微操作系统中微型物体上点的空间坐标的计算方法研究

为操作者提供微型物体的完整的、放大的三维信息,就可以使操作者像在他日常生活中那样精密地控制所使用的工具和被操作对象,解决了在显微镜下操作者的视野和观察方向受到限制以及缺少第三维信息的问题,而为了实时、无遮挡地生成物体的三维虚拟放大影像,需要获得被操作对象不同角度的图像信息并采用简洁、快速的算法以满足实时性要求.介绍在一个人类级微型物体虚拟遥操作系统中根据不同角度拍摄的图像计算物体上点的空间坐标时所采用的一些算法.     

基于Red-Black小波变换的多光谱图像融合方法

针对张量积小波在多光谱图像融合中的不足,提出了一种基于Red-Black小波变换的多光谱图像和高空间分辨率全色图像融合方法.首先对多光谱图像进行IHS变换,将得到的亮度分量和高空间分辨率全色图像进行直方图匹配,并分别作多尺度Red-Black小波分解,然后对低频部分采用加权平均、高频部分采用替代的融合算法对分解子图像进行融合,最后对融合后的各级子图像进行Red-Black重构和IHS逆变换得到融合结果图像,采用客观性能指标对融合结果图像进行了评价.实验结果表明,该方法有较好的融合效果,其保持光谱质量和空间分辨率信息的能力比基于IHS变换融合方法、基于DWT的融合方法和基于IHS-DWT的融合方法都强.     

基于压缩感知的红外与可见光图像融合

基于压缩感知理论提出了一种红外与可见光图像的融合新方法。该方法将Contourlet变换(CT)和小波变换(WT)相结合,以进一步增加变换后系数的稀疏性,同时对采样模式和融合规则进行改进。首先对图像进行Contourlet变换,再对各高层分解系数进行正交小波变换;然后使用各层采样率不同的分立双放射形采样矩阵对系数采样,并用不同的规则对各层采样值进行融合;最后使用非线性共轭梯度法重构融合图像。实验结果表明,在采样率为0.5时,本文方法融合图像的细节信息比小波方法和小波变换压缩感知(WTCS)方法更加丰富;在所有采样率上,本文方法的融合效果比WTCS法在互信息、空间频率和融合信息逼真度等客观融合质量评价指标上均提高约10%。     

Application of convolutional neural network to acquisition of clear images for objects with large vertical size in stereo light microscope vision system

For an object with large vertical size that exceeds the certain depth of a stereo light microscope (SLM), its image will be blurred. To obtain clear images, we proposed an image fusion method based on the convolutional neural network (CNN) for the microscopic image sequence. The CNN was designed to discriminate clear and blurred pixels in the source images according to the neighborhood information. To train the CNN, a training set that contained correctly labeled clear and blurred images was created from an open‐access database. The image sequence to be fused was aligned at first. The trained CNN was then used to measure the activity level of each pixel in the aligned source images. The fused image was obtained by taking the pixels with the highest activity levels in the source image sequence. The performance was evaluated using five microscopic image sequences. Compared with other two fusion methods, the proposed method obtained better performance in terms of both visual quality and objective assessment. It is suitable for fusion of the SLM image sequence.     

铁谱磨粒图像多焦点融合算法研究

铁谱分析技术是一种常用的磨损监测技术。受限于高倍物镜下的景深限制,一张铁谱大磨粒图像往往只有局部聚焦清晰的特征。为了能够解决在高倍物镜下铁谱大磨粒图像的自动化清晰采集以及高质量图像融合问题,设计并构建一套自动化扫描显微系统,该系统可进行多焦点铁谱图像的自动扫描采集;同时,提出一种基于相位一致性的铁谱磨粒图像多焦点融合算法,对自动扫描的多焦点图像进行融合,得到清晰的磨粒图像。实验结果表明,设计的自动化扫描显微系统能快速完成多焦点铁谱图像的自动化采集流程,提出的图像融合算法相较于传统的小波图像融合算法具有更高的图像评价质量,并能获得更加清晰的图像边缘信息。     

Study on clear stereo image pair acquisition method for small objects with big vertical size in SLM vision system

Microscopic vision system with stereo light microscope (SLM) has been applied to surface profile measurement. If the vertical size of a small object exceeds the range of depth, its images will contain clear and fuzzy image regions. Hence, in order to obtain clear stereo images, we propose a microscopic sequence image fusion method which is suitable for SLM vision system. First, a solution to capture and align image sequence is designed, which outputs an aligning stereo images. Second, we decompose stereo image sequence by wavelet analysis theory, and obtain a series of high and low frequency coefficients with different resolutions. Then fused stereo images are output based on the high and low frequency coefficient fusion rules proposed in this article. The results show that Δw₁(Δw₂) and ΔZ of stereo images in a sequence have linear relationship. Hence, a procedure for image alignment is necessary before image fusion. In contrast with other image fusion methods, our method can output clear fused stereo images with better performance, which is suitable for SLM vision system, and very helpful for avoiding image fuzzy caused by big vertical size of small objects. Microsc. Res. Tech. 79:408–421, 2016. © 2016 Wiley Periodicals, Inc.     

基于Curvelet变换的遥感图像融合研究

Curvelet变换是继小波变换之后,更适合图像处理的一种新的多尺度变换分析方法,相比小波而言,它更加适合分析二维图像中的曲线或直线状边缘特征,而且具有更高的逼近精度和更好的稀疏表达能力,同时也具有很强的方向性.本文论述了Curvelet变换的理论和实现算法,基于考虑图像中的那些弱的边缘,提出了一种利用Curvelet变换进行遥感图像融合的方法.实验结果分析表明:将Curvelet变换引入图像融合,能够更好地提取原始图像特征,为融合图像提供更多信息,使融合图像在较好地保留光谱信息的同时,空间细节信息得到增强,优于典型的IHS变换、主分量变换及小波变换图像合方法.     

Integrated approach for segmentation of 3-D confocal images of a tissue specimen

In this article we have proposed an integrated approach for segmentation of cells in volumetric image data obtained using the Confocal Microscope. The volumetric images are the stack of two-dimensional (2-D) images. Segmentation of cells in such an image stack is a difficult problem due to the complex structure of the objects and the spatial relationship of the object signatures in different image slices of the image stack. Here we have proposed a segmentation technique, which is a combination of several known and novel segmentation methods. Low-level techniques such as edge operators, middle-level techniques such as 3-D watershed, rule-based merging, and a high level technique, active surface model optimization, are integrated in one approach to get better segmentation with less human interaction. Some image enhancement and noise reduction techniques are also used to reduce the error in intermediate stages and speed up the segmentation process. Results are shown on 3-D images of prostate cancer tissue specimen.     

Correcting the axial shrinkage of skeletal muscle thick sections visualized by confocal microscopy

Confocal microscopy is a suitable method for measurements and visualization of skeletal muscle fibres and the neighbouring capillaries. When using 3D images of thick sections the tissue deformation effects should be avoided. We studied the deformation in thick sections of the rat skeletal muscle from complete stacks of images captured with confocal microscope. We measured the apparent thickness of the stacks and compared it to the slice thickness deduced from calibrated microtome settings. The ratio of both values yielded the axial scaling factor for every image stack. Careful sample preparation and treatment of the tissue cryosections with cold Ringer solution minimize the tissue deformation. We conclude that rescaling by the inverse of the axial scaling factor of the stack of optical slices in the direction of the microscope optical axis satisfactorily corrects the axial deformation of skeletal muscle samples.     

Multifocus watermarking approach based on discrete cosine transform

Image fusion process consolidates data and information from various images of same sight into a solitary image. Each of the source images might speak to a fractional perspective of the scene, and contains both “pertinent” and “immaterial” information. In this study, a new image fusion method is proposed utilizing the Discrete Cosine Transform (DCT) to join the source image into a solitary minimized image containing more exact depiction of the sight than any of the individual source images. In addition, the fused image comes out with most ideal quality image without bending appearance or loss of data. DCT algorithm is considered efficient in image fusion. The proposed scheme is performed in five steps: (1) RGB colour image (input image) is split into three channels R, G, and B for source images. (2) DCT algorithm is applied to each channel (R, G, and B). (3) The variance values are computed for the corresponding 8 × 8 blocks of each channel. (4) Each block of R of source images is compared with each other based on the variance value and then the block with maximum variance value is selected to be the block in the new image. This process is repeated for all channels of source images. (5) Inverse discrete cosine transform is applied on each fused channel to convert coefficient values to pixel values, and then combined all the channels to generate the fused image. The proposed technique can potentially solve the problem of unwanted side effects such as blurring or blocking artifacts by reducing the quality of the subsequent image in image fusion process. The proposed approach is evaluated using three measurement units: the average of Q^abf, standard deviation, and peak Signal Noise Rate. The experimental results of this proposed technique have shown good results as compared with older techniques. Microsc. Res. Tech. 79:431–437, 2016. © 2016 Wiley Periodicals, Inc.     

Design and performance of a combined secondary ion mass spectrometry-scanning probe microscopy instrument for high sensitivity and high-resolution elemental three-dimensional analysis

State-of-the-art secondary ion mass spectrometry (SIMS) instruments allow producing 3D chemical mappings with excellent sensitivity and spatial resolution. Several important artifacts however arise from the fact that SIMS 3D mapping does not take into account the surface topography of the sample. In order to correct these artifacts, we have integrated a specially developed scanning probe microscopy (SPM) system into a commercial Cameca NanoSIMS 50 instrument. This new SPM module, which was designed as a DN200CF flange-mounted bolt-on accessory, includes a new high-precision sample stage, a scanner with a range of 100 μm in x and y direction, and a dedicated SPM head which can be operated in the atomic force microscopy (AFM) and Kelvin probe force microscopy modes. Topographical information gained from AFM measurements taken before, during, and after SIMS analysis as well as the SIMS data are automatically compiled into an accurate 3D reconstruction using the software program "SARINA," which was developed for this first combined SIMS-SPM instrument. The achievable lateral resolutions are 6 nm in the SPM mode and 45 nm in the SIMS mode. Elemental 3D images obtained with our integrated SIMS-SPM instrument on Al/Cu and polystyrene/poly(methyl methacrylate) samples demonstrate the advantages of the combined SIMS-SPM approach.     

Accurate shape from focus based on focus adjustment in optical microscopy

Optical microscopy allows a magnified view of the sample while decreasing the depth of focus. Although the acquired images from limited depth of field have both blurred and focused regions, they can provide depth information. The technique to estimate the depth and 3D shape of an object from the images of the same sample obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus–sharpness–is the crucial part for final 3D shape estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that have approximately the same initial depth as of the center pixel and then it re-adjusts the center value accordingly. Experiments were conducted on synthetic and microscopic objects, and the results show that the proposed technique generates better shape and takes less computation time in comparison with previous SFF methods based on focused image surface (FIS) and dynamic programming. Microsc. Res. Tech., 2009. © 2008 Wiley-Liss, Inc.     

自适应性多模态特征融合的远小困难目标检测

为了解决由LiDAR点云稀疏性和语义信息不足造成的远小困难物体检测困难的问题,提出了一种多模态数据自适应性融合的3D目标检测网络,充分融合了体素的多邻域上下文信息和图片多层语义信息。首先,设计了一种更适用于检测任务的改进残差网络,提取图片多层语义特征的同时,在低分辨率特征图中有效保留了远小物体的结构细节信息。每个特征图进一步通过来自所有后续特征图的语义信息进行语义增强。其次,提取具有不同感受野大小的多邻域上下文信息,弥补远小物体点云信息不足的缺陷,加强体素特征的结构信息和语义信息,以提高体素特征对物体空间结构和语义信息的表征能力及特征鲁棒性。最后,提出了一种多模态特征自适应融合策略,通过可学习权重,根据不同模态特征对检测任务的贡献程度进行自适应性融合。此外,体素注意力根据融合特征进一步加强有效目标对象的特征表达。在KITTI数据集上的实验结果表明,本方法以明显的优势优于VoxelNet,即在中等难度和困难难度下AP分别提高8.78%和5.49%。同时,与许多主流的多模态方法相比,本方法在远小困难物体的检测性能上具有更高的检测性能,即在中等和困难难度级别上,AP的性能比MVX-Net AP均高出1%。     

红外双波段图像实时融合系统

选用高性能定点数字信号处理器DM642作融合系统核心处理器,S3C2410作辅助控制处理器,利用DM642的高速图像处理性能及它的视频编/解码单元无缝连接的灵活可配置视频端口,结合S3C2410强大的控制能力,研制了探测中波红外和长波红外的双波段图像实时融合系统。将一种基于离散小波变换的图像融合算法应用于红外双波段图像的融合,针对实时嵌入式系统的特点,对算法进行了优化,并将其移植在以DSP+ARM架构的嵌入式平台上。实验结果表明,该融合算法经仿真优化后,只需39.6ms即可完成两幅大小为320pixel×240pixel的红外图像的实时融合,满足25frame/s的工程需求,且融合后的图像能突出中波和长波红外各自的特点。     

基于有效区域选取的变电压 X-射线图像融合

针对变电压 X-射线图像序列融合有效区域的提取问题,本文提出了一种依据图像质量来判断有效区域灰度区间的方法,提高了提取的自适应性。首先,考虑图像直方图均衡性和灰度区间宽度,建立图像质量评价模型。然后求解模型,并依据所得灰度区间提取有效区域。最后,利用灰度变换函数融合各有效区域,获取被检测对象完整结构信息的图像。本文以缸盖为实验对象,验证了该方法的有效性。     

基于多尺度特征融合网络的多聚焦图像融合技术

多聚焦图像融合技术是为了突破传统相机景深的限制,将焦点不同的多幅图像合成一幅全聚焦图像,以获得更加全面的信息。以往基于空间域和基于变换域的方法,需要手动进行活动水平的测量和融合规则的设计,较为复杂。所提出的方法与传统的神经网络相比增加了提取浅层特征信息的部分,提高了分类准确率。将源图像输入训练好的多尺度特征网络中获得初始焦点图,然后对焦点图进行后处理,最后使用逐像素加权平均规则获得全聚焦融合图像。实验结果表明,本文方法融合而成的全聚焦图像清晰度高,保有细节丰富且失真度小,主、客观评价结果均优于其他方法。     

多光谱 CCD 相机几何标定技术研究

多光谱CCD相机的技术特点是利用不同遥感谱段获得同一目标图像,通过不同谱段图像的组合,获取地物目标的物理特性。为了使多光谱CCD相机同时还具备测绘功能,能对获取的地物目标进行精确定位,发射前需要在实验室对多光谱CCD相机的几何参数进行精确标定。多光谱CCD相机几何标定是相机研制过程中的关键环节,是确定相机在惯性坐标系中姿态的必不可少的关键技术。本文着重介绍了多光谱CCD相机几何标定设备、标定方法和标定精度。多次标定结果表明：主距的标定精度优于10μm （1σ）,主点的标定精度优于2.1μm （1σ）,畸变的标定精度优于1.5μm （1σ）,满足多光谱CCD相机几何标定精度的要求。     

多层次信息融合在铁谱图像磨粒识别中的应用

针对铁谱图像磨粒识别中异类信息综合利用率较低的问题,提出多层次信息融合的铁谱图像磨粒识别方法。首先,在铁谱图像二值化分割的基础上进行二值滤波,结合彩色铁谱图的R、G、B三分量,实现铁谱图像的彩色滤波。其次,以实际采集的磨粒图像样本为例,提取滤波后二值图像的形态特征,以及滤波后彩色图像的颜色特征;在特征层利用PCA对异类特征进行维数约简,并结合SVM和k-fold交叉验证,实现形态特征和颜色特征的特征层融合;在决策层将异类特征的SVM概率输出结果作为D-S证据理论的基本概率分配函数,实现形态特征和颜色特征的决策层融合。通过与形态学滤波结果对比,验证了本文提出滤波方法的优越性;其次,不同层次的信息融合结果表明,与单独使用颜色特征和形态特征相比,异类信息融合后可实现优势互补,有效提高故障磨粒的识别准确率。     

一种快速简单多焦面显微图像融合方法

开发了一个用于多焦面显微图像融合的插件,该插件能支持任意大小的图像处理,由输入的多幅不同聚焦面的局部清晰显微图像,融合得到完整清晰的图像。重点研究了基于小波变换的多焦面显微图像融合的算法,通过Matlab仿真确定较优算法,采用C++语言编写插件,实现图像融合的功能。该融合插件操作简便,融合效果好,用软件的方法扩大了显微物镜的焦深。     

基于分层模型与局部复原的多聚焦图像融合方法

针对多聚焦图像融合后清晰像素信息损失较为严重的问题,提出一种分层模型与局部复原相结合的方法。首先,根据源图像模糊区域分布情况,对不同灰度特征源图像进行多尺度分解选取恰当的分层模型,并利用此模型对图像进行初步融合;在此基础上,通过计算对应区域的功率谱曲线斜率以推导图像中局部模糊区域像素值,提高图像融合算法的细节提取能力,再结合维纳滤波策略对其进行恢复,以实现图像清晰融合。最后,利用3个实例验证了该方法的有效性与可行性。