Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure

Finite depth-of-field poses a problem in light optical imaging systems since the objects present outside the range of depth-of-field appear blurry in the recorded image. Effective depth-of-field of a sensor can be enhanced considerably without compromising the quality of the image by combining multi-focus images of a scene. This paper presents a block-based algorithm for multi-focus image fusion. In general, finding a suitable block-size is a problem in block-based methods. A large block is more likely to contain portions from both focused and defocused regions. This may lead to selection of considerable amount of defocused regions. On the other hand, small blocks do not vary much in relative contrast and hence difficult to choose from. Moreover, small blocks are more affected by mis-registration problems. In this work, we present a block-based algorithm which do not use a fixed block-size and rather makes use of a quad-tree structure to obtain an optimal subdivision of blocks. Though the algorithm starts with blocks, it ultimately identifies sharply focused regions in input images. The algorithm is simple, computationally efficient and gives good results. A new focus-measure called energy of morphologic gradients is introduced and is used in the algorithm. It is comparable with other focus measures viz.energy of gradients, variance, Tenengrad, energy of Laplacian and sum modified Laplacian. The algorithm is robust since it works with any of the above focus measures. It is also robust against pixel mis-registration. Performance of the algorithm has been evaluated by using two different quantitative measures.     

基于映射复支持度变换的多聚焦图像融合

针对支持度变换在多聚焦图像融合技术方面存在的不足,提出了基于软空间映射的复支持度变换的图像融合方法,该方法将图像信息(实数域)映射到其软空间的象(复数域),在此基础上进行支持度变换,既保留支持度变换已有的优点,又增加了图像的分解信息量,进一步提高了图像融合质量.实验结果表明在主观融合效果与客观评价指标方面与支持度变换相比均有一定提高.     

Multifocus image fusion using the log-Gabor transform and a Multisize Windows technique

Today, multiresolution (MR) transforms are a widespread tool for image fusion. They decorrelate the image into several scaled and oriented sub-bands, which are usually averaged over a certain neighborhood (window) to obtain a measure of saliency. First, this paper aims to evaluate log-Gabor filters, which have been successfully applied to other image processing tasks, as an appealing candidate for MR image fusion as compared to other wavelet families. Consequently, this paper also sheds further light on appropriate values for MR settings such as the number of orientations, number of scales, overcompleteness and noise robustness. Additionally, we revise the novel Multisize Windows (MW) technique as a general approach for MR frameworks that exploits advantages of different window sizes. For all of these purposes, the proposed techniques are firstly assessed on simulated noisy experiments of multifocus fusion and then on a real microscopy scenario.     

基于神经网络与对比度的多聚焦图像融合技术

提出了一种基于小波对比度和神经网络的多聚焦图像融合算法。首先对各源图像进行小波变换,根据变换后系数计算出图像的小波对比度,选取源图像部分区域小波对比度作为前馈神经网络的训练样本,调整神经网络权重;然后用训练好的神经网络组合融合图像的小波系数,对组合后的系数进行一致性校验;最后对该系数进行小波逆变换,得到融合图像。实验结果表明,该算法能够较好地解决多聚焦图像融合问题,生成的融合图像效果优于传统图像融合方法。     

非下采样Contourlet变换的彩色图像融合

为了提高图像融合的效果,以多聚焦的彩色图像为研究对象,提出了一种基于非下采样Contourlet变换的多聚焦彩色图像融合算法。对IHS模型的亮度分量I进行NSCT分解,根据多聚焦图像的成像特点和分解后的高低频系数相关性,对分解后的低频系数采用"区域能量取大"和高频系数采用"绝对值和取大"的融合准则进行融合,再进行非下采样Contourlet逆变换,得到融合后的亮度分量,并由此计算融合后的色调分量和饱和度分量,实现彩色图像的融合。实验结果表明,该方法的融合效果优于小波变换的融合结果,针对IHS模型的融合结果也明显优于RGB模型的融合结果。     

Multifocus image fusion using convolutional neural networks in the discrete wavelet transform domain

Multimedia Tools and Applications - In this paper, a novel multifocus image fusion algorithm based on the convolutional neural network (CNN) in the discrete wavelet transform (DWT) domain is...     

Multi-focal image fusion using degree of focus and fuzzy logic

The continuous advancement in the field of imaging sensor necessitates the development of an efficient image fusion technique. The multi-focal image fusion extracts the in-focus information from the source images to construct a single composite image with increased depth-of-field. Traditionally, the information in multi-focal images is divided into two categories: in-focus and out-of-focus data. Instead of using a binary focus map, in this work we calculate the degree of focus for each source image using fuzzy logic. The fused image is then generated based on the weighted sum of this information. An initial focus tri-state map is built for each input image by using spatial frequency and a proposed focus measure named as alternate sum modified Laplacian. For the cases where these measures indicate different source images to contain focused pixel or have equal strength, another focus measure based on sum of gradient is employed to calculate the degree of focus in a fuzzy inference system. Finally, the fused image is computed from the weights determined by the degree of focus map of each image. The proposed algorithm is designed to fuse two source images, whereas fusion of multiple input images can be performed by fusing a source image with the fusion output of the previous input group. The comparison of the proposed method with several transform and pixel domain based techniques are conducted in terms of both subjective visual assessment and objective quantitative evaluation. Experimental results demonstrate that our method can be competitive with or even outperforms the methods in comparison.     

An improved multi-focus image fusion algorithm based on multi-scale weighted focus measure

Applied Intelligence - This paper focuses on developing an improved multi-focus image fusion (MFIF) algorithm. Existing spatial domain algorithms dependent on the obtained fusion decision map still...     

基于改进LP变换及自适应PCNN的多聚焦图像融合方法

提出一种抗噪声多聚焦图像融合方法.首先,采用改进拉普拉斯金字塔（LP）变换构造图像的塔形数据结构.每层数据经脉冲耦合神经网络（PCNN）迭代运算生成相应的点火次数矩阵;然后,以此为依据经判决算子完成数据融合;最后,采用伪逆重构算法生成融合图像.实验结果表明,所提出的方法具有抗加性噪声及JPEG压缩系统噪声的能力,融合图像的客观评价指标较高,且能有效减弱原LP算法融合图像边缘的＂Gibss＂伪影现象.     

一种新的结合NSCT和PCNN的图像融合方法

考虑到小波变换存在一些局限性,提出一种把非下采样Contourlet变换(NSCT)与脉冲耦合神经网络(PCNN)相结合的图像融合新方法。用NSCT变换从多尺度和多方向上分解配准后的原始图像。低频应用改进的边缘能量结合空间频率的融合方法;高频应用PCNN简化数学模型,其链接强度用改进的拉普拉斯能量和表示。并且选择点火映射图的点火次数与其标准差相结合的方法。最后经过NSCT逆变换得出融合图像。实验分析可知,与其他几种图像融合方法进行比较,新方法取得了更高质量的融合图像。     

Multifocus image fusion method of Ripplet transform based on cycle spinning

The curvelet transform can represent images at both different scales and different directions. Ripplet transform, as a higher dimensional generalization of the curvelet transform, provides a new tight frame with sparse representation for images with discontinuities along C² curves. However, the ripplet transform is lack of translation invariance, which causes the pseudo-Gibbs phenomenon on the edges of image. In this paper, the cycle spinning method is adopted to suppress the pseudo-Gibbs phenomena in the multifocus image fusion. On the other hand, a modified sum-modified-laplacian rule based on the threshold is proposed to make the decision map to select the ripplet coefficient. Several experiments are executed to compare the presented approach with other methods based on the curvelet, sharp frequency localized contourlet transform and shearlet transform. The experiments demonstrate that the presented fusion algorithm outperforms these image fusion works.     

基于NSCT的自适应多聚焦图像融合算法

针对非降采样轮廓波变换（NSCT）具有多尺度、方向性和平移不变性等特点,为改善融合后图像模糊现象,提出了一种基于区域特性的非降采样轮廓波变换的多聚焦图像融合算法。该算法结合NSCT的特点,将图像进行NSCT,变换为不同方向的各子带信息;然后基于局部均值和局部方差选择低频子带系数,并在带通方向子带中引用局部方向对比度作为测量算子来选择带通方向子带系数;最后,通过反变换得到融合图像。实验结果表明,本算法融合效果优于传统的加权平均、小波变换及NSCT算法。     

非子采样变换域的方向向量约束图像融合算法

针对存在的频谱混叠及图像细节信息丢失问题, 研究已有基于多尺度分解的多分辨率图像融合方法, 提出采用具有移不变性的非子采样轮廓变换对图像进行多尺度、多方向分解, 更完整地表征图像的几何结构, 同时对滤波器组进行上采样消除频谱混叠。研究已有的子带系数的融合规则, 提出了基于方向向量约束对比度的高频系数融合规则, 以及基于局部多向方差能量的选择与平均的低频系数融合规则。经多组实验结果表明, 该融合方法在视觉上和评价测度指标上都优于已有基于多分辨率图像融合方法。     

基于Brenner函数与新轮廓波变换的多聚焦融合算法

为了消除Contourlet融合算法中各方向子带的频谱混叠现象,同时提高有效系数提取的正确率,提出了一种基于Brenner函数与新轮廓波变换(NCT-SFL)的多聚焦图像融合算法。该算法先使用新轮廓波变换分解多聚焦待融合图像;然后对低频系数采用传统的算术平均融合规则,高频系数采用基于Brenner函数的局部能量最大的融合规则,最后经逆新轮廓波变换得到融合图像。实验结果表明：该算法能有效提取待融合图像的轮廓信息,并在获得较优的主观视觉前提下,客观评价指标互信息与转移的边缘信息分别提高了99.34%与77.95%。此外,新轮廓波分解层数较多时,该算法的优势更为明显。     

Multifocus image fusion scheme based on discrete cosine transform and spatial frequency

Multimedia Tools and Applications - Multifocus images are different images of the same scene captured with different focus in the cameras. These images when considered individually may not give...     

各向异性扩散与高斯差分模型的多聚焦图像融合方法

利用各向异性扩散模型具有良好的边缘保持特性,提出一种基于各向异性扩散滤波与高斯滤波差分规则的图像融合算法。各向异性扩散方程对图像进行滤波操作,在图像的同质区域实施正向扩散以平滑图像,而在图像边缘实行较弱平滑以保护边缘细节信息。将通过各向异性扩散模型处理的图像与经过高斯函数滤波的结果图像进行差分操作,可以得到图像的高频系数信息。为提高健壮性,对高频系数进行小窗口累加,其作为像素选择准则,再分别从原始图像中直接获取对应的像素值组成融合结果图像。实验结果表明,所提出的方法可以有效地融合源图像信息,非常适合多聚焦     

A new wavelet-based measure of image focus

We present a new measure of image focus. It is based on wavelet transform of the image and is defined as a ratio of high-pass band and low-pass band norms. We show this measure is monotonic with respect to the degree of defocusation and sufficiently robust. We experimentally illustrate its performance on simulated as well as real data and compare it with existing focus measures (gray-level variance and energy of Laplacian). Finally, an application of the new measure in astronomical imaging is shown.     

提升静态小波特征对比度多聚焦图像融合算法*

针对多聚焦图像融合问题,提出了一种新的基于提升静态小波变换(lifting stationary wavelet transform,LSWT)的特征对比度图像融合方法。该方法对图像经LSWT分解得到的不同子带系数采用不同的融合方案。在选择低频子带系数时提出了一种基于改进拉普拉斯能量和的视觉特性对比度系数选择方案;而在选择融合图像的高频子带系数时,根据人眼视觉对图像局部对比度变换非常敏感的特性,提出了局部特征对比度的概念,设计了一种基于局部特征对比度的系数选择方案。实验证明,该算法相对于传统图像融合方法,能得到视觉效果更好、量化指标更优的融合图像。     

Infrared and visible image fusion based on NSCT and stacked sparse autoencoders

Multimedia Tools and Applications - To integrate the infrared object into the fused image effectively, a novel infrared (IR) and visible (VI) image fusion method by using nonsubsampled contourlet...