Improved Weighted Local Contrast Method for Infrared Small Target Detection

In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).

A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.

A pixel-level local contrast measure for infrared small target detection

Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure(PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously.With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker(RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.

Derivation of Optimal Global Equalization Function with Variable Size Block Based Local Contrast Enhancement

A conventional global contrast enhancement is difficult to apply in various images because image quality and contrast enhancement are dependent on image characteristics largely. And a local contrast enhancement not only causes a washed-out effect, but also blocks. To solve these drawbacks, this paper derives an optimal global equalization function with variable size block based local contrast enhancement. The optimal equalization function makes it possible to get a good quality image through the global contrast enhancement. The variable size block segmentation is firstly exeoated using intensity differences as a measure of similarity. In the second step, the optimal global equalization function is obtained from the enhanced contrast image having variable size blocks. Conformed experiments have showed that the proposed algorithm produces a visually comfortable result image.

Learning Local Contrast for Crisp Edge Detection

In recent years, the accuracy of edge detection on several benchmarks has been significantly improved by deep learning based methods. However, the prediction of deep neural networks is usually blurry and needs further post-processing including non-maximum suppression and morphological thinning. In this paper, we demonstrate that the blurry effect arises from the binary cross-entropy loss, and crisp edges could be obtained directly from deep convolutional neural networks. We propose to learn edge maps as the representation of local contrast with a novel local contrast loss. The local contrast is optimized in a stochastic way to focus on specific edge directions. Experiments show that the edge detection network trained with local contrast loss achieves a high accuracy comparable to previous methods and dramatically improves the crispness. We also present several applications of the crisp edges, including image completion, image retrieval, sketch generation, and video stylization.

A camouflage target detection method based on local minimum difference constraints

To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(LMDC).The algorithm first uses the mean to optimize the initial background model,removes the stable background region by global comparison,and extracts the edge point set in the potential target region so that each boundary point(seed)grows along the center of the target.Finally,we define the minor difference constraints term,combine the seed path and the target space consistency,and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection.The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms.The experimental results show that the proposed algorithm yields good results based on integrity,accuracy,and a number of objective evaluation indexes,and its overall performance is better than that of the compared algorithms.

NUMERICAL SIMULATION EXPERIMENTS OF THE IMPACTS OF LOCAL LAND-SEA THERMODYNAMIC CONTRASTS ON THE SCS SUMMER MONSOON ONSET

The important effects of local land-sea thermodynamic contrast between the South China Sea (SCS) and Indochina Peninsula on SCS summer monsoon onset are preliminarily studied by using two sets of SSTA tests and two ideal tests in s-p regional climate model. The result shows that warm SST in the SCS in winter and spring is favorable for the formation of monsoon circulation throughout all levels of the atmosphere over the sea, which hastens the onset of SCS summer monsoon. The effects of cold SST are generally the opposite. The local land-sea contrast in the SCS is one of the possible reasons for SCS summer monsoon onset. Superposed upon large-scale land-sea thermodynamic differences, it facilitates the formation of out-breaking onset characteristics of SCS summer monsoon in the SCS area.

Improved Image Contrast Enhancement Based on Local Standard Deviation and Compared with Other Algorithms

An adaptive contrast enhancement (ACE) algorithm is presented in this paper, in which the contrast gain is determined by mapping the local standard deviation (LSD) histogram of an image to a Gaussian distribution function. The contrast gain is nonlinearly adjusted to avoid noise overenhancement and ringing artifacts while improving the detail contrast with less computational burden. The effectiveness of our method is demonstrated with radiological images and compared with other algorithms.

基于FACET滤波加权局部对比度的红外小目标检测

针对低空复杂背景下红外图像中目标与背景对比度低、边缘高亮的问题,提出一种基于FACET滤波加权局部对比度的红外小目标检测算法。首先,通过FACET滤波运算获取目标候选像素;然后,利用目标区域和背景区域灰度差异的比率计算局部对比度,同时基于目标和背景的异质性设计了一种加权函数,用来增强目标显著性和抑制背景;最后,通过自适应阈值分割提取真实的目标。在5组真实红外小目标图像数据集上与不同的算法进行性能比较分析,实验结果表明,提出的算法在不同低空复杂场景中具有较好的检测性能。

超声造影预测局部晚期宫颈癌同步放化疗早期疗效的临床价值

目的 探讨超声造影(CEUS)预测局部晚期宫颈癌(LACC)患者同步放化疗(CCRT)早期疗效的临床价值。方法 选取临沂市肿瘤医院收治的LACC患者65例,于治疗前1周内及体外放疗结束后分别进行CEUS检查,通过定量分析时间-强度曲线(TIC)获得达峰时间(TP)、梯度(Grad)、曲线下面积(Area)及峰值强度(PI);测量治疗前后瘤体最大径,计算肿瘤消退率。体外放疗结束后根据治疗效果分为敏感组53例和不敏感组12例,比较两组上述参数的差异。采用Spearman相关分析法分析CEUS参数与肿瘤消退率的相关性;二元Logistic回归分析筛选预测LACC患者CCRT早期疗效的影响因素;绘制受试者工作特征(ROC)曲线分析其预测效能。结果 65例LACC患者治疗前和体外放疗结束后瘤体最大径分别为(60.7±14.6)mm、(31.2±16.9)mm,肿瘤消退率为(49.2±23.2)%。敏感组治疗前Grad、PI均低于不敏感组,差异均有统计学意义(均P<0.05);敏感组体外放疗结束后Grad、Area及PI均低于不敏感组,TP高于不敏感组,差异均有统计学意义(均P<0.001)。敏感组体外放疗结束后Grad、Area及PI均较治疗前降低,TP较治疗前升高,差异均有统计学意义(均P<0.05)。相关性分析显示,治疗前Grad、PI与肿瘤消退率均呈负相关(r=-0.602、-0.499,均P<0.05);体外放疗结束后Grad、PI与肿瘤消退率均呈负相关(r=-0.859、-0.913,均P<0.05)。二元Logistic回归分析显示,治疗前Grad、PI均为预测LACC患者CCRT早期疗效的独立影响因素(均P<0.05)。ROC曲线分析显示,治疗前Grad、PI预测LACC患者CCRT早期疗效的曲线下面积分别为0.802、0.894。结论 CEUS在预测LACC患者CCRT早期疗效中有一定的临床价值。

基于改进顶帽变换的红外弱小目标检测

天空背景下的红外弱小目标检测技术较为成熟,但在近地复杂背景下,红外弱小目标的检测存在准确率不高、虚警目标多、实时性差的问题。针对以上问题,该文提出一种基于改进顶帽变换的红外弱小目标检测算法(OTHOLCM)。该算法采用基于改进顶帽变换的图像预处理算法(OTH),通过对不同灰度值的图像采取不同的策略针对性地处理图像,达到目标增强、背景抑制的效果。并在此基础上,采用基于改进多尺度局部对比度的红外弱小目标检测算法(OLCM),通过针对目标尺寸特点进行尺度设计,使得在保证算法实时性的基础上扩大目标尺寸检测范围。实验证明:OTHOLCM算法可以保证实时性并明显提高目标检测准确率、减少虚警目标数量。与3层模板局部差异度量算法(TTLDM)、基于边角感知的时空张量模型(ECASTT)等先进算法相比,OTHOLCM算法可使真阳性率分别提高近79%,61%,假阳性率分别降低近77%,73%,目标检测速度达到每秒25帧。

融合片段对比学习的弱监督动作定位方法

针对现有基于注意力机制的弱监督动作定位方法对动作边界处的片段容易错误分类的问题,提出一种融合片段对比学习的弱监督动作定位方法。首先,引入三个分支的注意力机制,分别测量每个视频帧是动作实例、上下文以及背景的可能性;其次,基于得到的注意力值构建对应分支的类激活序列;然后,通过片段挖掘算法构造正负样本对;最后,利用片段对比学习引导网络将模糊片段正确归类。实验结果表明,当交并比(IoU)取值0.5时,在THUMOS14与ActivityNet1.3两个公共数据集上,所提方法的平均检测精度(mAP)分别达到了33.9%和40.1%,相较于DGCNN(Dynamic Graph modeling for weakly-supervised temporal action localization Convolutional Neural Network)弱监督动作定位模型在上述两个数据集上分别提升1.1和2.9个百分点,验证了所提方法的有效性。

基于动态阈值和差异性检验的自训练算法

针对自训练算法在迭代训练分类器的过程中存在难以有效选取高置信度样本以及误标记样本错误累积的问题,本文提出了基于动态阈值和差异性检验的自训练算法。引入样本的局部离群因子,据此剔除有标签样本中的离群点以及分类标注无标签样本,依据标注分批次处理无标签样本,以使模型更易选取到高置信度的无标签样本;根据新增伪标签样本的数量和对比隶属度的变化,设计一种动态隶属度阈值函数,提升高置信度样本的质量;定义密集距离度量样本间的差异性,分别计算伪标签样本与同类和不同类样本之间的密集距离之和,从而找出不确定度高的伪标签样本,并将此类样本并入下轮训练的无标签样本集中,缓解误标记样本错误累积的问题。实验结果表明,该算法在12个UCI基准数据集上均取得理想效果。

基于动态辅助对比学习的跨域行人重识别

具有混合记忆的自步对比学习(Self-paced Contrastive Learning,SpCL)通过集群聚类生成不同级别的伪标签来训练网络,取得了较好的识别效果,然而该方法从源域和目标域中捕获的行人数据之间存在典型的分布差异,使得训练出的网络不能准确区别目标域和源域数据域特征。针对此问题,提出了双分支动态辅助对比学习(Dynamic Auxiliary Contrastive Learning,DACL)框架。该方法首先通过动态减小源域和目标域之间的局部最大平均差异(Local Maximum Mean Discrepancy,LMMD),以有效地学习目标域的域不变特征;其次,引入广义均值(Generalized Mean,GeM)池化策略,在特征提取后再进行特征聚合,使提出的网络能够自适应地聚合图像的重要特征;最后,在3个经典行人重识别数据集上进行了仿真实验,提出的DACL与性能次之的无监督域自适应行人重识别方法相比,mAP和rank-1在Market1501数据集上分别增加了6.0个百分点和2.2个百分点,在MSMT17数据集上分别增加了2.8个百分点和3.6个百分点,在Duke数据集上分别增加了1.7个百分点和2.1个百分点。

基于快速局部对比度和目标特征的星图弱小目标检测算法

为了解决局部对比度方法在用于星图空间目标检测时存在运算量大和去除背景噪声困难的问题,提出了一种基于快速局部对比度和目标特征的方法来检测目标。在对比度计算前、对比度计算中和对比度计算后3个环节,分别提高了算法实时性、对复杂背景的抑制和去除噪声。首先,通过中值滤波去除高频噪声;然后,通过快速局部极大值滤波确定目标区域,通过局部对比度计算抑制背景,突出目标成像特征;最后,根据目标成像特征,设置目标能量分布、目标能量集中和目标能量传递3个特征函数,通过设置特征阈值去除噪声,提取真实目标。实验结果表明,本文所提方法在检测率和时间消耗上均具有优越性,对于信噪比为1.5的目标有95%的检测率,平均耗时仅为某些对比方法的1/30~1/6。本文所提方法更适用于星图复杂背景条件下的目标快速检测,满足星图空间目标检测算法鲁棒性强、实时性高的要求。

面向跨模态检索的查询感知双重对比学习网络

近期,跨模态视频语料库时刻检索(VCMR)这一新任务被提出,它的目标是从未分段的视频语料库中检索出与查询语句相对应的一小段视频片段.现有的跨模态视频文本检索工作的关键点在于不同模态特征的对齐和融合,然而,简单地执行跨模态对齐和融合不能确保来自相同模态且语义相似的数据在联合特征空间下保持接近,也未考虑查询语句的语义.为了解决上述问题,提出一种面向多模态视频片段检索的查询感知跨模态双重对比学习网络(QACLN),该网络通过结合模态间和模态内的双重对比学习来获取不同模态数据的统一语义表示.具体地,提出一种查询感知的跨模态语义融合策略,根据感知到的查询语义自适应地融合视频的视觉模态特征和字幕模态特征等多模态特征,获得视频的查询感知多模态联合表示.此外,提出一种面向视频和查询语句的模态间及模态内双重对比学习机制,以增强不同模态的语义对齐和融合,从而提高不同模态数据表示的可分辨性和语义一致性.最后,采用一维卷积边界回归和跨模态语义相似度计算来完成时刻定位和视频检索.大量实验验证表明,所提出的QACLN优于基准方法.

基于Retinex里双分量改进的微光图像增强方法

近年来,微光图像增强技术备受关注,但仍存在一些问题。例如,有时暗区域没有完全改善,有时光源或光源附近的明亮区域曝光过度。针对以上问题,提出了一种基于Retinex模型里照度分量和反射分量双增强的图像增强方法。该方法首先将原始图像由RGB空间转换到HSV空间,并提取其中的V分量进行后续处理;然后对V分量进行引导滤波获得图像照度分量,根据Retinex理论通过分解得到图像的反射分量;接着对照度分量进行全局自适应亮度增强,对反射分量进行多尺度细节加强;再将增强后的照度分量和反射分量按照Retinex模型重构得到V分量重构图,并经过非线性变换处理和局部对比度增强处理;最终转换回RGB空间获得最终增强图。实验结果表明,该方法的峰值信噪比(peak signal-to-noise ratio,PSNR)和结构相似指数(structural similarity index measure,SSIM)评价值分别为17.741和0.765,具有更好的图像质量,较其他方法表现出更好的增强效果。

基于结构张量的Non-Local Means去噪算法研究

非局部平均是当前一种新兴而有效的图像去噪方法。为了能充分利用数字图像局部几何结构的自相似性,同时由于结构张量可有效刻画数字图像的局部几何结构特征,进而提出了基于结构张量相似性度量的非局部平均去噪算法。实验结果验证了该算法抑制噪声的有效性,同时能很好地保持边缘等细节特征,峰值信噪比得到有效提高。

线性变换与局部均衡融合的红外图像增强

为了改善红外图像的效果,提升对比度和清晰度,丰富边缘细节信息,提出了融合线性变换和局部均衡的红外图像增强方法。利用图像的像素值分布,对像素值进行自适应的分段线性变换,并用局部的直方图均衡增强图像;分别计算两张增强图像的权重图。对比度权重、显著性权重和亮度分布权重;以拉普拉斯金字塔和高斯金字塔的方式,分别对增强图像和权重图进行分解,将分解的图像与权重图进行多尺度线性融合,获得效果理想的增强图像。结果表明,相对于现有方法,本文中提出的方法增强图像的视觉效果更清晰,信息熵、平均梯度和变异系数分别比现有方法高出9.03%、23.87%和9.97%以上。该研究可更有效地提高红外图像增强的性能。

基于视觉感知的低对比度图像细节增强方法

由于传统图像细节增强方法存在噪声含量大及失真等问题,为了提升低对比度图像的视觉效果,提出基于视觉感知的低对比度图像细节增强方法。根据人眼视觉感知的亮度掩蔽特性划分图像区域,并设计暗适应函数与明适应函数,分别延长低对比度图像灰度值较低、较高区域间隔,实现基于视觉感知的低对比度图像全局增强;以全局增强结果为基础,引入加权自适应局部低对比度增强法,增强低对比度图像的细节,获取最佳低对比度图像细节增强结果。实验结果表明:该方法增强后的低对比度图像保留了图像的边缘及细节,图像更加明亮清晰,显著提升了其视觉效果,同时低对比度图像具备噪声含量小、失真程度小、图像质量高的优势。