Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences

In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.

Image Fusion Using Wavelet Transformation and XGboost Algorithm

Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same item.Image fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual perception.Image fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on application.The research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet transform.Image fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient manner.More precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain characteristics.To tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed.The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice.The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images.This study enhances the performance of the eXtreme Gradient Boosting(XGBoost)algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection.The performance of images is improved by segmenting them employing the K-Means algorithm.The segmentation method aids in identifying specific regions of interest,using Particle Swarm Optimization(PCA)for trait selection and XGBoost for data classification.Extensive trials confirm the model’s exceptional visual performance,achieving an accuracy of up to 97.067%and providing good objective indicators.

A Novel Fast Algorithm of Mono Transition Region Determination with Gray Image

There is a transition region between objects and background in any gray image. Many valuable applications of image segmentation and edge detection based on transition region determination have been developed in recent years. But, the complexity of calculation for determining transition region is too high. It results in the very limitation of applications based on transition region. A new novel fast method for transition region determination is presented in this paper, which will reduce the complexity of calculation dramatically. Many experiments have showed that this algorithm is effective and correct and will lay a good foundation for applications based on transition region. Key words image segmentation - transition region - maximum point - efficient average gradient (EAG) CLC number TP 391.4 Biography: Zhang Ai-hua (1965-), male, Ph. D candidate, research direction: image processing.

MEASURING THE IMPURITIES AND NEPS ON COTTON GRAY GOODS BY IMAGE ANALYSIS

In this paper, a test system was developed in which a CCD camera was used as a sensor together with an IBM-AT-compatible computer with an Intel 80486 processor to measure the impurities and neps on a piece of cotton gray goods, and a method was proposed by which the differences of degree of gray between samples and interference caused by surface unevenness, creasing and the like within a test sample can be removed effectively. The whole test system is reliable, accurate and causing fewer subjective errors.

A novel algorithm for ocean wave direction inversion from X-band radar images based on optical flow method

As one of the important sea state parameters for navigation safety and coastal resource management, the ocean wave direction represents the propagation direction of the wave. A novel algorithm based on an optical flow method is developed for the ocean wave direction inversion of the ocean wave fields imaged by the X-band radar continuously. The proposed algorithm utilizes the echo images received by the X-band wave monitoring radar to estimate the optical flow motion, and then the actual wave propagation direction can be obtained by taking a weighted average of the motion vector for each pixel. Compared with the traditional ocean wave direction inversion method based on frequency-domain, the novel algorithm is fully using a time-domain signal processing method without determination of a current velocity and a modulation transfer function（MTF）. In the meantime,the novel algorithm is simple, efficient and there is no need to do something more complicated here. Compared with traditional ocean wave direction inversion method, the ocean wave direction of derived by using this proposed method matches well with that measured by an in situ buoy nearby and the simulation data. These promising results demonstrate the efficiency and accuracy of the algorithm proposed in the paper.

A LUMINANCE EDGE EXTRACTION METHOD USING GRADIENT OF GRAY LEVEL FOR SYNTHETIC APERTURE RADAR IMAGERY

In the multistage imaging processing for SAR digital imaging and applications ofSAR imagery,extraction of luminance edge for the SAR imageis often required.It is well studiedto extract the luminance edge for ordinary images,The methods using gradient are effective andcommonly used.Because of the serious noise of coherent speckle exists in SAR images,somepeople believe that edge extraction by using gradient for SAR imagery gives poor results.Inthis paper,we have derived a rather ideal method for the extraction of luminance edge for SARimagery with the consideration of the characteristics of SAR imagery.This method uses therelative average gradient and combines detection with tracking.

GLOBAL MEASURE ON IMAGE CONTENT

This paper investigated approaches to supporting effective and efficient retrieval of image based on principle component analysis. First, it extracted the image content, texture and color. Gabor wavelet transforms were used to extract texture feature of the image and the average color was used to extract the color features. The principle component of the feature vector of image can be constructed. Content based image retrieval was performed by comparing the feature vector of the query image with the projection feature vector of the image database on the principle component space of the query image. By this technique, it can reduce the dimensionality of feature vector, which in turn reduce the searching time.

Comparison between Different ESI Methods on Refractory Epilepsy Patients Shows a High Sensitivity for Bayesian Model Averaging

Electrical Source Imaging (ESI) is a non-invasive technique of reconstructing brain activities using EEG data. This technique has been applied to evaluate epilepsy patients being evaluated for epilepsy surgery, showing encouraging results for mapping interictal epileptiform discharges (IED). However, ESI is underused in planning epilepsy surgery. This is basically due to the wide availability of methods for solving the electromagnetism inverse problem (e-IP) associated to few studies using EEG setups similar to those most commonly used in clinical setting. In this study, we applied six different methods of solving the e-IP based on IEDs of 20 focal epilepsy patients that presented abnormalities in their MRI. We compared the ESI maps obtained by each method with the location of the abnormality, calculating the Euclidian distances from the center of the lesion to the closest border of the method solution (CL-BM) and also to the solution’s maxima (CL-MM). We also applied a score system in order to allow us to evaluate the sensitivity of each method for temporal and extra temporal patients. In our patients, the Bayesian Model Averaging method had a sensitivity of 86% and the shortest CL-MM. This method also had more restricted solutions that were more representative of epileptogenic activities than those obtained by the other methods.

Statistical Tests of Hypothesis Based Color Image Retrieval

This paper proposes a novel method based on statistical tests of hypotheses, such as F-ratio and Welch’s t-tests. The input query image is examined whether it is a textured or structured. If it is structured, the shapes are segregated into various regions according to its nature;otherwise, it is treated as textured image and considered the entire image as it is for the experiment. The aforesaid tests are applied regions-wise. First, the F-ratio test is applied, if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. means of the two images. If the images pass both tests, then it is concluded that the two images are the same or similar. Otherwise, they differ. Since the proposed system is distribution-based, it is invariant for rotation and scaling. Also, the system facilitates the user to fix the number of images to be retrieved, because the user can fix the level of significance according to their requirements. These are the main advantages of the proposed system.

A Semi-Vectorial Morphological Segmentation Multi-Component Images of Coumarins on Thin Layer Combined with Laser for Better Separation

In this work, we propose an approach for the separation of coumarins from thin-layer morphological segmentation based on the acquisition of multicomponent images integrating different types of coumarins. The first step is to make a segmentation by region, by thresholding, by contour, etc. of each component of the digital image. Then, we proceeded to the calculations of parameters of the regions such as the color standard deviation, the color entropy, the average color of the pixels, the eccentricity from an algorithm on the matlab software. The mean color values atR = 91.20 in red, atB = 213.21 in blue showed the presence of samidin in the extract. The color entropy values HG = 5.25 in green and HB = 4.04 in blue also show the presence of visnadine in the leaves of Desmodium adscendens. These values are used to consolidate the database of separation and discrimination of the types of coumarins. The relevance of our coumarin separation or coumarin recognition method has been highlighted compared to other methods, such as the one based on the calculation of frontal ratios which cannot discriminate between two coumarins having the same frontal ratio. The robustness of our method is proven with respect to the separation and identification of some coumarins, in particular samidin and anglicine.

Feasibility of 3.0 T diffusion-weighted nuclear magnetic resonance imaging in the evaluation of functional recovery of rats with complete spinal cord injury

Diffusion tensor imaging is a sensitive way to reflect axonal necrosis and degeneration, glial cell regeneration and demyelination following spinal cord injury, and to display microstructure changes in the spinal cord in vivo. Diffusion tensor imaging technology is a sensitive method to diagnose spinal cord injury; fiber tractography visualizes the white matter fibers, and directly displays the structural integrity and resultant damage of the fiber bundle. At present, diffusion tensor imaging is restricted to brain examinations, and is rarely applied in the evaluation of spinal cord injury. This study aimed to explore the fractional anisotropy and apparent diffusion coefficient of diffusion tensor magnetic resonance imaging and the feasibility of diffusion tensor tractography in the evaluation of complete spinal cord injury in rats. The results showed that the average combined scores were obviously decreased after spinal cord transection in rats, and then began to increase over time. The fractional anisotropy scores after spinal cord transection in rats were significantly lower than those in normal rats （P 〈 0.05）; the apparent diffusion coefficient was significantly increased compared with the normal group （P 〈 0.05）. Following spinal cord transection, fractional anisotropy scores were negatively correlated with apparent diffusion coefficient values （r = -0.856, P 〈 0.01）, and positively correlated with the average combined scores （r = 0.943, P 〈 0.01）, while apparent diffusion coefficient values had a negative correlation with the average combined scores （r = -0.949, P 〈 0.01）. Experimental findings suggest that, as a non-invasive examination, diffusion tensor magnetic resonance imaging can provide qualita- tive and quantitative information about spinal cord injury. The fractional anisotropy score and apparent diffusion coefficient have a good correlation with the average combined scores, which reflect functional recovery after spinal cord injury.

Application of image alignment and time averaging methods in AFM detection for single DNA molecules

Enhancement of the SNR (signal to noise ratio) in single-molecule imaging is significantly important for improving image resolu-tion and distinguishing the fine structures of single molecules at a higher precision level.Image processing techniques have dem-onstrated the remarkable capability to improve the SNR and the resolution level by breaking through some inherent limitations unresolved by instrument hardware optimization.In this paper, we focus on single-biomolecule imaging using atomic force mi-croscopy (AFM), a unique method in separated single-biomolecule imaging, and there was few suitable image processing tech-niques reported to increase the SNR of a single molecule’s AFM image.With the typical samples of separately dispersed DNA molecules, we replaced the classified averaging method, which is usually used when the molecules’ structure can be easily and repeatedly prepared, with the time averaging method to improve the SNR in a single molecule’s AFM imaging.Combining the time averaging technique with the image alignment method for the series of time-lapse AFM images of a single DNA molecule, we were able to improve the image’s SNR and recover some buried signals from the background noises.Furthermore, the fine structures of single molecules can potentially be further resolved if other image processing techniques are applied collaboratively with some newly developed imaging methods like Vibrating Mode Scanning Polarization Force Microscopy (VSPFM), and such combination will also be advantageous for the manipulation of single-biomolecules by AFM.In addition, the proposed algorithms for the estimations of drift, distortion and SNR are applicable for quantitative status characterization of AFM imaging.

The Properties of Expected Scattering and Averaged Scattering and Their Applications to Texture Classification

In order to further improve the effectiveness of image processing,it is necessary that an efficient invariant representation is stable to deformation applied to images.This motivates the study of image representations defining an Euclidean metric stable to these deformation.This paper mainly focuses on two aspects.On the one hand,in this paper,two properties of expected scattering and averaged scattering,i.e.,Lipschitz continuity and translation invariance,are proved in detail.These properties support that excepted scattering and averaged scattering are invariant,stable and informative representations.On the other hand,the issue of texture classification based on expected scattering and averaged scattering has been analyzed respectively in this study.Energy features,which are based on expected scattering and averaged scattering,are calculated and used for classification.Experimental results show that starting with the seventh feature,the two approaches can achieve good performance in texture image classification.

经阴道彩色多普勒超声在子宫内膜病变患者中的应用价值

目的分析经阴道彩色多普勒超声在子宫内膜病变患者中的应用价值。方法选取2021年2月至2022年4月南昌大学第四附属医院收治的88例子宫内膜病变患者作为研究对象,所有患者均接受经阴道彩色多普勒超声及病理学检查,按照病理学检查结果将患者分为息肉组与癌变组,每组44例。比较两组二维超声子宫内膜情况、病灶血流动力学情况、经阴道彩色多普勒超声血流相关参数;以病理学检查结果为金标准,分析经阴道彩色多普勒超声对子宫内膜癌变的诊断效能。结果癌变组子宫内膜厚度厚于息肉组,内膜-肌层分界清晰、病灶回声均匀占比均低于息肉组,差异有统计学意义(P<0.05);癌变组血流丰富占比高于息肉组,差异有统计学意义(P<0.05);癌变组血流阻力指数(RI)、搏动指数(PI)均低于息肉组,时间平均峰值流速(TAP)慢于息肉组,差异有统计学意义(P<0.05);经阴道彩色多普勒超声对子宫内膜癌变的诊断灵敏度为86.36%,特异度为84.09%,准确度为85.23%;诊断息肉的灵敏度为84.09%,特异度为86.36%,准确度为85.23%。结论经阴道彩色多普勒超声对子宫内膜病变的诊断准确率较高,能通过血流动力学、RI、PI、TAP及影像学表现鉴别息肉或癌变。

基于几何矩特征与纹理度量的图像匹配算法

为提高多种几何内容变化下的特征点匹配准确度,引入双边滤波器,设计了几何矩特征耦合纹理度量的图像匹配算法。引入双边滤波器,对图像进行预处理,以去除图像中噪声,接着利用Hessian算子,准确检测图像特征。利用图像灰度信息,计算图像特征圆域内的几何矩,以形成鲁棒性较强的特征向量。通过平均梯度模型,对特征点邻域的纹理特征进行度量,并将其联合欧氏测量结果,对图像特征实施匹配。借助互相关模型,对匹配特征的相关程度进行测量,以检测特征点的匹配正确性,优化特征匹配结果。实验数据表明:较当前特征匹配方法而言,在多种几何内容变化下,所提技术具有更高的匹配准确度与鲁棒性。

一种改进的暗通道先验低光照图像增强算法

针对低光照图像增强算法常见的亮度不均匀、色彩失真、图像噪点较多、细节不清晰等问题,提出了一种改进的暗通道先验低光照图像增强算法。该方法对像素值取反后的低光照图像,首先采用引导滤波,解决图像在运用最小值滤波计算暗通道时引起的块效应,其次在剔除像素为255的纯白色点干扰后进行大气光值的计算,然后引入细化系数进行透射率自适应修正使透射率更加平滑,最后采用非局部平均滤波进行噪声去除。实验表明,所提出的算法使图像的亮度增强合适,细节清晰,在Low-Light弱光图像数据集上测试图片,所得到的SSIM值比对比算法提升20.5%,PSNR值提升19.9%,无论从主观感受,还是客观评价指标等各方面,都有优化。

湍流图像退化中的模糊与畸变

基于光线传播以及相位叠加原理,构建了湍流图像退化模型,获得了湍流下目标对应的点扩散函数,并通过调制传递函数面积以及等晕角验证了仿真模型。同时,通过平均相关系数定义了像方近似不变区域,像方近似不变区域有助于对点扩散函数变化程度的理解。通过光线在湍流中的传播过程,猜测图像退化的模糊与畸变的程度和湍流的分布有关。为验证以上猜测,设置了两种极端情况下的湍流分布情形进行仿真实验,并通过调制传递函数面积以及像方近似不变区域对仿真结果进行分析。结果表明在同等湍流强度情况下,靠近镜头端的湍流会对图像造成更多的模糊效应,靠近物体端的湍流会对图像造成更多的畸变效应。基于此,解释了在湍流图像退化模型中采用先畸变后模糊是更为合理的操作,并建议湍流复原算法应根据退化结果中的模糊与畸变的程度采取相应的复原措施。

沱沱河辫状河道多尺度形态特征及非活跃汊道的影响

目前对长江源辫状河流的形态特征已有初步认识,但缺乏河段多尺度形态特征与非活跃汊道的影响研究。基于GEE和PyRIS,使用Landsat 8、Sentinel-2遥感影像、STRM V4地形数据和沱沱河站2013-2018年逐日流量数据,分析沱沱河多尺度的辫状平面形态特征及非活跃汊道的影响。结果表明:全河段平面形态呈宽窄相间的藕节状,且汊道随流量增大呈先增多后增宽的阶段性变化特征。子河段辫状强度与活动水域面积比呈先增后减的抛物线型变化规律,与水面展宽呈正幂律关系。非活跃汊道仍具有辫状河道的辫状强度特征,其地形较高,缺少漫滩机会,内部非活跃汊道形态稳定,年内摆幅小于10 m。

融合注意力的教师互一致性半监督医学图像分割

医学图像分割在疾病辅助诊断中起着关键的作用。现有的深度分割模型需要依赖带有标注的数据完成大规模训练,而医学影像标注需要具有专业背景的临床医生进行像素级标注,导致标注数据获取困难。基于半监督的医学图像分割方法利用少量的标注数据和大量的未标注数据进行学习,可以在一定程度上缓解标注数据获取困难的问题。针对半监督分割模型不能充分利用未标注数据中的可学习信息的问题,提出一种半监督分割模型TCA-Net。该模型使用U-Net作为骨干网络,通过在U-Net中引入卷积块注意力模块(CBAM)与多头自注意力模块(MHA)来解决其在下采样过程中的信息丢失问题;为了充分利用未标注数据中的不确定性信息,构建一个教师互一致性模型,该模型由具有1个编码器和3个略有不同的解码器的学生模型与教师模型组成,通过在学生模型的概率映射与教师模型的伪标签之间添加一致性约束,以此在训练过程中最小化输出之间的差异,从而提升模型的分割效果。在公开的WORD腹部多器官数据集与ACDC心脏数据集上进行实验,结果表明,在使用20%标注数据的WORD数据集上,TCA-Net的Dice系数、Jaccard指数、HD95和ASD分别达到90.81%、83.79%、21.38和6.08,在ACDC数据集上分别达到89.69%、81.94%、1.66和0.45。消融实验与对比实验结果表明,TCA-Net能够有效提升未标注数据的利用率,在不同数据集上均达到了较好的分割效果,验证了模型的鲁棒性。