基于梯度一致性约束的多光谱/全色影像最大后验融合方法

多光谱/全色影像融合可以得到高空间分辨率的多光谱影像,在影像解译和分类等方面具有十分重要的意义。提出一种基于梯度一致性约束的遥感影像融合方法。该方法在最大后验概率框架下,通过梯度一致性约束建立理想高空间分辨率多光谱影像和全色影像之间的关系,并结合多光谱影像观测模型和Huber-Markov影像先验,构建融合目标函数,最后采用梯度下降法求解得到融合影像。本文方法在目标函数中引入了梯度一致性约束,克服了现有的同类方法受限于波段数量的缺陷,同时在求解中自适应确定每个波段的迭代步长,充分顾及了各波段的光谱特性,从而既确保了融合影像的光谱信息保真度,也提高了融合影像的空间信息融入度。通过IKONOS和WorldView-2影像对该方法进行了验证,并和GS,AIHS和AMBF等融合方法从定性和定量两方面进行了比较分析。实验结果表明,相比于其他方法,该方法可以在更好保持光谱信息的同时增强影像的空间分辨率,具有更广泛的适用范围和更佳的融合效果。     

基于改进梯度投影NMF和复Contourlet变换的遥感图像融合

为了尽可能地保留全色图像的空间信息和多光谱图像的光谱信息,提出了一种基于改进梯度投影非负矩阵分解和复Contourlet变换的遥感图像融合方法.首先,以多光谱图像的强度分量图像为标准,对全色图像做直方图匹配,得到新的全色图像;然后,利用复Contourlet变换分别分解多光谱图像的强度分量图像和新的全色图像,得到各自对应的低频分量和高频分量;接着,对两幅低频分量图像采用改进梯度投影的非负矩阵分解作为融合规则获取新的低频分量,并对两幅高频分量图像使用系数绝对值较大法获取新的高频分量;最后,通过逆复Contourlet变换和逆色调-饱和度-强度变换获得融合后的图像.大量实验结果表明,与HSI方法、NMF与无下采样Contourlet变换结合的方法以及提升小波变换与HSI结合的方法相比,本文方法获得的融合图像具有更高的空间分辨率和更多的光谱信息.     

一种基于单元信息熵的目标匹配改进算法

为解决图像信息熵无法有效进行图像匹配的问题,将图像单元信息熵和投影特征相结合,定义了图像单元信息熵,并提出了一种基于单元投影信息熵的图像匹配方法.在单元信息熵的基础上,在各个单元格内进行单元信息熵投影计算,然后按照一定的测度进行计算,从而实现图像的匹配.采用网格分层的搜索算法,加快搜索速度,提高其工程实用性.实验证明:该算法具有良好的抗几何失真能力和抗辐射失真的能力,以及很好的抗噪声干扰的能力,可以准确的进行目标匹配.     

一种快速稀疏分解图像去噪新方法*

提出了一种基于分层树型结构正交匹配追踪算法的快速图像去噪方法.通过选择高斯函数和墨西哥草帽小波母函数构建混合冗余字典,采用分层树状结构表示字典,结合构正交匹配追踪算法,实现图像稀疏表示,提高了图像表示的稀疏性,降低了算法的复杂度.依据噪音能量阈值,通过多次迭代达到图像去噪的目的.实验结果表明,在相同的噪音水平下,该迭代去噪算法取得了较高的较好的PSNR,获得更好的视觉效果.     

基于序列图象对应概率累积的基础矩阵求解方法*

提出一种基于图象序列对应概率累积的基础矩阵求解算法.该算法基于序列对应概率累积求最大熵点的对应概率累积分布图,采用RANSAC思想和加权最小二乘法拟合极线,通过统计分类器求解极点并消除出格极线,并利用所求的对应极线和极点,采用一种参量化方法求解F矩阵.该算法工程实现容易,对输入的源图象没有苛刻的要求,可以基于随机拍摄的图象序列方便地求解F矩阵.     

基于小波域三状态HMT模型的含噪图像增强

针对含噪图像增强问题,提出一种基于小波域三状态隐马尔可夫树模型的方法,采用三状态的高斯混合模型逼近小波系数的分布,不需要设定精确的阈值,依据期望最大算法训练得到的每个系数所属状态的后验概率,将系数区分为噪声系数、弱边缘系数和强边缘系数,然后通过抑制噪声系数,增强细节特征系数来达到对含噪图像增强的目的,并引入循环平移策略避免人工失真.通过对含噪的标准图像和人脑核磁共振图像进行仿真实验,并与几种经典的图像增强方法作视觉上的对比和定量分析.实验结果表明,本文所提出的方法具有很好的鲁棒性,在突出了图像中更多的细节信息的同时,可以有效抑制噪声.     

基于等效节点的子孔径拼接系统公差分析

在子孔径拼接系统中,各子孔径之间的倾斜与位移对整个系统的成像质量有直接影响.本文应用动态光学理论中等效节点概念,根据各个子孔径倾斜及轴向与垂轴位移位移时等效节点也将发生相应移动的原理,利用拼接元件形成像点的位移引起的各个子镜形成像点叠加误差形成的弥散圆尺寸变化之间的关系,简化了子孔径倾斜与位移对拼接系统像质影响的分析与计算.通过在子孔径拼接原理样机结构设计与公差分析中的应用,得出原理样机的倾斜公差为4 μrad,位移公差为6 μm,使原理样机的子孔径装配调整得到了有效控制.     

NMR imaging of white button mushroom (Agaricus bisporis) at various magnetic fields

Nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) have been applied to visualize physiological phenomena in plants and agricultural crops. Imaging sequences that result in contrast of a combination of parameters (e.g., proton density, ) cannot be used for a correct and unique interpretation of the results. In this study multiecho imaging together with monoexponential T₂ decay fitting was applied to determine reliable proton density and T₂ distributions over a mushroom. This was done at three magnetic field strengths (9.4, 4.7, and 0.47 T) because susceptibility inhomogeneities were suspected to influence the T₂ relaxation times negatively, and because the inflences of susceptibility inhomogeneities increase with a rise in magnetic field strength. Electron microscopy was used to understand the different T₂'s for the various tissue types in mushrooms. Large influences of the tissue ultrastructure on the observed T₂ relaxation times were found and explained. Based on the results, it is concluded that imaging mushrooms at low fields (around or below 0.47T) and short echo times has strong advantages over its high-field counterpart, especially with respect to quantitative imaging of the water balance of mushrooms. These conclusions indicate general validity whenever NMR imaging contrast is influenced by susceptibility inhomogeneities.     

内增强变象管相机中的干扰研究

本文讨论了带快门的内增强变象管相机的快门电路及其对扫描电路的干扰,干扰信号对相机性能指标的影响及解决方法。     

Good match exploration for thermal infrared face recognition based on YWF-SIFT with multi-scale fusion

Stable local feature detection is a critical prerequisite in the problem of infrared (IR) face recognition. Recently, Scale Invariant Feature Transform (SIFT) is introduced for feature detection in an infrared face frame, which is achieved by applying a simple and effective averaging window with SIFT termed as Y-styled Window Filter (YWF). However, the thermal IR face frame has an intrinsic characteristic such as lack of feature points (keypoints); therefore, the performance of the YWF-SIFT method will be inevitably influenced when it was used for IR face recognition. In this paper, we propose a novel method combining multi-scale fusion with YWF-SIFT to explore more good feature matches. The multi-scale fusion is performed on a thermal IR frame and a corresponding auxiliary visual frame generated from an off-the-shelf low-cost visual camera. The fused image is more informative, and typically contains much more stable features. Besides, the use of YWF-SIFT method enables us to establish feature correspondences more accurately. Quantitative experimental results demonstrate that our algorithm is able to significantly improve the quantity of feature points by approximately 38%. As a result, the performance of YWF-SIFT with multi-scale fusion is enhanced about 12% in infrared human face recognition.