基于多尺度对比度塔的图像融合方法及性能评价

给出了一种新的基于对比度塔形分解的分层图像融合方法,其基本思想是先对源图像进行对比度塔形分解,其次,按照融合规则,采用基于区域特性量测的加权算子去构造融合图像对应的对比度金字塔,最后,通过逆塔形变换重构融合图像。该方法被成功地用于图像的融合处理,此外,利用熵,交叉熵,互信息,均方根误差,峰值信噪比等参量,对该融合方法的融合性能进行了评价与分析,实验结果表明,该融合方法是十分有效的。     

基于亚像素区域加权能量特征的多尺度图像融合算法

对矩形和圆形区域中各像素进行亚像素划分,确定各亚像素的权值,得到基于哑像素的综合加权区域能量.融合箅法首先对源图像进行金字塔分解,然后对金字塔的高频细节分量使用基于哑像素加权区域能量特征的融合规则取大,对低频粗糙分量取平均.得到融合图像的塔形分解,最后重构融合图像.仿真结果表明,新算法融合效果较常规的区域能量特征作为融合规则的多分辨率图像融合算法效果更好,从清晰度和熵的评价来看,提高了融合图像的品质.     

基于嵌入式多尺度分解和可能性理论的多波段纹理图像融合

将多尺度变换和“高频取大、低频加权平均”融合规则相结合是融合双波段图像的有效方法。但用该类方法融合多波段图像时,序贯式加权常常会导致原图像间固有的差异信息在融合图像中被弱化,从而影响后续的目标识别和场景理解。该问题在融合具有纹理特征的多波段图像时更为突出。为此,提出了一个基于嵌入式多尺度分解和可能性理论的多波段纹理图像融合新方法。首先,利用一种多尺度变换方法把多波段原图像分别分解为高频和低频成分,并对多波段图像中标准差最大的一幅原图像的低频成分利用另一种多尺度方法进行分块,再以该分块图像的大小和位置为标准对其余波段的原图像进行分块。然后,基于可能性理论的相关融合规则逐一融合对应的多波段块图像,再把块融合图像进行拼接,以拼接结果作为低频融合图像。最后,将该低频融合图像和利用取大规则融合得到的高频成分一起通过多尺度逆变换获得最终的融合图像。这种方法不仅将像素级和特征级融合方法综合在一起, 而且将空间域和变换域技术综合在一起, 并通过对大小块采用不同融合规则解决了目标边缘的锯齿效应问题。实验表明该方法效果显著。     

基于小波变换的多尺度图像融合增强算法

为解决原始单源图像缺乏多尺度细节信息和图像融合后出现的噪声问题,提出了一种基于小波变换的多尺度图像融合增强算法,根据不同频率子带分量采用不同融合规则的思想,对高频子带提出了三种融合方法,同时构建了一种新颖的多尺度残差金字塔空间并将其参与融合过程,以减少融合噪声.多种小波分解和对比实验结果表明,提出的小波多尺度融合增强算...     

图像融合效果评价方法研究

以IKONOS影像为数据源，对IHS、PC，Gram—Schmidt和基于亮度调节的平滑滤波（SFIM）四种融合算法进行融合处理，对其结果进行了评价．结果表明，定性比较主观性较强，定量比较方法虽多，但大都有一定的局限性，不同谱段的计算结果总有些差异．因此，定量评价时应该多选一些波段与评价方法才能获得比较客观的评价结果．光谱曲线的比较不失为一种比较好的方法，在对光谱曲线进行比较时本文引入了光谱角度和几何距离两种方法进行定量的比较．     

基于多尺度形态学滤波器的图像融合新方法

提出了一种保持图像细节和高抗噪性的图像融合新方法。这种方法首先对源图像进行多尺度形态学开闭滤波,得到源图像的低频平滑图像;然后应用多尺度Top-hat变换和Bottom-hat变换来提取小于相应尺度的图像细节特征。因为在较小的尺度特征中包含噪声颗粒的可能性较大,据此修正了Top-hat变换和Bottom-hat变换的相应系数;最后对以上两步骤得到的低频平滑图像和多尺度高频细节图像分别进行图像融合,应用形态学重建过程生成融合图像。实验表明,这种融合方法具有图像细节保持完整和噪声消除效果好的优点,处理效果优于传统的图像融合方法。     

一种基于小波变换的多尺度多算子图像融合方法

图像融合技术以其综合多传感器信息的优越性日益受到诸多领域的重视。为了使其应用在医学、遥感、计算机视觉、气象预报、军事目标识别等方面更迅速、深入地开展,有效、实用的融合算法是至关重要的。本文在小波变换金字塔结构的基础上,提出了一种多尺度多算子融合方法,对热红外图像和可见光图像的融合进行了研究。结果融合效果很好,目标和背景区别显著,而且边缘不突兀。由于这种方法对小波分解的层数要求不高,因此计算量不大,便于并行处理及硬件实时化实现,具有广阔的应用前景。     

基于迭代导向滤波与多视觉权重信息的红外与 可见光图像融合

为了充分利用源图像重要特征,提出了一种基于迭代导向滤波与多视觉权重信息的红外与可见光图像融合算法.首先,通过一种迭代导向滤波器将输入图像分解为基础层与细节层;其次,利用边角信息、清晰度与对比度来综合确定二进制权重系数,再选择导向滤波对其优化,进一步去除噪声并抑制伪影的产生;最后,应用重构准则对基础层与细节层进行组合,得到融合图像.实验结果表明,与其它多尺度分解相比,该方法具有尺度感知特性,可以更好地分离空间重叠的特征,不仅可以使夜视融合图像的细节信息更突出,还能够有效地抑制伪影.     

基于区域特性量测的图像融合方法

在图像融合过程中，融合规则及融合算子的选择对于融合的质量至关重要，也是图像融合中至今尚未很好解决的难点问题。本文给出了一种新的融合规则——基于区域特性量测的融合规则，即在对某一分解层图像进行融合处理时，为了确定融合后的像素不仅要考虑参加融合图像中对应的各像素，而且要考虑参加融合像素的局部领域。     

二尺度分解和显著性提取的红外与可见光图像融合

为增强红外与可见光图像融合可视性,克服红外与可见光图像融合结果中细节丢失、目标不显著和对比度低等问题,提出一种基于二尺度分解和显著性提取的红外与可见光图像融合方法。首先,以人类视觉感知理论为基础,针对人眼对图像不同区域敏感性不同特性,在跨模态融合任务中需要对源图像进行不同层次分解,避免高频分量和低频分量混合减少光晕效应,采用二尺度分解方法对源红外与可见光图像进行分解,分别获取各自的基本层和细节层,该分解方法能够很好的表达图像并具有很好的实时性;然后,针对基本层的融合提出一种基于视觉显著图(VSM)的加权平均融合规则,VSM方法能够很好提取源图像中的显著结构和目标。采用基于VSM的加权平均融合规则对基本层融合,能够有效避免直接使用加权平均策略而导致对比度损失,使融合图像可视性更好;针对细节层的融合,采用Kirsch算子对源图像分别提取得到显著图,然后通过VGG-19网络对显著图进行特征提取获取权值图,并与细节层进行融合,得到融合的细节层;Kirsch算子能在八个方向上快速提取图像边缘,显著图中将包含更多边缘信息和更少噪声,且VGG-19网络能够提取到图像更深层特征信息,获取的权值图中将包...     

Multi-Focus Image Fusion Based on Multi-Scale Generative Adversarial Network

The methods based on the convolutional neural network have demonstrated its powerful information integration ability in image fusion. However, most of the existing methods based on neural networks are only applied to a part of the fusion process. In this paper, an end-to-end multi-focus image fusion method based on a multi-scale generative adversarial network (MsGAN) is proposed that makes full use of image features by a combination of multi-scale decomposition with a convolutional neural network. Extensive qualitative and quantitative experiments on the synthetic and Lytro datasets demonstrated the effectiveness and superiority of the proposed MsGAN compared to the state-of-the-art multi-focus image fusion methods.     

Multi-Focus Image Fusion Method Based on Multi-Scale Decomposition of Information Complementary

Multi-focus image fusion is an important method used to combine the focused parts from source multi-focus images into a single full-focus image. Currently, to address the problem of multi-focus image fusion, the key is on how to accurately detect the focus regions, especially when the source images captured by cameras produce anisotropic blur and unregistration. This paper proposes a new multi-focus image fusion method based on the multi-scale decomposition of complementary information. Firstly, this method uses two groups of large-scale and small-scale decomposition schemes that are structurally complementary, to perform two-scale double-layer singular value decomposition of the image separately and obtain low-frequency and high-frequency components. Then, the low-frequency components are fused by a rule that integrates image local energy with edge energy. The high-frequency components are fused by the parameter-adaptive pulse-coupled neural network model (PA-PCNN), and according to the feature information contained in each decomposition layer of the high-frequency components, different detailed features are selected as the external stimulus input of the PA-PCNN. Finally, according to the two-scale decomposition of the source image that is structure complementary, and the fusion of high and low frequency components, two initial decision maps with complementary information are obtained. By refining the initial decision graph, the final fusion decision map is obtained to complete the image fusion. In addition, the proposed method is compared with 10 state-of-the-art approaches to verify its effectiveness. The experimental results show that the proposed method can more accurately distinguish the focused and non-focused areas in the case of image pre-registration and unregistration, and the subjective and objective evaluation indicators are slightly better than those of the existing methods.     

基于二代curvelet变换的图像融合研究

曲波(Curvelet)作为一种新的多尺度分析方法比小波更加适合分析二维图像中的曲线或直线状边缘特征,而且具有更高的逼近精度和更好的稀疏表达能力.将curvelet变换引入图像融合,能够更好地提取原始图像的特征,为融合图像提供更多的信息.第二代curvelet理论的提出也使得其理论更易理解和实现.因此,提出了一种基于第二代curvelet变换的图像融合方法,首先将图像进行curvelet变换,然后在相应尺度上利用融合规则将curvelet系数融合,最后进行重构得到融合结果.对多聚焦图像进行了实验,采用均方误差、偏差指数和相关系数对融合结果进行了客观评价,并与基于小波变换的融合进行了比较,实验结果表明该方法除分解2层时与小波性能相当,取其他分解层数时均获得更好的融合效果.     

Multi-Scale Mixed Attention Network for CT and MRI Image Fusion

Recently, the rapid development of the Internet of Things has contributed to the generation of telemedicine. However, online diagnoses by doctors require the analyses of multiple multi-modal medical images, which are inconvenient and inefficient. Multi-modal medical image fusion is proposed to solve this problem. Due to its outstanding feature extraction and representation capabilities, convolutional neural networks (CNNs) have been widely used in medical image fusion. However, most existing CNN-based medical image fusion methods calculate their weight maps by a simple weighted average strategy, which weakens the quality of fused images due to the effect of inessential information. In this paper, we propose a CNN-based CT and MRI image fusion method (MMAN), which adopts a visual saliency-based strategy to preserve more useful information. Firstly, a multi-scale mixed attention block is designed to extract features. This block can gather more helpful information and refine the extracted features both in the channel and spatial levels. Then, a visual saliency-based fusion strategy is used to fuse the feature maps. Finally, the fused image can be obtained via reconstruction blocks. The experimental results of our method preserve more textual details, clearer edge information and higher contrast when compared to other state-of-the-art methods.     

灰值数学形态学的二值算法及其光学实现

在灰值数学形态学的阴影算法的基础上，提出了采用表面点集代替阴影来代表灰值结构核的改进算法，大大降低了二值邻域互连的数量。光学实现采用一个光学非相干相关器与阈值器件结合的二值数学形态学系统，并设计了一种数字空间编码技术使这二维系统能实现算法要求的有限三维操作。     

基于形态学4子带分解金字塔的图像融合

提出了一种基于数学形态学滤波的多分辨力图像融合。这种融合方法使用了形态学开闭运算构造了低通与高通滤波器,将原始图像分解为4子带图像金字塔和4子带方向衬比度图像金字塔。然后利用方向衬比度和区域标准差进行图像融合得到融合的4子带图像金字塔,最后应用子带图像重构得到融合图像。融合实验表明,该方法优于传统的形态学金字塔图像融合,衬比度金字塔图像融合和小波分解图像融合。     

Parallel Image Processing Algorithms Based on Mathematical Morphology Using a Multiple-Imaging System

Morphological techniques are applied to binary image processors in a multiple-imaging optical system and the following algorithms are proposed: performing morphological basic operations, extraction of boundary lines, detection of characteristic points in small-scale patterns, elimination of salt noises with few pixels, and smoothing of boundaries. Techniques for processing a complicated binary image are demonstrated using the algorithms and a hybrid parallel computing system with a simple optical multiple-imaging system and a personal computer.     

Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion

In this paper, to improve the slow processing speed of the rule-based visible and NIR (near-infrared) image synthesis method, we present a fast image fusion method using DenseFuse, one of the CNN (convolutional neural network)-based image synthesis methods. The proposed method applies a raster scan algorithm to secure visible and NIR datasets for effective learning and presents a dataset classification method using luminance and variance. Additionally, in this paper, a method for synthesizing a feature map in a fusion layer is presented and compared with the method for synthesizing a feature map in other fusion layers. The proposed method learns the superior image quality of the rule-based image synthesis method and shows a clear synthesized image with better visibility than other existing learning-based image synthesis methods. Compared with the rule-based image synthesis method used as the target image, the proposed method has an advantage in processing speed by reducing the processing time to three times or more.     

一种基于提升小波的图像融合技术研究

针对红外与可见光图像进行提升小波变换后低频图像的特点,提出了一种低频分量的融合算法,高频分量采取邻域方差取大为准则进行融合,然后进行提升小波逆变换得到融合图像。通过与传统小波融合方法进行比较,并引入信息熵、清晰度、Xydeas-Petrovic客观性能指标对融合后的图像进行分析。实验结果表明不论从视觉效果还是从客观性能指标上,该算法都优于传统的融合方法。