基于NSST 域隐马尔可夫树模型的SAR 和灰度可见光图像融合

针对合成孔径雷达(SAR) 图像和可见光图像融合问题, 提出一种基于非下采样剪切波变换域的隐马尔可夫树模型的图像融合方法(NHMM), 图像经过非下采样剪切波变换(NSST) 分解形成一个低频子带和多个高频子带.在NSST 域中, 对低频系数采用基于标准差的融合策略; 针对高频子带, 建立NSST 域隐马尔可夫树(HMT) 模型对高频系数进行训练, 并根据梯度能量对训练后的高频系数进行选择, 最后通过NSST 逆变换得到融合图像. 实验结果表明, 所提出的方法可提高图像的融合质量, 并能降低图像噪声, 具有一定的有效性和实用性.

     

EGGDD: An explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain

Most of the traditional medical image fusion methods that use the multi-scale decomposition schemes suffer from the bad image representations and the loss of the dependency in different highpass subbands. To deal with these problems, a novel dependency model, named Explicit Generalized Gaussian Density Dependency (EGGDD) model, is developed by the shift-invariant shearlet transform (SIST). Substantially different from describing the dependency by two hidden states in the Hidden Markov Tree (HMT) model, we provide the scheme to explicitly describe the marginal statistics of each highpass subband using the Generalized Gaussian Density (GGD), as well as the dependency between different subbands by the Kullback–Leibler distance (KLD). After embedding the obtained dependency into each highpass subband, an efficient fusion scheme, inspired by the divisive normalization response in the V1 visual cortex model, is developed to combine the highpass-subband coefficients. The experiments demonstrate that the developed method can produce better fusion results than those of some existing methods by the comparison of visual sense and quantitative measurements in terms of mutual information, entropy, spatial frequency, standard deviation, Q^AB/F and Q^W.     

Remote sensing image fusion using the curvelet transform

This paper presents an image fusion method suitable for pan-sharpening of multispectral (MS) bands, based on nonseparable multiresolution analysis (MRA). The low-resolution MS bands are resampled to the fine scale of the panchromatic (Pan) image and sharpened by injecting highpass directional details extracted from the high-resolution Pan image by means of the curvelet transform (CT). CT is a nonseparable MRA, whose basis functions are directional edges with progressively increasing resolution. The advantage of CT with respect to conventional separable MRA, either decimated or not, is twofold. Firstly, directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve a noise reduction that is better than that obtained in the separable wavelet domain. Secondly, modeling of the relationships between high-resolution detail coefficients of the MS bands and of the Pan image is more fitting, being accomplished in the directional multiresolution domain. Experiments are carried out on very-high-resolution MS + Pan images acquired by the QuickBird and Ikonos satellite systems. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art. Fusion tests at the full scale reveal that an accurate and reliable Pan-sharpening, little affected by local inaccuracies even in the presence of complex and detailed urban landscapes, is achieved by the proposed method.     

A new metric based on extended spatial frequency and its application to DWT based fusion algorithms

A new quantitative metric is proposed to objectively evaluate the quality of fused imagery. The measured value of the proposed metric is used as feedback to a fusion algorithm such that the image quality of the fused image can potentially be improved. This new metric, called the ratio of spatial frequency error (rSFe), is derived from the definition of a previous measure termed “spatial frequency” (SF) that reflects local intensity variation. In this work, (1) the concept of SF is first extended by adding two diagonal SFs, then, (2) a reference SF (SF_R) is computed from the input images, and finally, (3) the error SF (SF_E) (subtracting the fusion SF from the reference SF), or the ratio of SF error (rSFe = SF_E/SF_R), is used as a fusion quality metric. The rSFe (which can be positive or negative) indicates the direction of fusion error—over-fused (if rSFe > 0) or under-fused (if rSFe < 0). Thus, the rSFe value can be back propagated to the fusion algorithm (BP fusion), thereby directing further parameter adjustments in order to achieve a better-fused image. The accuracy of the rSFe is verified with other quantitative measurements such as the root mean square error (RMSE) and the image quality index (IQI), as well as with a qualitative perceptual evaluation based on a standard psychophysical paradigm. An advanced wavelet transform (aDWT) method that incorporates principal component analysis (PCA) and morphological processing into a regular DWT fusion algorithm is implemented with two adjustable parameters—the number of levels of DWT decompositions and the length of the selected wavelet. Results with aDWT were compared to those with a regular DWT and with a Laplacian pyramid. After analyzing several inhomogeneous image groups, experimental results showed that the proposed metric, rSFe, is consistent with RMSE and IQI, and is especially powerful and efficient for realizing the iterative BP fusion in order to achieve a better image quality. Human perceptual assessment was measured and found to strongly support the assertion that the aDWT offers a significant improvement over the DWT and pyramid methods.     

A reversible data hiding scheme based on the Sudoku technique

Data hiding, also known as information hiding, plays an important role in information security for various purposes. Reversible data hiding is a technique that allows distortion-free recovery of both the cover image and the secret information. In this paper, we propose a new, reversible data hiding scheme that is based on the Sudoku technique and can achieve higher embedding capacity. The proposed scheme allows embedding more secret bits into a pair of pixels while guaranteeing the good quality of the stego-image. The experimental results showed that the proposed scheme obtained higher embedding capacity than some other previous schemes. In addition, our proposed scheme maintained the good visual quality of the stego-image (i.e., PSNR > 46 dB), which outperforms some existing schemes.     

An edge detection scheme based on least squares support vector machine in a contourlet HMT domain

In this paper, we have presented a new and effective edge detection scheme based on least squares support vector machine (LS-SVM) classification in a contourlet Hidden Markov Tree Model (contourlet HMT). First, the input noisy image is decomposed into coarser and finer coefficients using a contourlet HMT transform to derive an efficient multiscale and multidirectional image representation. Second, the feature vector is performed through spatial regularity in a contourlet HMT domain, and the coarser coefficients classified using LS-SVM classifier into two classes: noise coefficients and edge coefficients. Next, all noisy contourlet HMT coefficients are well denoised by the BayesShrink method.Finally, the denoised coefficients and edge coefficients are fused using the weighted average rule, and the inverse contourlet HMT is applied to obtain the edge image.Experimental results demonstrate that our scheme can attain improved performance over state-of-the-art edge detection approaches, both qualitatively and quantitatively. Tests were performed on several images from the Berkeley dataset corrupted with Gaussian noise and on other images such as a cameraman, pepper and medical images. The results illustrate that the performance of the proposed scheme is stable.     

基于PCNN与区域特征的红外与可见光图像融合

针对红外图像与可见光图像融合中容易产生红外目标不明显、对比度不高的问题,提出了一种新的融合算法。该算法创新地将PCNN与区域特征应用到NSCT域内低频和带通子带系数的选择上。通过NSCT分解得到待融合图像的子带系数。运用PCNN对分解后的子带系数进行处理,得到子带系数的点火映射图。低频子带点火映射图采取基于区域标准差的方法选取融合系数。带通子带点火映射图采取基于区域能量的方法选取融合系数。融合图像通过NSCT逆变换可以得到。仿真实验表明与其他算法相比,该算法能够得到红外目标突出、质量更好的融合图像,图像客观评价指标提升明显。     

Multi-channel satellite cloud image fusion in the tetrolet transform domain

A novel multi-channel satellite cloud image fusion algorithm constructed in the tetrolet transform domain is proposed. Tetrolet is successfully applied in image denoising, image sparse representation, and image restoration. In this paper, tetrolet transform was introduced into the field of satellite cloud image fusion since its sparse degree is high. Tetrolet can describe the geometric structure feature of the satellite cloud image very well. First, tetrolet transform must be implemented into the multi-channel satellite cloud images to obtain low- and high-frequency coefficients and corresponding covering distribution values. Then, a Laplacian pyramid algorithm must be used to decompose the low-frequency portion in the tetrolet domain by averaging the values of its top layer and taking the maximum absolute values of the other layers. While reconstruction is implemented in this stage, the algorithm takes the maximum standard deviation of the high-frequency parts for each block in the tetrolet domain. Last, an inverse tetrolet transform must be used to obtain the final fused image. This paper compares the proposed image fusion algorithm to three similar image fusion algorithms: the curvelet image fusion algorithm, the non-subsampled contourlet transform (NSCT) image fusion algorithm, and the tetrolet image fusion algorithm. Mutual information, joint entropy, mean structural similarity (MSSIM), standard deviation, and average relative deviation are used as objective criteria to evaluate the quality of the fused images. In order to verify the efficiency of the proposed algorithm, the fusion cloud image is used to determine the centre location of eye and non-eye typhoons. Experimental results show that the proposed algorithm performs well when fusing the information in multi-channel satellite cloud images and improves the precision of locating the typhoon’s centre. The proposed algorithm’s comprehensive performance is superior to similar image fusion algorithms.     

GC-ASM: Synergistic integration of graph-cut and active shape model strategies for medical image segmentation

Image segmentation methods may be classified into two categories: purely image based and model based. Each of these two classes has its own advantages and disadvantages. In this paper, we propose a novel synergistic combination of the image based graph-cut (GC) method with the model based ASM method to arrive at the GC-ASM method for medical image segmentation. A multi-object GC cost function is proposed which effectively integrates the ASM shape information into the GC framework. The proposed method consists of two phases: model building and segmentation. In the model building phase, the ASM model is built and the parameters of the GC are estimated. The segmentation phase consists of two main steps: initialization (recognition) and delineation. For initialization, an automatic method is proposed which estimates the pose (translation, orientation, and scale) of the model, and obtains a rough segmentation result which also provides the shape information for the GC method. For delineation, an iterative GC-ASM algorithm is proposed which performs finer delineation based on the initialization results. The proposed methods are implemented to operate on 2D images and evaluated on clinical chest CT, abdominal CT, and foot MRI data sets. The results show the following: (a) An overall delineation accuracy of TPVF > 96%, FPVF < 0.6% can be achieved via GC-ASM for different objects, modalities, and body regions. (b) GC-ASM improves over ASM in its accuracy and precision to search region. (c) GC-ASM requires far fewer landmarks (about 1/3 of ASM) than ASM. (d) GC-ASM achieves full automation in the segmentation step compared to GC which requires seed specification and improves on the accuracy of GC. (e) One disadvantage of GC-ASM is its increased computational expense owing to the iterative nature of the algorithm.     

一种基于小波变换的多分辨图像融合算法

图像的融合过程是将两个或更多的图像结合成一个图像,融合图像保留了每一个图像的重要特征。图像融合技术能够提高图像质量和数据的实用性。主要描述了图像融合的两个课题：（1）融合算法;（2）质量评估。介绍的图像融合算法是先对原图像进行多分辨小波变换,再把输入图像的小波变换系数通过一定的融合规则进行选择,然后经过逆小波变换得到新的图像,对图像融合的质量评估也进行了讨论。     

基于PCNN和非线性滤波万有引力的医学图像融合

为了更好地满足现代医学临床诊断和治疗的需要,提高医学图像的融合质量,提出在提升小波变换的基础上,结合脉冲耦合神经网络（PCNN）和像素点的非线性滤波万有引力的医学图像融合方法。低频子系数采用基于区域灰度均值的融合规则;高频子系数采用自适应PCNN的融合规则,将像素的非线性滤波万有引力作为简化的PCNN模型中的链接强度,使PCNN能够自适应调节链接强度的大小,并根据点火矩阵确定高频子系数。实验结果表明,该方法得到的融合图像比其他融合方法保留了更多的边缘细节信息,各项评价指标均有所提高,有更好的融合性能。     

Contourlet变换系数加权的医学图像融合

目的 由于获取医学图像的原理和设备不同,不同模式所成图像的质量、空间与时间特性都有较大差别,并且不同模式成像提供了不互相覆盖的互补信息,临床上通常需要对几幅图像进行综合分析来获取信息。方法 为了提高对多源图像融合信息的理解能力,结合Contourlet变换在多尺度和多方向分析方法的优势,将Contourlet变换应用于医学图像融合中。首先将源图像经过Contourlet变换分解获得不同尺度多个方向下的分解系数。其次通过对Contourlet变换后的系数进行分析来确定融合规则。融合规则主要体现在Contourlet变换后图像中的低频子带系数与高频子带系数的优化处理中。针对低频子带主要反映图像细节的特点,对低频子带系数采用区域方差加权融合规则;针对高频子带系数包含图像中有用边缘细节信息的特点,对高频子带系数采用基于主图像的条件加权融合规则。最后经过Contourlet变换重构获得最终融合图像。结果 分别进行了基于Contourlet变换的不同融合规则实验对比分析和不同融合方法实验对比分析。通过主观视觉效果及客观评价指标进行评价,并与传统融合算法进行比较,该算法能够克服融合图像在边缘及轮廓部分变得相对模糊的问题,并能有效地融合多源医学图像信息。结论 提出了一种基于Contourlet变换的区域方差加权和条件加权融合算法。通过对CT与MRI脑部医学图像的仿真实验表明,该算法可以增加多模态医学图像互补信息,并能较好地提高医学图像融合的清晰度。     

一种改进的PCNN图像融合算法

针对使用小波变换及简单融合规则的图像融合算法的不足,提出了一种改进的基于脉冲耦合神经网络（Pulse Coupled Neural Networks,PCNN）融合规则的非下采样轮廓波变换（Nonsubsampled Contourlet Transform,NSCT）图像融合方法。对已配准待融合图像进行NSCT分解,采用改进的PCNN融合规则对Contourlet域系数进行融合,得到融合图像的NSCT系数,经逆变换重构得到融合图像。实验结果表明该算法在主观视觉和客观评价指标上都取得了较好的融合效果。     

基于NSCT变换的红外与可见光图像融合技术研究

提出了一种基于非下采样Contourlet变换(NSCT)的红外与可见光图像的融合算法。采用对低频系数取平均,对高频系数中最大分解尺度选择系数最大值,其他尺度系数采用局部方差最大的规则,通过对所得到的融合系数进行逆变换即可得到融合后的图像。实验表明:该算法结合了NSCT的多尺度、多方向和平移不变性的优点,能够更好地提取源图像特征,增强融合图像的空间细节表现能力。融合后的图像具有较好的主观视觉效果,标准差和熵值较传统的融合方法有所提高。     

Image denoising in extended Shearlet domain using hidden Markov tree models

Denoising of images is one of the most basic tasks of image processing. It is a challenging work to design an edge-preserving image denoising scheme. Extended discrete Shearlet transform (extended DST) is an effective multi-scale and multi-direction analysis method; it not only can exactly compute the Shearlet coefficients based on a multiresolution analysis, but also can provide nearly optimal approximation for a piecewise smooth function. In this paper, a new image denoising approach in extended Shearlet domain using hidden Markov tree (HMT) model is proposed. Firstly, the joint statistics and mutual information of the extended DST coefficients are studied. Then, the extended DST coefficients are modeled using an HMT model with Gaussian mixtures, which can effectively capture the intra-scale and inter-scale dependencies. Finally, the extended Shearlet HMT model is applied to image denoising. Extensive experimental results demonstrate that our extended Shearlet HMT denoising method can obtain better performances in terms of both subjective and objective evaluations than other state-of-the-art HMT denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.     

Novel infrared and visible image fusion method based on independent component analysis

The goal of infrared (IR) and visible image fusion is for the fused image to contain IR object features from the IR image and retain the visual details provided by the visible image. The disadvantage of traditional fusion method based on independent component analysis (ICA) is that the primary feature information that describes the IR objects and the secondary feature information in the IR image are fused into the fused image. Secondary feature information can depress the visual effect of the fused image. A novel ICA-based IR and visible image fusion scheme is proposed in this paper. ICA is employed to extract features from the infrared image, and then the primary and secondary features are distinguished by the kurtosis information of the ICA base coefficients. The secondary features of the IR image are discarded during fusion. The fused image is obtained by fusing primary features into the visible image. Experimental results show that the proposed method can provide better perception effect.     

采用shearlet变换的多聚焦图像融合

相对传统多尺度分析工具,shearlet变换更适于提取图像细节信息。采用shearlet变换进行图像融合,对源图像进行shearlet域分解,对低频子带采用SML算子作为融合依据,高频子带采取区域能量与单个像素相结合的方式选择系数,对融合后的系数进行逆shearlet变换得到融合图像。仿真实验表明,算法在视觉效果和量化结果上均有提高。     

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。     

双重字典学习与自适应PCNN相结合的医学图像融合

目的 针对基于稀疏编码的医学图像融合方法存在的细节保存能力不足的问题,提出了一种基于卷积稀疏表示双重字典学习与自适应脉冲耦合神经网络（PCNN）的多模态医学图像融合方法。方法 首先通过已配准的训练图像去学习卷积稀疏与卷积低秩子字典,在两个字典下使用交替方向乘子法（ADMM）求得其卷积稀疏表示系数与卷积低秩表示系数,通过与对应的字典重构得到卷积稀疏与卷积低秩分量;然后利用改进的的拉普拉斯能量和（NSML）以及空间频率和（NMSF）去激励PCNN分别对卷积稀疏与卷积低秩分量进行融合;最后将融合后的卷积稀疏与卷积低秩分量进行组合得到最终的融合图像。结果 对灰度图像与彩色图像进行实验仿真并与其他融合方法进行比较,实验结果表明,所提出的融合方法在客观评估和视觉质量方面明显优于对比的6种方法,在4种指标上都有最优的表现;与6种多模态图像融合方法相比,3组实验平均标准差分别提高了7%、10%、5.2%;平均互信息分别提高了33.4%、10.9%、11.3%;平均空间频率分别提高了8.2%、9.6%、5.6%;平均边缘评价因子分别提高了16.9%、20.7%、21.6%。结论 与其他稀疏表示方法相比,有效提高了多模态医学图像融合的质量,更好地保留了源图像的细节信息,使融合图像的信息更加丰富,符合人眼的视觉特性,有效地辅助医生进行疾病诊断。