基于数值微分的图像融合方法

提出一种基于数值微分的图像融合方法。用精确求解离散数据导数的数值微分方法取代传统的邻域差分来求解图像梯度,将求解得到的图像梯度应用到基于偏微分方程的图像融合模型中,改进基于偏微分方程的图像融合数值化方案。将改进前后的方法进行对比实验,结果表明,应用改进方法得到的融合图像的质量指标和图像收敛性较优。     

基于二维连续模型的图像加、解密技术研究

文章讨论了基于二维伪抛物型方程的图像加、解密快速实现问题。首先证明了基于二维连续模型的加、解密问题是适定的,为加、解密实验提供了理论依据;然后根据模型的二维C-N格式系数矩阵的特殊稀疏结构,给出快速迭代求解算法;最后运用此迭代算法对图像进行了加、解密实验,结果表明采用该算法的图像加、解密在安全性上优于基于一维模型的算法。     

基于能量选择的DCT域音频数字水印算法

文中提出了基于能量选择的离散余弦变化域的音频水印算法。该算法根据人类听觉系统掩蔽特性和离散余弦变换能量不变的特性,利用在音频信号能量较大处插入水印对音频信号影响较小的特点,将二维的图像降维成一维的二值序列,通过音频信号时域序列的能量选择在离散余弦变换域低频系数中自适应地寻找最佳的水印图像插入点。实验表明,文中提出的算法不仅有较好的隐蔽性,而且对于数字信号处理操作中的加性高斯白噪声、滤波等具有较好的鲁棒性。     

基于一维模型的椒盐噪声去除方法

提出了一种基于一维信号处理去除图像椒盐脉冲噪声的方法，该算法给出了4种二维信号转换为一维信号的模型，用线性处理代替块处理去除图像椒盐脉冲噪声。经过实验仿真并与其他滤波算法比较表明，该算法可以有效地去除图像中的椒盐脉冲噪声，尤其是在计算复杂度上表现了很好的性能，同时也降低了图像边界点处理的边界效应。     

基于二进制量子微粒群算法的二维熵图像分割

分析量子计算的特点,对量子旋转门进行研究,给出了新的量子旋转门调整策略,并与离散二进制粒子群优化算法进行组合,提出了二进制量子粒子群优化算法。该算法具有收敛速度快、全局寻优能力强的特点。用典型复杂函数对其进行测试,测试结果表明,算法的优化质量和效率都优于离散二进制粒子群优化算法。将二进制量子粒子群优化算法与阈值法相结合应用于图像分割,结果表明了基于二进制量子粒子群优化算法的二维熵图像分割法用于阈值寻优具有更快的收敛速度和更好的全局寻优能力。     

二维枕形PSD信号采集与处理系统的设计

针对二维枕形PSD信号输出特点和工程实践中便携化的要求,基于新型单片机C8051F064和二维枕形PSD S1881,设计了一个二维枕形PSD信号采集与处理系统,并利用液晶显示模块SMG240128A设计了数据显示接口电路,实现了对二维枕形PSD输出信号的实时采集与显示;实验表明该采集系统方案是可行的,达到了设计要求.     

广义混沌二值序列生成方法

为了得到具有良好随机性和初值敏感性的二值序列，在已有的混沌系统的基础上，利用Bernstein函数，给出了一种基于插值方法构造的广义混沌序列产生方法。实验结果表明，生成的广义混沌序列具有很好的伪随机特性。通过对一维和二维广义混沌序列的非线性离散化处理，有效提高了离散化过程的安全性。给出了生成的二值序列在图像加密算法中的应用，该算法对图像加密效果良好。     

基于二维直方图双斜率划分的快速Otsu图像分割算法

为了克服Otsu算法对图像分割的不足,本文在二维Otsu的基础上提出了基于二维直方图双斜率划分的快速Otsu分割算法,该算法将二维直方图上像素点之间关系转化为斜率值,构建斜率值与频率的一维直方图,利用双阈值Otsu对灰度斜率的一维直方图取双阈值,利用求出的双阈值（临界斜率）对图像二维直方图进行重新划分,对划分的图像二维直方图的有效区域,再次利用一维Otsu方法划分,从而得到最终结果。最后通过实验,验证此方法有较强的抗噪性,比二维Otsu方法计算效率高,处理效果好。     

多特征动态融合的三维模型检索方法

提出一种基于二维正交投影图像的多特征动态融合的三维模型检索方法.首先计算三维模型的二维正交投影图像,然后提取二维正交投影图像的投影直方图和Zernike矩特征,通过加权求和在输出层融合,得到总体上模型间的相似度.每种特征采用动态权值,针对输入查询模型的不同,根据用户反馈自动更新知识库中的权值.实验表明,该方法在提高检索准确率的同时,也能保证检索效率.     

基于小波域隐马尔可夫模型的信号超分辨率重建

提出一种基于小波域隐马尔可夫模型(WHMM)的信号超分辨率重建算法。根据贝叶斯原理和最大后验概率估计理论,将WHMM作为先验知识给出一般信号的超分辨率重建模型;详细推导了重建问题的Euler-La-grange方程及对数似然函数的导数计算,将信号的超分辨率重建归结为一个简单线性方程组的求解;最后采用期望最大化(EM)算法和共轭梯度算法交替迭代计算WHMM的模型参数和高分辨率重建信号。一维和二维测试信号的实验结果表明该方法在有效抑制噪声的同时,能够很好地重建出信号的高频细节。在相同的信号降质条件下,针对一维情形,本文重建方法的峰值信噪比(PSNR)较三次插值和Tikhonov正则化方法平均提高2.3994 dB和4.474 2 dB;针对二维情形,本文重建方法的PSNR较双三次插值和Tikhonov正则化方法平均提高1.1741 dB和0.648 7 dB。     

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。     

Fast evolution of image manifolds and application to filtering and segmentation in 3D medical images

In many instances, numerical integration of space-scale PDEs is the most time consuming operation of image processing. This is because the scale step is limited by conditional stability of explicit schemes. We introduce the unconditionally stable semiimplicit linearized difference scheme that is fashioned after additive operator split (AOS) [Weickert, J. et al. (1998)], [Goldenberg, R et al., (2001)] for Beltrami and the subjective surface computation. The Beltrami flow [Kimmel, R. (1997) (1999)], [Sochen, N. et al. (1998)], is one of the most effective denoising algorithms in image processing. For gray-level images, we show that the flow equation can be arranged in an advection-diffusion form, revealing the edge-enhancing properties of this flow. This also suggests the application of AOS method for faster convergence. The subjective surface [Sarti, A. et al. (2002)] deals with constructing a perceptually meaningful interpretation from partial image data by mimicking the human visual system. However, initialization of the surface is critical for the final result and its main drawbacks are very slow convergence and the huge number of iterations required. We first show that the governing equation for the subjective surface flow can be rearranged in an AOS implementation, providing a near real-time solution to the shape completion problem in 2D and 3D. Then, we devise a new initialization paradigm where we first "condition" the viewpoint surface using the fast-marching algorithm. We compare the original method with our new algorithm on several examples of real 3D medical images, thus revealing the improvement achieved.     

A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

To overcome the shortcomings of 1D and 2D Otsu’s thresholding techniques, the 3D Otsu method has been developed. Among all Otsu’s methods, 3D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image; it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional 1D Otsu, 2D Otsu and 3D Otsu methods, as evident from the objective and subjective evaluations.      

用提升格式构造二次平均插值小波

提升格式是小波研究的新热点，提升格式的运用有两个不同的层次，即用提升格式来实现已有的小波变换和构造新的小波，首先用提升格式构造了二次平均插值小波，并用构造出的小波对灰度图象做变换，然后对变换后的系数采用最优比特率分配的方法来做编码，取得了与JPEG2000推荐的D9/7双正交小波图象编码基本一致的结果，但是，二次平均插值小波的提升格式要比D9/7双正交小波的提升格式简单得多，这意味着消耗的计算资源也少得多。     

Combined new nonnegative matrix factorization algorithms with two-dimensional nonnegative matrix factorization for image processing

In recent years, nonnegative matrix factorization (NMF) has attracted significant amount of attentions in image processing, text mining, speech processing and related fields. Although NMF has been applied in several application successfully, its simple application on image processing has a few caveats. For example, NMF costs considerable computational resources when performing on large databases. In this paper, we propose two enhanced NMF algorithms for image processing to save the computational costs. One is modified rank-one residue iteration (MRRI) algorithm , the other is element-wisely residue iteration (ERI) algorithm. Here we combine CAPG (a NMF algorithm proposed by Lin), MRRI and ERI with two-dimensional nonnegative matrix factorization (2DNMF) for image processing. The main difference between NMF and 2DNMF is that the former first aligns images into one-dimensional (1D) vectors and then represents them with a set of 1D bases, while the latter regards images as 2D matrices and represents them with a set of 2D bases. The three combined algorithms are named CAPG-2DNMF, MRRI-2DNMF and ERI-2DNMF. The computational complexity and convergence analyses of proposed algorithms are also presented in this paper. Three public databases are used to test the three NMF algorithms and the three combinations, the results of which show the enhancement performance of our proposed algorithms (MRRI and ERI algorithms) over the CAPG algorithm. MRRI and ERI have similar performance. The three combined algorithms have better image reconstruction quality and less running time than their corresponding 1DNMF algorithms under the same compression ratio. We also do some experiments on a real-captured image database and get similar conclusions.     

基于多进制小波的多源遥感影像融合

首先介绍了遥感影像融合的一般理论和方法，然后在讨论多进制小波理论和影像特征的基础上，提出了一种基于特征的多进制小波变换的影像融合算法，该算法根据待融合影像分辨率之比来确定采用几进制小波，将待融合的高分辨率影像进行多进制小波变换，然后把高分辨影像经小波变换后获得的低频成分和低分辨率影像依据一定的关系进行相互转换，以形成新的高分辨影像的低频成分，经过多进制小波逆变换获得到融合后的影像，最大限度地利用了待融合影像的信息，防止了影像信息的丢失，通过对具体影像的清晰度和空间分辨率，融合后的影像最大限度地保留了待融合影像的光谱信息，同时提高了待融合影像的清晰度和空间分辨率，给出了SPOT全色影像与SPOT多光谱影像，SPOT全色影像与TM影像的融合结果，并与其他方法进行了比较，从而证明了本方法的优越性和自适应能力。     

基于双子代差分演化和自适应分块机制的多聚焦图像融合算法

基于分块的多聚焦图像融合算法是多聚焦图像融合领域中的一个重要算法。基于差分演化的多聚焦图像融合算法将图像分块大小作为差分演化算法的种群,通过多次演化,最后获得使融合图像效果最好的图像分块。为克服标准差分演化算法由于丢失父代种群的部分信息导致收敛速度变慢、全局搜索范围较小,以及当对应图像块的清晰度相等时该算法的处理方式会改变源图像的像素值的缺点,在原算法的基础上,引入双子代机制和自适应分块机制,提出一种基于双子代差分演化和自适应分块机制的多聚焦图像融合算法。在演化过程中生成两个子代种群,最大程度上保留父代种群的信息,扩大全局搜索范围,提高算法的收敛性能;利用自适应分块机制,当出现图像块清晰度相等的情况时,将图像块分解成更小的图像块,然后再进行清晰度的比较,使改进算法获得的融合图像比原算法获得的效果更好,而且不会改变源图像的像素值。实验结果表明,基于双子代差分演化和自适应分块机制的多聚焦图像融合算法可以获得比原算法效果更好的融合图像,而且收敛性能更好。     

Pixel-level image fusion with simultaneous orthogonal matching pursuit

Pixel-level image fusion integrates the information from multiple images of one scene to get an informative image which is more suitable for human visual perception or further image-processing. Sparse representation is a new signal representation theory which explores the sparseness of natural signals. Comparing to the traditional multiscale transform coefficients, the sparse representation coefficients can more accurately represent the image information. Thus, this paper proposes a novel image fusion scheme using the signal sparse representation theory. Because image fusion depends on local information of source images, we conduct the sparse representation on overlapping patches instead of the whole image, where a small size of dictionary is needed. In addition, the simultaneous orthogonal matching pursuit technique is introduced to guarantee that different source images are sparsely decomposed into the same subset of dictionary bases, which is the key to image fusion. The proposed method is tested on several categories of images and compared with some popular image fusion methods. The experimental results show that the proposed method can provide superior fused image in terms of several quantitative fusion evaluation indexes.     

A two-dimensional discrete fractional Fourier transform-based pansharpening scheme

In this paper, a new approach for fusion of multi-spectral (MS) and panchromatic (Pan) images based on 2D-discrete fractional Fourier transform (2D-DFRFT) is proposed. The proposed technique is closer in approach to the other filtering-based pansharpening schemes existing in the literature. In the proposed method histogram equalized Pan image is transformed using the 2D-DFRFT and further used to generate the pansharpened image using appropriate pansharpening rule. The angle parameters associated with the 2D-DFRFT provide additional degrees of freedom which are optimized by single-objective particle swarm optimization (PSO) algorithm for finding better pansharpening results. Simulation results of the proposed technique carried out in MATLAB are presented for IKONOS and GeoEye-1 satellite images and compared with existing fusion methods in terms of both visual perception and objective metrics such as Q-index (Q4), Spectral Angle Mapper (SAM), relative dimensionless global error (ERGAS) and quality with-no reference (QNR). It is observed that the proposed pansharpening scheme provides improved spectral and spatial quality as compared with the existing schemes. The effects of aliasing and mis-registration errors on the proposed method are also investigated and compared with existing pansharpening methods. It is seen that the proposed method is robust against aliasing and mis-registration errors.