Image pre-processing research of coal level in underground coal pocket

Mathematical morphology is widely applicated in digital image procesing.Vari- ary morphology construction and algorithm being developed are used in deferent digital image processing.The basic idea of mathematical morphology is to use construction ele- ment measure image morphology for solving understand problem.The article presented advanced cellular neural network that forms mathematical morphological cellular neural network (MMCNN) equation to be suit for mathematical morphology filter.It gave the theo- ries of MMCNN dynamic extent and stable state.It is evidenced that arrived mathematical morphology filter through steady of dynamic process in definite condition.

Research of Image pre-processing Algorithm in Automation Fingerprint Identification

In the paper, a valid method of fingerprint Image pre- processing is introduced. Experiment results show that this kind of algorithm can availably wipe off yawp imported by the incom- plete leave fingerprint - marking of sensor surface when finger- print sensor record fingerprint. Meanwhile, it can extract the ef- fective and uneffective zone of fingerprint effectively, and also further enhance ridge line and vale line of fingerprint so that make the lines of fingerprint clear, continuum, lubricity and has better contrast, at the same time, has quite quick speed, this fingerprint Image pre- processing time can be shorten greatly.

Reconstructing the landing trajectory of the CE-3 lunar probe by using images from the landing camera

An accurate determination of the landing trajectory of Chang＇e-3 （CE-3） is significant for verifying orbital control strategy, optimizing orbital planning, accu- rately determining the landing site of CE-3 and analyzing the geological background of the landing site. Due to complexities involved in the landing process, there are some differences between the planned trajectory and the actual trajectory of CE-3. The land- ing camera on CE-3 recorded a sequence of the landing process with a frequency of 10 frames per second. These images recorded by the landing camera and high-resolution images of the lunar surface are utilized to calculate the position of the probe, so as to reconstruct its precise trajectory. This paper proposes using the method of trajectory reconstruction by Single Image Space Resection to make a detailed study of the hov- ering stage at a height of 100 m above the lunar surface. Analysis of the data shows that the closer CE-3 came to the lunar surface, the higher the spatial resolution of im- ages that were acquired became, and the more accurately the horizontal and vertical position of CE-3 could be determined. The horizontal and vertical accuracies were 7.09 m and 4.27 m respectively during the hovering stage at a height of 100.02 m. The reconstructed trajectory can reflect the change in CE-3＇s position during the powered descent process. A slight movement in CE-3 during the hovering stage is also clearly demonstrated. These results will provide a basis for analysis of orbit control strategy, and it will be conducive to adjustment and optimization of orbit control strategy in follow-up missions.

BACKGROUND RECTIFICATION AND FEATURE EXTRACTION OF IMAGE IN A SPOT WELD OF AL ALLOY X-RAY DETECTION

A primary study on Processing in X - ray inspection of spot weld for aluminum alloy spot welding,in- cluding for background simulation,acquisition of ideal binary image, and extraction and identifi- cation of defect features was presented.

Example-based super-resolution for single-image analysis from the Chang'e-1 Mission

Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution （SR） algorithm is employed to obtain high- resolution （HR） images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.

A Survey on Pre-Processing in Image Matting

Pre-processing is an important step in digital image matting, which aims to classify more accurate foreground and background pixels from the unknown region of the input three-region mask （Trimap）. This step has no relation with the well-known matting equation and only compares color differences between the current unknown pixel and those known pixels. These newly classified pure pixels are then fed to the matting process as samples to improve the quality of the final matte. However, in the research field of image matting, the importance of pre-processing step is still blurry. Moreover, there are no corresponding review articles for this step, and the quantitative comparison of Trimap and alpha mattes after this step still remains unsolved. In this paper, the necessity and the importance of pre-processing step in image matting are firstly discussed in details. Next, current pre-processing methods are introduced by using the following two categories： static thresholding methods and dynamic thresholding methods. Analyses and experimental results show that static thresholding methods, especially the most popular iterative method, can make accurate pixel classifications in those general Trimaps with relatively fewer unknown pixels. However, in a much larger Trimap, there methods are limited by the conservative color and spatial thresholds. In contrast, dynamic thresholding methods can make much aggressive classifications on much difficult cases, but still strongly suffer from noises and false classifications. In addition, the sharp boundary detector is further discussed as a prior of pure pixels. Finally, summaries and a more effective approach are presented for pre-processing compared with the existing methods.

Wavelet Analysis of Space Solar Telescope Images

The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.

A super-high angular resolution principle for coded-mask X-ray imaging beyond the diffraction limit of a single pinhole

High angular resolution X-ray imaging is always useful in astrophysics and solar physics. In principle, it can be performed by using coded-mask imaging with a very long mask-detector distance. Previously, the diffraction-interference effect was thought to degrade coded-mask imaging performance dramatically at the low energy end with its very long mask-detector distance. The diffraction-interference effect is described with numerical calculations, and the diffraction-interference cross correlation reconstruction method （DICC） is developed in order to overcome the imaging performance degradation. Based on the DICC, a super-high angular resolution principle （SHARP） for coded-mask X-ray imaging is proposed. The feasibility of coded mask imaging beyond the diffraction limit of a single pinhole is demonstrated with simulations. With the specification that the mask element size is 50 × 50 μm^2 and the mask-detector distance is 50 m, the achieved angular resolution is 0.32arcsec above about 10keV and 0.36arcsec at 1.24keV （λ = 1 nm）, where diffraction cannot be neglected. The on-axis source location accuracy is better than 0.02 arcsec. Potential applications for solar observations and wide-field X-ray monitors are also briefly discussed.

Image mathematical morphology and image restoration application in detecting underground bin level

By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used based on neural network. A modified Hopfield network was presented for image restoration. The greed algorithm with n-simultaneous updates and apartially asynchronous algorithm were combined, im- proving convergence and avoiding synchronization penalties. Mathematical morphology was widely applicated in digital image processing. The basic idea of mathematical mor- phology is to use construction element measure image morphology for solving under- stand problem. Presented advanced Cellular neural network that forms MMCNN equa- tion to be suit for mathematical morphology filter. It gave the theory of MMCNN dynamic extent and stable state. It was evidenced that arrived mathematical morphology filter through steady of dynamic precess in definite condition. The results of implementation were applied in detecting undergroug bin level.

An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction

This paper propose a computerized method of magnetic resonance imaging （MR/） of brain binarization for the uses of preprocessing of features extraction and brain ab- normality identification. One of the main problems of MR/ binarization is that many pixels of brain part cannot be cor- rectly binarized due to extensive black background or large variation in contrast between background and foreground of MR/. We have proposed a binarization that uses mean, vari- ance, standard deviation and entropy to determine a thresh- old value followed by a non-gamut enhancement which can overcome the binarization problem of brain component. The proposed binarization technique is extensively tested with a variety of MR/and generates good binarization with im- proved accuracy and reduced error. A comparison is carried out among the obtained outcome with this innovative method with respect to other well-known methods.

四川某锂辉石矿智能预选抛废工艺研究

为提高四川某锂辉石矿磨选给矿Li_(2)O品位、降低选矿生产成本,采用图像智能分选机开展了智能预选抛废工艺试验,考察了入选粒度、矿石水洗与否、抛废率、皮带速度和吹喷方式等对分选效果的影响。结果表明:在给矿粒度为60~10 mm、预先冲洗矿泥、抛废率为31.88%、皮带速度为0.5 m/s、采用“正吹”喷吹的条件下,获得了Li_(2)O品位为1.47%、Li_(2)O回收率为96.03%的锂辉石合格矿,仅3.97%的Li_(2)O损失在废石中;图像分选机分选出的锂辉石合格矿总体呈亮黄色,而废石则呈黑色或白色;XRD结果显示,相较于原矿,锂辉石合格矿的锂辉石相明显增强,而废石中未见锂辉石相。表明智能图像选矿机可有效实现锂辉石和脉石矿物的智能分选,减少磨浮作业处理量,提高入磨料Li_(2)O品位,降低生产成本,减轻浮选药剂的潜在环境危害。

基于Unit-Linking PCNN和图像熵的图像分割新方法

基于单位链接脉冲耦合神经网络(Unit-Linking PCNN)模型,在图像方差准则基础上将最大香农熵准则,最小交叉熵准则相结合,再组合图像分块策略与图像加权预处理策略,提出了不同准则和策略组合的8种图像分割方案。分析了各种准则和策略的优势和不足,比较了各种分割方案条件下的图像分割效果,部分方案的分割结果体现了图像的更多的细节。与已有文献结果比较,具有使用神经元模型参数少的特点,与PCNN模型相比,该模型参数对图像分割结果的影响较不敏感。计算机仿真结果表明,该方法具有较好的图像分割效果和实验仿真速度性能,具有较强适用性。

基于NSCT-I的红外图像收缩阈值去噪算法

分析了非下采样Contourlet变换,利用基于冗余结构的非线性提升方法实现了平移不变的不可分离小波变换,提出了一种改进的非下采样Contourlet变换理论(NSCT-I)。NSCT-I具有更好的细节捕捉能力、平移不变性、良好的多分辨率、局部化和多方向性,能够获得更好的图像处理效果。在分析现有阈值函数和阈值选择方法的基础上,构建了适合NSCT-I变换方法的阈值函数,提出了一种基于NSCT-I的收缩阈值去噪算法。分析和仿真实验表明:该算法具有良好的去噪效果和保持细节的性能。

融合结构重参数化变换的气体泄漏红外检测

针对常规工业气体泄漏检测装置需泄漏扩散到一定范围并与传感器接触时才能响应的不足,提出一种融合结构重参数化变换的红外非接触式检测网络模型GRNet。GRNet模型采用Mosaic-Gamma变换的预处理方法增加泄漏样本数量并提高图像对比度以增强模型的鲁棒性;通过K-means聚类分析出适用于气体泄漏红外检测的候选框以预置模型参数;优化定位损失函数以提高模型对泄漏区域的定位准确性;采用改进后的轻量化网络RepVGG模块重构特征提取网络增强模型的特征提取能力,以实现轻量化并提高检测精度。实验结果表明,GRNet模型对氨气泄漏的平均检测精度达到94.90%,单张图像平均检测时间达到3.40 ms。采用伪色彩映射实现泄漏浓度的视觉感知效果,采用PyQt5将GRNet模型进行封装实现气体泄漏红外检测系统界面的可视化并在Jetson Nano B01嵌入式实验平台部署该模型,验证了实际工程应用的可行性和有效性,为开发气体泄漏非接触探测装置以保障涉气企业的安全生产和稳定运行提供一种有效的检测算法。

CBERS-2CCD遥感图像辐射校正技术

在对CBERS-2卫星CCD遥感图像辐射校正技术进行深入研究的基础上,给出了图像的0级预处理、传感器标定、大气辐射校正、照度校正等技术方法。在0级图像预处理方面,提出了利用测试数据来解决拼接区的辐射失真问题,并提出一种新的条纹检测技术;传感器的标校方面,讨论了底电平和非均匀性的消除方法;大气辐射校正采用回归分析法和直方图法;给出了太阳高度和地形坡度的照度校正方法与技术。

基于小波变换和U-Net的眼底图像血管分割

眼底血管在许多疾病的检测和诊断中含有丰富的信息,因此提高眼底血管分割精度对于多种疾病的诊断和临床研究具有重要意义。本文提出了一种基于小波变换和U-Net神经网络的眼底图像血管分割算法,该算法通过数据增广与预处理、小波系数图像获取、U-Net神经网络训练与预测、阈值分割与后处理等步骤完成眼底血管的分割。采用分割准确率、特异度、灵敏度与AUC等指标,对本文算法进行了性能分析。实验结果显示,本文提出的眼底图像血管分割算法能得到满意的分割结果,在中医现代目诊中具有潜在的应用价值。

多层特征融合的超声甲状腺结节分割方法

为精确地从超声图像中分割出甲状腺结节,提出一种包含Swin Transformer和卷积神经网络两个分支的多层特征融合分割方法,利用3个单向特征桥接单元(one-way feature bridging unit,OFU)桥接多层语义特征,并下采样特征图。实验采用来自斯坦福AIMI共享数据集的超声甲状腺结节图像用于训练、验证和测试。经过实验对比,验证了该模型在用时较短的情况下,相比其它模型取得了更好的分割效果。

基于卷积神经网络的白酒上甑探汽方法

针对白酒“探汽上甑”工艺在实现自动化过程中出现的探汽准确率低的问题,提出一种基于卷积神经网络的探汽方法。通过红外热成像仪采集甑锅内酒醅表面的红外图像并做预处理,再结合上甑工艺特点将图像分类,利用卷积神经网络训练得到探汽模型。训练结果表明,AlexNet、VGGNet⁃16、GoogLeNet、ResNet⁃18、DenseNet⁃37的探汽准确率分别为0.9970、0.9980、0.9942、0.9898、0.9970,综合考虑选用DenseNet⁃37做模型评估,测试集测试的精确率为0.9970,召回率为0.9970,F1分数为0.9969,表示该模型性能表现好,故能满足探汽上甑要求。

基于智能优化图像处理的硬币金额检测

硬币检测对于银行部门大额硬币流通、自助机交易、假币鉴别等具有实用意义。针对目前光电传感器硬币检测效果不理想的问题,提出了一种基于智能优化图像处理的硬币金额检测方法。首先利用颜色特征和智能优化Otsu图像分割对五角硬币特征区域进行提取并计数,再依次利用图像预处理、边缘检测、形态学处理和Hough圆检测等环节检测各硬币的半径信息,通过对半径进行比较可识别出硬币的种类及面额,最后利用App Designer设计了硬币自动识别系统。实验结果表明,该方法具有效果好、速度快、易于实现等优点,能够满足工程化需要。