椭圆方向窗与新轮廓波应用于多聚焦融合算法研究

针对光学显微镜景深扩展中的多聚焦图像融合问题,提出了一种基于方向特性的新轮廓波域多聚焦图像融合算法。该算法首先对图像进行新轮廓波变换(New Contourlet Transform with Sharp Frequency Localization,NCT-SFL),分解得到不同尺度、不同方向的高低频系数,低频系数融合使用算术平均法,高频系数融合分为两步:先采用改进拉普拉斯能量和(Sum Modified Laplacian,SML) 提取特征值;然后定义新的与方向分解一一对应的椭圆方向窗,在确定的椭圆窗参数下,对提取的特征值进行累加并以此为依据对高频系数进行融合,最后通过反新轮廓波变换得到融合图像。在实验部分用定义的新的客观评价指标互结构信息(Mutual Structural Information,MSI)对融合算法进行了评价,结果表明:对多聚焦图像本文所提方法比新轮廓波域方形窗算法MSI提高了2.94%,比Contourlet域方形窗与椭圆窗算法MSI分别提高了10.44%和8.56%。说明本文方法能提取源图像中更多的清晰信息到融合图像,是一种有效的景深扩展手段。      

多特征结合引导滤波的多聚焦图像融合

提出一种多特征结合引导滤波的图像融合算法,使用多个特征,计算各特征的置信图,将各特征的优势相结合,提升整体融合的质量。具体步骤如下:首先计算源图像的多类特征,各特征经过引导滤波后计算初始决策图和置信图,然后通过形态学方法去除小连通区域后,再次使用引导滤波得到最终决策图,最后使用置信图对最终决策图加权,与源图像相乘再相加得到最终的融合图像。实验结果表明,该算法对残差图的主观分析和客观融合质量评价指标的定量分析,较当前传统多聚焦图像融合算法均有提升。     

基于双树复小波变换的多聚焦图像融合算法研究

针对传统离散小波变换图像融合算法在图像特征表达上存在的不足,采用具有近似平移不变性和方向选择性的双树复小波变换对多聚焦图像进行多分辨率分解与重构,并对高频子带应用基于局部相似性度量的加权平均与选择相结合的融合策略。通过对实验结果的主客观分析,证明该方法得到的融合图像效果较好,优于基于传统小波变换的算法和局部能量取大的算法。     

基于离散Walsh-Hadamard变换和引导滤波的多聚焦图像融合

多聚焦图像融合作为一种有效的信息融合方法,在图像处理和计算机视觉领域引起了越来越多的关注.提出了一种基于离散Walsh-Hadamard变换(DWHT)和引导滤波的多聚焦图像融合算法.首先,提出了一种新的聚焦区域检测方法,该方法运用DWHT并计算L1范数得到初始决策图;然后,运用数学形态学方法和引导滤波优化生成最终决策...     

基于小波变换和彩色变换的多聚焦图像融合

提出一种结合小波变换及彩色空间变换的多聚焦图像融合方法。首先把彩色图像从RGB空间变换到YIQ空间,将颜色分量与亮度分量进行分离,从而克服RGB空间各颜色分量的相关性造成融合后图像颜色信息的丢失和错乱;接着,将待融合的多聚焦图像进行小波分解以刻画图像的多尺度信息,在小波域实现融合处理。在融合策略的选取上,对高频分量与低频分量分别采取局部方差与局部梯度最大的融合规则,同时以亮度分量Y作为衡量标准,通过一致性检测对融合系数做进一步的优选,以保持融合后图像的区域连续性。实验表明,该方法的融合结果无论在视觉质量及定量指标上都明显优于传统方法。     

基于NSCT-PCNN的多聚焦红外图像融合

由于红外镜头景深的限制,为获得场景中所有区域都聚焦清晰的图像,提出一种在非下采样轮廓波变换(NSCT)域结合改进的脉冲耦合神经网络(PCNN)的多聚焦红外图像的融合算法.首先通过NSCT将图像分解为不同尺度和方向的子带;低频子带图像利用基于一致性验证的特征选择规则进行融合;对于高频子带,采用改进的空域频率激励PCNN模型,选择点火时间最大的系数进行融合;最后通过NSCT反变换得到融合图像.通过多组同一场景不同聚焦位置下的红外图像融合实验,结果分析表明该算法能从源图像中获得更多的信息,更好地保留源图像的边缘信息,融合效果优于相关算法.     

多聚焦显微图像融合算法

在显微成像过程中,受系统景深限制,沿光轴方向不同层面之间聚焦位置存在显著差异,同时不同层面的显微图像存在部分聚焦区域重叠,现有多聚焦融合算法往往无法并行提取和融合多幅显微图像中最清晰的聚焦部分。提出一种多聚焦显微图像融合算法,首先构造了一种类高斯四邻域梯度算子并结合快速引导滤波,实现高频聚焦信息的提取;同时针对大视场显微图像序列中存在聚焦信息重叠、像素数量大的情况,引入了一种小区域聚焦度量方法,提高了对聚焦清晰区域高频信息提取的能力,实现了多图最佳聚焦点的融合。拍摄3组包括4 mm和2 mm对角线视场的多聚焦显微图像序列进行测试,相较5种常用多聚焦图像融合算法,所提算法的峰值信噪比平均提高了2.4772,结构相似性指数达0.9400以上,对聚焦清晰区域有更好的融合效果,融合图像细节丰富且清晰度高,能够满足大视场多聚焦显微图像融合的准确性要求。     

基于非抽样复轮廓波变换的图像去噪算法研究

将二维双树复小波变换(DT-CWT)与非抽样方向滤波器组(NSDFB)相结合,构造一种新的非抽样复轮廓波变换(NSCCT),并对其平移不变性作了相应证明。同时利用对称的正态逆高斯(NIG)分布先验概率模型和贝叶斯最小均方算法,提出一种基于NSCCT的图像去噪算法。实验结果表明,本文构造的NSCCT能够有效地抑制伪Gibbs现象,并且具有更丰富的方向分量,因而在图像的细节和纹理表现方面有一定的优势。     

结合SML与差分图像的多聚焦图像融合算法

针对传统多聚焦图像融合算法中融合边缘出现模糊、伪影等问题,提出了一种结合改进拉普拉斯能量和（SML）与差分图像的多聚焦图像融合算法。首先,为了提取源图像的聚焦特征信息,分别通过SML和滤波差分进行聚焦度量,再采用引导滤波获得更多的细节特征;接着,利用像素最大值规则生成初始融合决策图,再对初始融合决策图进行小区域去除消除因聚焦和散焦区域相似造成的噪点,并对融合决策图进行不一致处理,获得更精确的聚焦区域;最后,由逐像素加权平均规则,得到融合图像。实验结果表明,所提出的算法在主观视觉效果和客观评价指标上均优于对比算法,互信息、特征互信息、图像梯度特征在彩色图像上分别提高了0.17%、0.38%和0.11%,在灰度图像上分别提高了0.7%、0.69%和0.33%,并且平均运行时间少于0.5 s,具有较高的计算效率。此外,该算法能够较好地保留源图像信息的完整性,融合图像边缘清晰、无伪影。     

基于分块和Contourlet变换相结合的多聚焦图像融合算法

分块融合易产生块效应,而Contourlet变换融合方法不具备平移不变性.结合以上方法的各自优势,提出1种新的基于Contourlet变换与块分割相结合的多聚焦图像融合方法.首先对图像进行分块并评价子块清晰度,然后通过建立标记矩阵,确定图像清晰区域、模糊区域及两者的交界区域.对非交界区域,直接选取清晰子块的像素值作为融合后的像素值;对介于清晰与模糊之间的交界区域,采用1种新的基于Contourlet变换的融合算法完成融合.实验结果表明,该方法有效抑制了图像块效应,且具备平移不变性,融合效果明显优于分块和Contourlet变换融合方法.     

Multi-focus image fusion with joint guided image filtering

Multi-focus image fusion is the activity of synthesizing multiple images of different focusing settings to construct a fully focused image. Many of the latest methods for image fusion rarely consider the structural differences between the guidance image and the input image, and do not retain well the important source image features while producing a fully focused image. To address this issue, a method exploiting a combination of static and dynamic filters (SDF) is proposed herein. This combination has good edge smoothing characteristics and strong robustness against artifacts such as gradient inversion and global strength migration. First, SDF is utilized in order to decompose the source image into structure and texture layers. Secondly, a morphological gradient operator filter is used to calculate the significance map of different levels of the source. Thirdly, the maximum pixel value of the significance map is used to construct the binary decision graph of the two source images. Then, the structure and texture layers are fused with the aid of the binary decision graph, and subsequently the final fusion image is created by combining the fused structure layer and texture layer. This process ensures that spatial consistency is preserved. Tests on grayscale and color multi-focus image sets show that the proposed method has better performance than that of any of the existing methods according to both objective and subjective evaluation.     

Weighted image fusion using cross bilateral filter and non-subsampled contourlet transform

Multidimensional Systems and Signal Processing - This paper provides a fusion technique for multi-focus imaging using cross bilateral filter and non-subsampled contourlet transform. The snapshots...     

边缘保护滤波的多焦点图像融合算法

基金项目：。摘要：已有的多焦点图像融合算法要么基于不含噪声的源图像假设,要么进行高频滤波时造成边缘特征的破坏;针对这些不足,提出边缘保护滤波的图像融合算法。首先利用局部细节特征的各向异性,建立了能够同时去噪和融合的能量函数,然后采用多焦点特征加权的融合规则,建立了能量函数的迭代求解策略。实验结果表明,算法的残留噪声更少,结构相似度和视觉融合质量更高。     

Multi-focus image fusion using HOSVD and edge intensity

The purpose of multi-focus image fusion is to integrate the partially focused images into one single image which is focused everywhere. To achieve this purpose, higher order singular value decomposition (HOSVD) and edge intensity (EDI) based multi-focus image fusion method is proposed. The main characteristics of the proposed method includes: 1. an effective and robust sharpness measure based on edge intensity is presented; 2. considering the fact that HOSVD is an effective data-driven decomposition technique and shows the outstanding ability in the representation of high-dimensional data, it is used to decompose multi-focus images; and 3. a multi-strategy fusion rule based on sigmoid function is used to fuse the decomposition coefficients. Furthermore, several experiments are conducted to verify the superiority of the proposed fusion framework in terms of visual and statistical analyses.     

A reduced-complexity image coding scheme using decision-directed wavelet-based contourlet transform

Recently the wavelet-based contourlet transform (WBCT) is adopted for image coding because it matches better image textures of different orientations. However, its computational complexity is very high. In this paper, we propose three tools to enhance the WBCT coding scheme, in particular, on reducing its computational complexity. First, we propose short-length 2-D filters for directional transform. Second, the directional transform is applied to only a few selected subbands and the selection is done by a mean-shift-based decision procedure. Third, we fine-tune the context tables used by the arithmetic coder in WBCT coding to improve coding efficiency and to reduce computation. Simulations show that, at comparable coded image quality, the proposed scheme saves over 92% computing time of the original WBCT scheme. Comparing to the conventional 2-D wavelet coding schemes, it produces clearly better subjective image quality.     

基于方向导波增强的红外与可见光图像融合

为提高融合图像的视觉效果,并解决可见光图像受光线、天气等影响而成像不清导致的夜视背景弱的问题,本文基于方向导波提出了一种可见光与红外线图像的融合方法。首先,利用方向导波对可见光图像的内容进行增强,然后,利用方向导波的多尺度分解将可见光和红外线图像进行分解后再分别合成为小尺度层、大尺度层和基础层。在大尺度层的信息合成的过程中利用视觉基础上的正则参数将红外线图像的信息加入到可见光图像中去;在基础层的融合过程中采用基于能量保护与细节提取的融合规则。最后,将大尺度层、小尺度层与基础层合并为融合后的图像。实验结果表明所给方法在提高夜视背景、细节处理、能量保护等方面都有良好的效果。     

基于contourlet变换和模糊理论的图像融合算法

提出一种基于Contourlet变换和模糊理论的红外与可见光图像融合算法。首先,对源图像进行Contourlet变换;随后对低频和高频的小波系数采用不同的融合规则,即低频部分采用基于模糊集的自适应加权融合方法,高频部分采用基于区域小波能量加权的方法;最后再通过contourlet逆变换,获得融合图像。实验结果表明该算法能够较好地保留源图像的细节信息,具有较好的视觉效果,是一种有效实用的方法。     

Multi-focus image fusion with Geometrical Sparse Representation

Multi-focus image fusion aims to generate an image with all objects in focus by integrating multiple partially focused images. It is challenging to find an effective focus measure to evaluate the clarity of source images. In this paper, a novel multi-focus image fusion algorithm based on Geometrical Sparse Representation (GSR) over single images is proposed. The main novelty of this work is that it shows the potential of GSR coefficients used for image fusion. Unlike the traditional sparse representation-based (SR) methods, the proposed algorithm does not need to train an overcomplete dictionary and vectorize the signal. In our algorithm, using a single dictionary image, the source images are first represented by geometrical sparse coefficients. Specifically, we employ a weighted GSR model in the sparse coding phase, ensuring the importance of the center pixel. Then, the weighted GSR coefficient is used to measure the activity level of the source image and an average pooling strategy is applied to obtain an initial decision map. Third, the decision map is refined with a simple post-processing. Finally, the fused all-in-focus image is constructed with the refined decision map. Experimental results demonstrate that the proposed method can be competitive with or even superior to the state-of-the-art fusion methods in both subjective and objective comparisons.     

基于DT-CWT的多聚焦图像的融合

由于离散小波变换在图像融合中的缺陷,提出基于双树复小波变换的图像融合方法。首先对源图像进行双树复小波变换,对低频和高频分别采用局部窗口能量比和局部梯度比作为融合相关性测度,然后基于加权和选择相结合的规则融合高频系数和低频系数,得到各频域系数,最后复原图像。实验结果表明,双树复小波变换的融合方法具有良好的客观评价性能和主观视觉效果,融合规则的可行性。