基于光饱和影响校正的作物叶绿素分布光谱成像检测

叶绿素含量是作物光合能力与营养评价的重要指标,因此快速检测作物叶绿素含量与分布可为作物营养动态分析与长势评估提供支持。基于RGB(Red, Green, Blue)和NIR(Near Infrared)多光谱图像的获取,开展玉米作物营养状态分布光谱学成像检测。构建了多光谱图像采集平台获取RGB和NIR图像,研究了基于光饱和校正算法的RGB图像的光饱和校正与NIR图像去噪方法,通过图像的匹配分割,冠层的提取校正,建立了基于冠层图像的作物SPAD值检测模型与分布成图。采集15株玉米植株RGB-NIR图像,并同步获取不同植株,不同位置共68个叶绿素含量指标SPAD值。首先对RGB图像进行光饱和校正,再对NIR图像进行滤波和图像增强,其次对RGB和NIR图像进行了SURF(speeded-up robust features)和RANSAC(random sample consensus)图像匹配,利用RGB图像的颜色特征,采用ExG(Extra Green)和OTSU算法生成分割掩模,对RGB图像和NIR图像进行分割提取,提取图像的R, G, B和NIR分量,利用4阶灰度板进行反射率校正,然后计算作物图像中像素级P_SPAD值,并建立图像P_SPAD值与叶绿素仪SPAD值的拟合模型,最后绘制作物SPAD分布图。通过HSI(Hue, Saturation, Intensity)彩色模型中的I分量直方图对比去饱和前后光分布范围,以作物SPAD值分布图验证光饱和校正算法对作物叶绿素含量分布检测提升的效果。RGB图像光饱和校正前I分量集中在[140～180]之间,光饱和校正后的RGB图像I分量集中在[85～130]之间, 校正了相机成像时产生模糊和RGB图像饱和。对分割后的RGB图像和NIR图像提取R,G,B,NIR分量进行4阶灰度板校正,相关系数分别为0.829,0.828,0.745和0.994,进而生成R,G,B和NIR四波段的反射率伪彩色图像,反射率R_NIR＞R_C＞R_R＞R_B。体现了作物的在蓝光和红光区域吸收光,在绿光区域和近红外区域反射光的光谱特性。校正前后的R和NIR分量反射率计算图像P_SPAD值拟合叶绿素含量指标SPAD值的模型结果显示,校正前R2为0.332 6,校正后R2为0.619 3,绘制作物的SPAD特征分布图,可为作物的营养动态快速分析与分布检测提供技术支持。     

近红外光谱技术快速预测泡桐活立木年轮密度

应用近红外光谱分析技术主要对兰考泡桐活立木年轮密度进行了研究。取样时采用无损的方式,即不伐木,只从样木胸径处钻取直径为5 mm的木芯;用近红外光谱仪采用自动进级式采集样品光谱,波谱范围为350～2 500 nm;密度值采用水银体积仪测量;校正模型和预测模型建立与分析采用偏最小二乘和完全交互验证法。研究结果表明,中径级和大径级泡桐所选年轮数与近红外光谱值之间均存在很好的相关关系,预测模型相关系数分别达0.88和0.91;中径极的泡桐年轮密度与近红外光谱的校正模型和预测模型相关系数分别达到0.90和0.83,校正标准误差(SEC)和预测标准误差(SEP)分别为0.012和0.016。由此可见,该法可简单、快速、无损的预测中径级即接近采伐龄泡桐活立木的年轮密度。     

改进(2D)~2PCA的彩色图像融合框架

针对彩色图像融合时空间变换产生的色彩畸变以及红绿蓝(RGB)色彩空间各通道间的强相关性,同时考虑到基于主元分析(PCA)的图像融合算法存在图像结构利用率低、光谱信息损失多的缺点,提出了一种基于改进双向二维主元分析[(2D)2PCA]的图像融合框架。针对RGB色彩图像的结构特点,以待融合图像行、列方向的RGB分量作为基元进行二维主元分析(2DPCA),采用基于协方差的线性权重分配方法对融合图像进行重构,依照重构图像的结构特性进行主元替换,经基于协方差的加权逆变换得到融合图像。为验证算法的有效性进行了二次实验:1)是选取模糊彩色图像与对应的清晰灰度图像;2)是彩色可见光图像与对应的红外图像进行实验。实验结果表明使用该方法得到的融合图像可取得较好空间分辨率和理想的融合指标。     

新型分子高光谱成像系统性能分析及数据预处理

将分子成像技术和高光谱技术相结合,研制了基于AOTF(Acousto-optic Tunable Filters)的分子高光谱成像系统。系统由显微镜、分光仪、CCD镜头、图像数据采集卡和计算机等几部分组成。在综合考虑各功能部件的性能及相互的制约关系的基础上,分析了系统的性能指标,系统的光谱范围从550～1 000 nm,可采集200个波段,空间分辨率可达0.061 5 μm,光谱分辨率可达2 nm,当CCD工作在积分模式下采集速度可达到2.612 5 s·B-1,当CCD工作在非积分模式下可达到约0.11 μs·B-1。由于受系统光源和光路中透镜及传感器性能的影响,采集到的图像数据需要进行预处理,文中提出一种空间维和光谱维联合校正的灰度校正系数算法,并给出算法的具体实现。以白血病的血液作样本,通过对比校正前后的单波段图像、伪彩色图像和光谱曲线,说明校正算法的有效性,为后续的光谱图像数据分析提供了有效的数据。     

CCD立靶光源适用性的图像判定技术

针对CCD立靶在室外自然环境下和配有主动光源的工程应用,提出一种基于背景图像分析的光源适用性判定方法。在图像采集与实际测试状态一致的条件下,采集无目标背景图像,通过数字图像处理,确定采集视场内各个位置处图像的灰度变化范围f及灰度均值fmean,若背景图像中各个像元的fmean2f,即认为CCD立靶可以对目标图像进行可靠识别。实验室及外场CCD立靶应用实验结果验证了该判据的有效性和可靠性。     

具有光照强度不变性的显微细胞图像处理算法

显微图像受光源光照强度的影响很大,研究了不受光照强度影响的显微细胞图像的增强技术。对显微灰度图像,用Top Hat算法对细胞进行了突出;对于显微彩色图像,先由图像的RGB空间变换到IHS(Intensity,HueandSaturation)空间,然后,通过对分量提取细胞的颜色信息。实验表明,以上两种方法消除了光照强度变化对图像的影响。     

光纤面板透过率分布检测方法

通过建立光纤面板图像中的透过率与灰度的对应关系,运用数字图像采集、处理技术和面阵CCD成像技术,设计了用于测量光纤面板透过率分布的实验系统。通过实验给出了该系统的测量结果,并验证了该检测方法的可行性。     

自由飞行昆虫运动变形测量中弱光条提取

通过分析昆虫自由飞行状态下结构光光条图像的特点,提出了一种用于提取昆虫运动变形测量中弱光条中心点的图像处理方法。该方法根据图像的灰度分布情况对图像进行分块,采用小波理论对图像消噪,利用模糊增强和小波同态滤波相结合的算法增强各图像的弱光条信号,再应用steger算法提取弱光条中心点的亚像素位置。给出几种算法实验结果的比较和分析,表明该方法成功实现了弱光条信号中心点的提取,并有效抑制了噪声和干扰。     

结合计算全息与混沌的彩色图像加密方法

针对现有彩色图像光学加密方法存在解密结果失真的问题,提出一种结合混沌运算与菲涅耳衍射全息的彩色图像单通道加密新方法.首次加密操作利用菲涅耳衍射将彩色图像RGB通道分量转换成一幅实值计算全息图;第二次加密操作是利用改造的Logistic混沌系统对计算全息图像素进行置换与扩散.结果 表明,本文方法除传统混沌系统密钥以外,菲涅耳衍射距离、参考光波长和入射角方向余弦作为关键密钥均可以增大密钥空间(约为10249),而且具有较小的密钥体积;解密图像的保真度高且相邻像素相关性、信息熵、像素数改变率和归一化改变强度等评价指标均接近理想值;密文图像的直方图平坦,灰度分布均匀,完全隐藏了原始彩色图像的灰度和色彩信息.     

窄带快照式多光谱成像色彩还原方法

由于兼具光谱分辨和空间分辨能力, 快照式窄带多光谱成像在资源遥感、精准农业、医疗检测等领域将有广泛应用。由于该方法使用窄带成像来提高光谱分辨率及图像对比度,所获得的图像是灰度信息,失去了场景的色彩信息,不便专家对图像鉴别、评价与赏析。已有的色彩还原算法主要针对光谱波段带宽较宽或者多个波段叠加基本覆盖整个可见光谱范围等两种光谱成像仪,不适合窄带多光谱成像方法的色彩还原。该研究适合于快照式窄带多光谱成像的色彩还原方法,提出建立窄带多光谱彩色相机的概念。首先,提出两种窄带多光谱色彩还原方法：(1)基于CIE色度系统三刺激值色的,(2)基于贝尔阵列插值算法的;其次,分别应用两种算法还原快照式窄带多光谱相机所获得的植物、手臂及宫颈组织等三种代表性场景窄带多光谱灰度图像;之后,计算并比较表征两种算法所得的代表性场景彩色图像的均值、方差、熵及梯度等表征图像质量的参数数值,确定出适合快照式窄带多光谱成像的色彩还原方法;最后,对所确定的色彩还原算法进行色偏校正。实验结果表明,基于CIE三刺激值色彩还原方法比贝尔阵列插值法更适用于窄带多光谱成像颜色复原。配合使用CIE三刺激值色彩还原方法及灰度图象校正算法,从窄带光谱成像所获得的植物、手臂皮肤及宫颈组织的灰度图像所还原出的近彩色图像逼近物体真实色彩,满足人眼观察习惯。介绍了仅覆盖可见光光谱范围30%的窄带多光谱图像进行色彩还原的方法,该方法证明快照式窄带多光谱成像可以兼具光谱分辨能力,同时保持可供人主观辨识的色彩信息。所提出实现快照式窄带多光谱彩色成像的方法,有望设计不同于传统RGB相机的彩色相机实施方案。     

红外和彩色可见光图像亮度-对比度传递融合算法

以红外和彩色可见光图像为研究对象,提出了一种基于亮度-对比度传递（LCT）技术的彩色图像融合算法。首先借助灰度融合方法将红外图像与彩色可见光图像亮度分量融合,然后用LCT技术改善灰度融合结果的亮度和对比度,最后利用快速YCBCR变换融合策略在RGB空间内直接生成彩色融合图像。文中利用像素平均融合法和多分辨率融合法作为不同的灰度融合措施以分别满足高实时性和高融合质量的需求。实验结果表明,提出算法的融合结果不仅具有与输入彩色可见光图像相近的自然色彩,而且具备令人满意的亮度和对比度,即使采用运算简单的像素平均法进行灰度融合,同样可以获得良好的融合效果。     

Retinex-Based Fast Algorithm for Low-Light Image Enhancement

We proposed the Retinex-based fast algorithm (RBFA) to achieve low-light image enhancement in this paper, which can restore information that is covered by low illuminance. The proposed algorithm consists of the following parts. Firstly, we convert the low-light image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and use the linear function to stretch the original gray level dynamic range of the V component. Then, we estimate the illumination image via adaptive gamma correction and use the Retinex model to achieve the brightness enhancement. After that, we further stretch the gray level dynamic range to avoid low image contrast. Finally, we design another mapping function to achieve color saturation correction and convert the enhanced image from the HSV color space to the RGB color space after which we can obtain the clear image. The experimental results show that the enhanced images with the proposed method have better qualitative and quantitative evaluations and lower computational complexity than other state-of-the-art methods.     

一种基于图像特征和神经网络的苹果图像分割算法

苹果识别是开发苹果采摘机器人的关键环节,利用图像处理技术和神经网络分类器探索苹果图像分割算法.从苹果树图片中选取苹果图像样本和背景网像样本.分别计算这两类图像样本的颜色特征和纹理特征.颜色特征的计算基于RGB色彩模型,纹理特征的计算基于灰度共生矩阵.选取适当的颜色特征(R/B值)和纹理特征(对比度值和相关性值)作为输入节点,利用反向传播神经网络分类器建模,输出值是一个O～1之间的计算值.通过阈值将输出结果分类为苹果或背景.试验结果表明,该算法正确率大于87.6%,对光照的影响不敏感,是一利较为实用的苹果分割算法.     

All-silicon chiral metasurfaces and wavefront shaping assisted by interference

The usage of full-color imaging in digital pathology produces significant results. Compared with a grayscale image or a pseudocolor image containing contrast information, a full-color image can identify and detect the target object better with color texture information. Fourier ptychographic microscopy(FPM) is a high-throughput computational imaging technique that breaks the tradeoff between high resolution(HR) and a large field of view. It also eliminates the artifacts of scanning and stitching in digital pathology and improves its imaging efficiency. However, the conventional full-color digital pathology based on FPM is still time-consuming because of the repeated experiments with tri-wavelengths. A color transfer FPM approach termed "CFPM" was reported. The color texture information of a low-resolution full-color pathologic image is directly transferred to the HR grayscale FPM image captured by only a single wavelength. Both of the color space of FPM based on the standard CIE-XYZ color model and the display based on the standard RGB color space were established. Different FPM colorization schemes were analyzed and compared with 30 biological samples. Three types of evaluation approaches were provided, including the root-mean-square error(RMSE), the difference maps, and the image histogram cosine similarity. The average RMSE values of the conventional method and CFPM compared with the ground truth were 5.3% and 5.7%, respectively. Therefore, the reconstruction time is significantly reduced by 2/3 with the sacrifice of precision of only 0.4%. The CFPM method is also compatible with advanced fast FPM approaches to further reduce computation time.     

Potential for assessing long-term dynamics in soil nitrogen availability from variations in delta15N of tree rings

Numerous researchers have used the isotopic signatures of C, H, and O in tree rings to provide a long-term record of changes in the physiological status, climate, or water-source use of trees. The frequently limiting element N is also found in tree rings, and variation in its isotopic signature may provide insight into long-term changes in soil N availability of a site. However, research has suggested that N is readily translocated among tree ring of different years; such infidelity between the isotopic compositions of the N taken up from the soil and the N contained in the ring of that growth year would obscure the long-term N isotopic record. We used a 15-year 15N-tracer study to assess the degree of N translocation among tree rings in ponderosa pine (Pinus ponderosa) trees growing in a young, mixed-conifer plantation. We also measured delta13C and delta15N values in unlabeled trees to assess the degree of their covariance in wood tissue, and to explore the potential for a biological linkage between them. We found that the maximum delta15N values in rings from the labeled trees occurred in the ring formed one-year after the 15N was applied to the roots. The delta15N value of rings from labeled trees declined exponentially and bidirectionally from this maximum peak, toward younger and older rings. The unlabeled trees showed considerable interannual variation in the delta15N values of their rings (up to 3 and 5 per thousand), but these values correlated poorly between trees over time and differed by as much as 6 per thousand. Removal of extractives from the wood reduced their delta15N value, but the change was fairly small and consistent among unlabeled trees. The delta13C and delta15N values of tree rings were correlated over time in only one of the unlabeled trees. Across all trees, both delta13C values of tree rings and annual stem wood production were well correlated with annual precipitation, suggesting that soil water balance is an important environmental factor controlling both net C gain and transpirational water loss at this site. Our results suggest that interannual translocation of N among tree rings is substantial, but may be predictable enough to remove this source of variation from the tree-ring record, potentially allowing the assessment of long-term changes in soil N availability of a site.     

Switching non-local vector median filter

This paper describes a novel image filtering method that removes random-valued impulse noise superimposed on a natural color image. In impulse noise removal, it is essential to employ a switching-type filtering method, as used in the well-known switching median filter, to preserve the detail of an original image with good quality. In color image filtering, it is generally preferable to deal with the red (R), green (G), and blue (B) components of each pixel of a color image as elements of a vectorized signal, as in the well-known vector median filter, rather than as component-wise signals to prevent a color shift after filtering. By taking these fundamentals into consideration, we propose a switching-type vector median filter with non-local processing that mainly consists of a noise detector and a noise removal filter. Concretely, we propose a noise detector that proactively detects noise-corrupted pixels by focusing attention on the isolation tendencies of pixels of interest not in an input image but in difference images between RGB components. Furthermore, as the noise removal filter, we propose an extended version of the non-local median filter, we proposed previously for grayscale image processing, named the non-local vector median filter, which is designed for color image processing. The proposed method realizes a superior balance between the preservation of detail and impulse noise removal by proactive noise detection and non-local switching vector median filtering, respectively. The effectiveness and validity of the proposed method are verified in a series of experiments using natural color images.     

红外伪彩色图像的去彩化研究

为适应人眼的感知能力,伪彩色编码技术常被用于处理红外探测系统输出的灰度图像。较于灰度图像,计算机对彩色图像处理所需的存储量与计算量较大。鉴于某些红外设备只提供伪红外图像的输出,因此伪红外图像的去彩色化算法成为该文的主要研究目标。除利用最大值分解、平均值与光亮度法3种常见灰度化算法对伪红外图像的处理外,直接对伪彩色图像进行解码的方式也在文中被用于红外图像的去彩色化。实验基于上述两类方法,对线性与正弦非线性彩虹伪彩色图像的去彩色化进行了研究,并对处理后图像的质量进行了比较。实验结果表明,常规灰度算法处理后的伪彩色图像质量较差,而线性伪彩色图像经解码处理后可完全恢复出原图,实现0均方差;对于正弦非线性算法,虽然存在由量化误差引起的损失,但与原图像比仍有非常低的均方差（0.309 4）与高的峰值信噪比（101.356 0）。     

Color-image encryption scheme based on channel fusion and spherical diffraction

A secure encryption scheme for color images based on channel fusion and spherical diffraction is proposed in this paper. In the proposed encryption scheme, a channel fusion technology based on the discrete wavelet transformation is used to transform color images into single-channel grayscale images, firstly. In the process of transformation, the hyperchaotic system is used to permutate and diffuse the information of red—green—blue (RGB) channels to reduce the correlation of channels. Then the fused image is encrypted by spherical diffraction transform. Finally, the complex-valued diffraction result is decomposed into two real parts by the improved equal module decomposition, which are the ciphertext and the private key. Compared with the traditional color image encryption schemes that encrypt RGB channels separately, the proposed scheme is highly secure and robust.     

基于低照度三基色图像去噪及融合彩色图像增强方法研究

现有的各类彩色图像增强方法,主要用于对光照不均匀或弱光条件下已获取的彩色图像进行直接增强,而低照度彩色成像系统获取的彩色图像是由单独采集的三基色图像融合而成。为此提出了基于低照度三基色图像去噪及融合彩色图像增强方法。利用小波变换对低照度条件下独立采集的三基色图像进行去噪;然后将去噪后图像融合成彩色图像,将彩色图像由RGB颜色空间转换为HSV颜色空间;在HSV空间对V进行MSR增强,再对增强后的图像进行S调整;最后再将其转换回RGB颜色空间。采用客观评价方法对选取的去噪增强后图像进行了评价。结果表明:经过去噪和增强后的彩色图像在均值、方差和熵3项指标上均优于三基色直接融合的彩色图像,平均提高幅度为均值11.37%、方差8.46%、信息熵0.44%。该方法可移植到低照度彩色成像系统中,对成像质量的提升具有指导意义。     

彩色分割的航空影像房屋自动检测

航空影像房屋提取方法的研究中大多基于灰度影像的区域生长算法,此类算法不仅忽略了不同材质的房屋所呈现的光谱特征对提取结果的影响,而且过于依赖种子像素的选取,处理效率不高。为了从高分辨率航空影像中实现房屋的自动检测,综合利用彩色信息与屋顶材料的光谱特征,采用影像分割原理,研究了房屋自动检测的方法。首先对RGB与HIS彩色空间进行转换,利用HIS空间各分量间不相关的特点和屋顶材料光谱特征进行影像分割,分离出红色瓦片屋顶与灰色水泥屋顶区域,并利用标记分水岭算法实现房屋区域的初始分割;然后计算各标记区域内的色调均值选取种子像斑样本,进而以像斑为单元在色调分量中进行区域生长,最后经过消除小斑和矩形拟合优化处理,得到轮廓清晰的房屋区域。与传统的基于像素区域分割算法相比,该方法整个过程无需人工干预且均在一维彩色空间进行处理,计算量明显降低,同时采用改进的基于像斑区域生长算法能够兼顾邻近区域内像素的几何结构信息,使算法精度得到显著提高,采用上述方法对高分辨率航空影像进行了实验,结果证明该方法有着较高的处理效率和准确性,具有实用价值。