于图像的局部极值点特性,定义了归一化结构极值点数目这个指标作为清晰度判断准则,同时还给出了利用该准则和融合决策矩阵快速估计技术的多聚焦图像快速融合方法。结果：利用本文提出的清晰度判断准则和融合方法,实验表明上述问题得到了很好的解决。结论：本文提出了一个新的图像清晰度判断准则,该准则判断准确率高,且对脉冲噪声有好的鲁棒性。通过与传统融合方法对两组实验图像融合结果的主客观比较表明,该方法的融合速度和效果比现有多聚焦图像融合方法有明显提高。     

基于超分辨率的多聚焦图像融合算法研究

针对传统小波变换在图像融合过程中出现边缘模糊、图像失真等问题,提出了一种基于超分辨率的多聚焦图像融合算法。对所有的源图像进行了双三次插值的单帧超分辨率处理,增强源图像对比度等细节信息,采用的源图像为分别进行左右聚焦处理的同一场景中的两幅图像。对这些高分辨率源图像实现了平稳小波变换（SWT）,并将源图像划分为四个子带。针对这些子带所包含源图像细节信息混乱、结构信息冗余等问题,采用了主成分分析（PCA）,分别选取源图像各子带的最大信噪比进行图像融合。利用逆平稳小波变换（ISWT）对融合子带进行重构,得到高质量融合图像。为了评定融合后图像的质量,选择了无参考图像和全参考图像的两种度量方法来检测融合后的图像质量。经实验结果表明,提出的算法克服了传统小波变换算法在图像融合上的缺点,具有边缘清晰、视觉感知好、清晰度好、失真小等优点。     

一种自适应的多聚焦图像融合方法

为了对不同的多聚焦图像进行有效融合，提出了一种小波域中基于区域特征的自适应多聚焦图像融合方法。该方法首先对参加融合的两幅图像进行小波分解，然后针对低频部分，在保留源图像共同特征的基础上，将待融合的两图像各自所具有的特征添加到融合图像中，而对于高频部分，则根据区域的小波能量进行融合；最后通过小波逆变换来重构融合图像。该方法不仅能够完全自适应地对多聚焦图像进行有效的融合，而且对于各种不同的源图像具有通用性。实验表明，该算法能够得到良好的融合效果，是一种有效的多聚焦图像融合方法。     

一种新的基于小波变换的多聚焦图像融合算法

提出了一种基于小波变换的多聚焦图像融合算法（简记为WMFFA）。首先综合源图像求得参照图像,并对源图像和参照图像分别做小波变换,将其分解成低频和高频图像,然后求出源图像的每个小波系数与参照图像中对应位置上的小波系数之间的距离,按照距离特征从源图像中选取小波系数,组成融合后的小波系数并进行小波反变换,得到融合图像。对提出的融合算法性能进行了评价与分析,结果表明提出的算法对多聚焦图像融合是有效的。     

Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure

Multi-focus image fusion aims to extract the focused regions from multiple partially focused images of the same scene and then combine them together to produce a completely focused image. Detecting the focused regions from multiple images is key for multi-focus image fusion. In this paper, we propose a novel boundary finding based multi-focus image fusion algorithm, in which the task of detecting the focused regions is treated as finding the boundaries between the focused and defocused regions from the source images. According to the found boundaries, the source images could be naturally separated into regions with the same focus conditions, i.e., each region is fully focused or defocused. Then, the focused regions can be found out by selecting the regions with greater focus-measures from each pair of regions. To improve the precision of boundary detection and focused region detection, we also present a multi-scale morphological focus-measure, effectiveness of which has been verified by using some quantitative evaluations. Different from the general multi-focus image fusion algorithms, our algorithm fuses the boundary regions and non-boundary regions of the source images respectively, which helps produce a fusion image with good visual quality. Moreover, the experimental results validate that the proposed algorithm outperforms some state-of-the-art image fusion algorithms in both qualitative and quantitative evaluations.