Recovering spectral data from natural scenes with an RGB digital camera and colored filters

Many spectral‐recovery methods using RGB digital cameras assume the underlying smoothness of illuminant and reflectance spectra, and apply low‐dimensional linear models. The aim of the present work was to test whether a direct‐mapping method could be used instead of a linear‐models approach to recover spectral radiances and reflectances from natural scenes with an RGB digital camera and colored filters. In computer simulations, a conventional RGB digital camera with up to three colored filters was used to image scenes drawn from a hyperspectral image database. Three measures were used to evaluate recovery with the direct‐mapping method: goodness‐of‐fit, root‐mean‐square error, and a color‐difference metric. It was found that with two and three filters both spectral radiances and reflectances could be recovered sufficiently accurately for many practical applications. With little increase in computational complexity, an RGB camera and a few colored filters can provide significantly better recovery of natural scenes than an RGB camera alone. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 352–360, 2007     

ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation and Registration Nets” (ASRNet) for the simultaneous estimation of the blood vessel segmentation and the registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills the blood vessel segmentation task, and (ii) A registration module R that estimates the spatial correspondence of an image pair. Based on the segmention-driven registration network, we train the segmentation network using a semi-supervised adversarial learning strategy. Our experimental results show that the proposed ASRNet can achieve state-of-the-art accuracy in segmentation and registration tasks performed with real MSI datasets.     

基于SPOT-5多光谱影像的矿区塌塘水体提取方法研究

针对基于遥感影像的水体提取方法存在水体提取不完整和误提的现象,提出了一种基于SPOT-5多光谱影像的矿区塌塘水体提取方法。在利用波段合成增加一个可用波段的基础上对已有的水体提取方法进行适当的改进,并基于决策树分类器和改进后的方法进行矿区水体的四级提取,保证了水体提取的完整性,同时减少了误提率;最后利用实测数据对水体提取的精度进行了评定。试验结果表明,基于决策树分类器的水体提取方法具有较高的精度,能满足矿区实际应用的需要。     

多光谱颜色复制技术研究发展

针对基于色度的颜色再现技术存在同色异谱的问题,综述了基于多光谱的颜色复制技术框架。总结了多光谱颜色复制的7个关键技术,即多光谱颜色复制系统的设计、多光谱图像的获取、多光谱图像的融合、光谱反射率的重建、多光谱图像降维、光谱分色算法与模型开发以及光谱颜色管理技术,并指出了多光谱颜色复制技术的研究思路和前景。     

多光谱掌脉和掌纹离焦图像融合方法

非接触条件下单模态手部图像纹理信息有限,不利于识别。本文提出利用同一装置在多光谱下分时快速获取掌纹和掌脉图像。通过掌纹图像在静脉图像中进行掌纹主线增强处理,达到增加纹理信息、改善识别效果的目的。控制掌纹、掌脉两幅图像获取间隔小于0.1s,忽略手掌细微空间移位,对获取的掌纹图像进行感兴趣区域(Region of interest,ROI)提取,并利用掌纹ROI坐标参数进行静脉图像的ROI提取。然后对两个ROI区域进行预处理,通过多层小波分解得到的掌纹高频信息快速定位掌纹主线。最后根据掌纹主线归一化后的高频分量,进行掌纹和掌脉小波系数的融合,最终得到在静脉纹理图像中增强掌纹主线纹理的融合图像。实验结果表明,融合后的图像中在保持静脉纹理信息原有状态下,掌纹主线纹理信息明显增加,识别效果得到改善。     

基于自组织映射和模糊隶属度的混合像元分解

遥感图像中普遍存在着混合像元,将混合像元分解为端元和它们之间混合的丰度,对于高精度的地物识别和定量遥感具有重要意义.结合自组织映射神经网络和模糊理论中的模糊隶属度,提出一种新的多光谱和高光谱遥感图像混合像元分解的方法.首先对自组织映射神经网络进行有监督的训练,然后基于模糊模型对混合像元进行分解.其分解结果自动满足混合像元分解问题所要求的2个约束:丰度值非负约束及丰度值和为1约束.实验结果表明,该方法不仅适用于线性光谱混合的情况,也适用于非线性光谱混合的情况,能够获得较好的混合像元分解结果,同时具有较强的抗噪声能力.     

多光谱图像Clifford拟微分算子及应用

采用Clifford代数理论处理多光谱图像不同于传统代数的处理方法,其可有效利用光谱层之间的联系信息.本文通过分析Clifford微分,提出了多光谱图像的Clifford拟微分算子,丰富了多光谱图像处理的Clifford代数理论体系.在边缘识别仿真实验中,与最大熵法相比较,本文提出的以Clifford拟微分算子理论为核心的边缘检测算法识别模糊边界能力更强.在医学CT影像辅助诊断边缘检测实验中,新算法能有效凸显病灶,提高了诊疗准确性和诊断效率.     

Non-Linear Scale-Spaces Isomorphic to the Linear Case with Applications to Scalar,Vector and Multispectral Images

A basic requirement of scale-space representations in general is that of scale causality, which states that local extrema in the image should not be enhanced when resolution is diminished. We consider a special class of nonlinear scale-spaces consistent with this constraint, which can be linearised by a suitable isomorphism in the grey-scale domain so as to reproduce the familiar Gaussian scale-space. We consider instances in which nonlinear representations may be the preferred choice, as well as instances in which they enter by necessity. We also establish their relation to morphological scale-space representations based on a quadratic structuring function.     

基于NSCT和遗传算法的SAR图像和多光谱图像融合

合成孔径雷达(SAR)图像与多光谱图像成像机理和光谱特性差异较大,一般的融合方法很难取得满意的融合结果。文章提出了一种基于Nonsuosampled Contourlet transform(NSCT)和遗传算法的融合算法,首先将经过预处理后的图像进行NSCT分解,低频系数采取区域信息熵最大的准则融合;高频子带计算区域相关性,对相关性在不同阈值范围内的系数进行融合,阈值的选取采用遗传算法进行搜索;最后对融合系数进行NSCT逆变换,得到融合结果。仿真结果表明该算法显著优于基于像素点和基于区域的融合方法。     

Combining kriging, multispectral and multimodal microscopy to resolve malaria-infected erythrocyte contents

In this communication, we demonstrate how kriging, combine with multispectral and multimodal microscopy can enhance the resolution of malaria-infected images and provide more details on their composition, for analysis and diagnosis. The results of this interpolation applied to the two principal components of multispectral and multimodal images illustrate that the examination of the content of Plasmodium falciparum infected human erythrocyte is improved.