联合估计混叠度，运动参数和高分辨率图像的JEMAP算法

本文提出一种空域联合估计混叠度，运动参数和高分辨率图像的JEMAP算法，以提高超分辨率处理的适应性，在地面上稳定地获得高于实测卫星图像中所用CCD设备成像精度的高清晰图像。算法根据星戡CCD设备的成像机理，定义卫星图像的混叠度，确定超分辨率处理模型，通过空域迭代联合估计混叠度，运动参数和高分辨率图像。实际卫星图像处理表明JEMAP算法具有很好的适应性和稳定性，包含先验约束能力较强，可以解开欠采样低分辨率输入图像的混叠，重构清晰卫星图像。     

单帧低分辨率图像的SFMAP高分辨率重建算法

卫星图像的超分辨率处理是基础性和标志性的空间技术。一般情况下很难得到可供处理的一序列卫星图像，单帧图像的超分辨率处理尤为重要。我们提出由单帧欠采样的低分辨率噪音卫星图像重建高清晰卫星图像的最大后验概率估计(SFMAP)算法。通过插值生成多帧低分辨率图像族，模拟亚像元位移的低分辨率图像，参与空域迭代过程，以满足其空域代数方程组的超定问题，在一定程度上解开输入低分辨率图像的混叠，得到高分辨率重建图像。实际处理显示出较好的效果。     

一种基于MAP的超分辨率图像重建的快速算法

超分辨率图像重建技术就是通过融合多幅变形、模糊、有噪、频谱混叠的低分辨率降质图像(或视频序列)来重建一幅高质量高分辨率图像.MAP估计算法是一种广泛使用的统计重建方法.针对标准MAP估计算法运算量大的问题提出了两点改进.第1点是当计算梯度时直接计算目标函数的增量,避免了函数值的冗余计算;第2点是采用非精确一维搜索确定步长,避免了运算量庞大的海塞矩阵的计算.实验结果表明,提出的改进在保持重建效果基本不变的前提下,在很大程度上提高了MAP超分辨率图像重建方法的速率,与此同时保证了算法的收敛性.     

基于自适应双边全变差的图像超分辨率重建

超分辨率图像重建技术就是通过融合多幅变形、模糊、有噪、频谱混叠的低分辨率降质图像(或视频序列)来重建一幅高质量高分辨率图像.MAP估计算法是一种广泛使用的统计重建方法.针对标准的MAP算法引入了自适应概念,引入了图像自适应加权系数矩阵;据此给出一种基于自适应双边全变差的图像超分辨率重建算法,该方法不仅能在图像超分辨率重建过程中抑制噪声,而且能锐化图像中的边缘信息;建立了自适应重建模型并用梯度下降法推导出迭代计算公式.实验表明,该算法在收敛性和精确性上都达到了较好的效果.     

基于差分进化的置换混叠图像盲分离

针对1类存在模糊差异的置换混叠图像,提出了1种基于差分进化的置换混叠图像盲分离算法。选择空域作为特征域,将置换混叠图像的梯度图像分成小块,并为每个小块设定个阈值,组成1个阈值向量。利用差分进化算法获得最优的阈值向量,将置换混叠图像阈值化为二值图像,利用图像形态学实现二值图像分离出置换图像。实验结果表明,与阈值法相比较,本算法能较好地把置换图像从置换混叠图像中分离出来,而不受置换图像位置、大小和个数的限制。     

抗混叠轮廓波HMT模型的医学图像融合

为了更好地对医学图像进行融合,提出了一种利用抗混叠轮廓波HMT模型的图像融合新算法。该算法首先对原始轮廓波变换的频谱混叠问题展开研究,明确LP分解中的两个低通滤波器不满足Nyquist抽样定律是造成混叠的主要原因。接着,在对低通滤波器考虑带限约束条件下,设计了一种能抑制混叠的利用双通道滤波器组结构的多尺度分解方案,用于代替原始轮廓波变换的LP分解,结合方向滤波器组,实现了一种抗混叠的轮廓波变换。在此基础上,提出一种采用隐马尔可夫树（HMT）来刻划变换系数尺度间相关性的医学图像成像模型,并以期望最大化算法估计模型参数,得到融合图像。CT与MR图像以及MR-T1与MR-T2图像的融合实验表明,该算法的融合结果无论在视觉质量及定量指标上都明显优于基于小波变换和原始轮廓波变换的方法。     

基于MAP框架的时空联合自适应视频序列超分辨率重建

超分辨率重建在视频监控、高清晰度电视、遥感图像、医学图像处理等领域具有广阔的应用前景. 最大后验估计(maximum a posteriori, MAP)法是普遍采用的一种超分辨率重建方法. 针对传统MAP法存在的局限性, 本文提出了一种基于MAP框架的时空联合自适应视频序列超分辨率重建算法. 时空联合自适应机制的引入使得算法在保持边缘的同时可减小错误运动估计矢量对重建图像质量的影响. 实验结果表明, 算法具有重建质量好、边缘保持能力强、收敛速度快等特点.     

针对光场图像的多尺度抗混叠绘制方法

针对现有光场图像存在混叠的问题,提出一种多尺度抗混叠绘制光场图像的方法.首先分析了光场角度降采样引起图像混叠的原因,然后给出角度采样率与图像混叠关系的数学描述,并且指出了影响图像混叠的相关因素.在此基础上,提出采用多尺度光场图像梯度融合的方法降低图像混叠,其优点在于无需场景深度先验信息.分别在虚拟光场数据和真实光场数据集上进行了抗混叠绘制实验,结果表明该方法优于经典光场绘制方法,能够得到与已知深度绘制方法相近的结果.     

基于QR分解的盲源分离几何算法

提出了一类新的实时线性混叠信号盲分离算法,该算法基于混叠矩阵的QR分解思想,结合均匀分布的源信号相互独立时其Scatter图具有的特殊形状以及与坐标轴平行的几何性质,导出了一类盲源分离的几何算法.本文的方法相对Taro和Hyvarinen而言,大大简化了其分离过程,从而缩短了分离时间.仿真结果表明,对同样两幅混叠图像的分离,在效果相当的情况下,本文算法的分离时间比Hyvarinen的分离时间缩短了约2.5倍.     

蚁群聚类的欠定盲源分离方法

利用欠定盲源分离情况下稀疏源信号具有直线聚类的特点,提出了一种估计混叠矩阵的新方法。通过对混叠信号进行标准化处理,使混叠信号形成球形簇,将线性聚类转变成致密聚类;利用蚁群聚类算法对其进行搜索得到聚类中心,从而获得对混叠矩阵的精确估计。该方法能实现源信号数目未知情况下的欠定盲源分离,且能推广到三路或更多路观测信号的情况。对语音信号的仿真结果证明,该方法能精确地分离和恢复原始信号。     

Aliasing artifacts reduction with subband signal analysis for demosaicked images

Demosaicking is a process used to estimate missing color values from the subsampled color filter array (CFA) image to reduce the cost and volume of a digital still camera. However, by sampling theory, it is known that subsampling a signal causes overlaps of signals in the frequency domain, which is known as aliasing. Most current demosaicking processes cannot completely solve aliasing problem resulting in aliasing artifacts such as false colors and zipper effects. In this paper, we propose an algorithm to remove these aliasing artifacts in demosaicked color images. A luminance image with minimum aliasing is obtained from the CFA image by using a low-pass kernel with cutoff frequencies determined by an approximate model for the Fourier spectrum. An aliasing map is computed by analyzing subband signals of the CFA image based on the high correlation of the high-frequencies of the luminance and color channels. Then, a least squares of the luminance acquisition processes is used to design a cost function with the aliasing map to remove the aliasing artifacts. The experiments demonstrate that the proposed algorithm sufficiently removes aliasing artifacts and improves the quality of the color images.     

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.     

基于冗余性的图像重采样检测

数字图像取证中,目前的重采样检测算法都是检测图像中是否存在插值过程引入的周期性,而周期性的判定一般通过在频域的幅度谱中寻找峰值来进行,进而通过峰值的位置来计算重采样因子。但是由于重采样过程中的频率混叠问题导致了重采样因子不能完全确定。针对这个问题,本文提出一种时域中计算重采样因子的方法。重采样图像中每个像素行（或列）和相邻行（或列）的冗余性大小不同,并且冗余性大小呈现出周期性的分布。通过检测此特征就可以实现对重采样的取证,并且确定重采样因子。实验显示,在未压缩的图像中算法可以正确地估计出所有重采样因子,在压缩图像中本文的算法较之前的算法也有明显的优势。     

对不同分辨率图象进行空间分辨率改善的频域方法

图象空间分辨率的改善在遥感图象和计算机视觉的一些应用中非常必要。已有的一些空间分辨率改善的频域方法是针对多幅相同空间分辨率图象的频谱解混叠方法。本文从分辨率低的欠采样图象会导致相应空间频率域频谱混叠的理论出发，给出了多次欠采样图象在频率域混叠的更一般的公式，并给出一种针对不同分辨率图象解频谱混叠的逐行迭代方法。最后通过计算机仿真实验对本文所述理论进行了验证。实验表明，该迭代解法在有噪声情形下也具有很好的收敛性。     

基于并行遗传算法的图像超分辨率复原

图像超分辨率复原技术，提供了一种利用低分辨率像机获取高分辨率图像的可能途径。图像超分辨率复原有频域方法和空域方法两类：其中频域方法主要基于频谱解混叠；空域方法又分迭代反投影方法、凸集投影方法、Bayesian估计方法等。为了提高图像超分辨率复原的效率和提高复原图像的质量，提出了一种基于并行遗传算法的图像(序列)超分辨率复原的新框架方法，由于遗传算法采用实值编码方式，且基于岛模型的并行机制也有利于多帧图像信息的融合，因而使得算法直观和高效；同时提出采用其他超分辨率复原方法的迭代形式来充当遗传算法的变异算子，因为它能有效地利用已有方法的优点。最后，借用图像复原的客观评价指标来评价超分辨率复原算法的效果。实验证明，该方法有效可行。     

Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified Differential Evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no-reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend.     

一种基于多层次MRF的多源图象融合算法

图象融合技术的主要目的是将多种图象传感器数据中的互补信息组合起来 ,使形成的新图象更适合于计算机处理 (如分割、特征提取和目标识别 )等 .在多层次 MRF模型的基础上 ,提出了一种应用于多源图象分类的图象融合算法 .该融合算法将定义在多层次图结构上的非线性因果 Markov模型与贝叶斯 SMAP(sequential m axi-mum a posteriori)最优化准则结合起来 ,克服了 MAP(maximum a posteriori)准则在多层次图结构上计算不合理的缺陷 .该算法可应用于多源遥感图象中的信息融合 ,使像素分类更精确 ,并解决多源海量数据的富集表示 .另外还利用合成图象与自然图象分别针对多层次 MRF模型的改进及算法中可最优化准则的不同进行了对比实验 ,结果表明 ,该算法具有许多优越性     

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.     

基于WPS快速构建面向专业应用的WEB遥感影像处理应用系统试验研究

卫星遥感技术的飞速发展,促进了遥感数据的应用从专业领域向更加广阔的领域发展,这就需要提供应用简单、使用方便、不依赖于专业遥感影像处理系统的应用工具为各领域的专业人员、公众使用,OGC的WPS服务为基于通用浏览器的遥感影像处理应用提供了可能。本文尝试了以ERDAS APOLLO SERVER为服务器基于WPS服务快速构建网络遥感影像处理应用的一种新框架。基于该框架开发的系统很好地解决了海量影像数据的管理、网络影像应用功能的提供、网络影像处理功能使用数据的输入/输出、功能和数据的安全性等问题,并初步构建了可实际应用的通用影像处理应用系统。