一种基于Multi-focus+context的鱼眼显示技术

针对Focus+context中单焦点方式在设备屏幕上只能显示一个焦点的内容细节,而不能对多处关注细节同时显示的局限性,提出一种基于Multi-focus+context技术的,根据关联内容自适应的,显示全局图形及多个焦点内容的细节的方法。焦点区域可以是任意形状的几何图形,并利用鱼眼功能将这些图形区域放大,非焦点区域大小不变,从而保持屏幕中整个图形的尺寸不变。最后,将这种方法用在移动设备显示地铁地图方面。结果表明,在看清用户关注的多个换乘站细节信息的同时,也能了解到全局的拓扑。

A New Method of Multi-Focus Image Fusion Using Laplacian Operator and Region Optimization

Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus image fusion extracts the focused information from the source images to construct a global in-focus image which includes more information than any of the source images. In this paper, a novel multi-focus image fusion based on Laplacian operator and region optimization is proposed. The evaluation of image saliency based on Laplacian operator can easily distinguish the focus region and out of focus region. And the decision map obtained by Laplacian operator processing has less the residual information than other methods. For getting precise decision map, focus area and edge optimization based on regional connectivity and edge detection have been taken. Finally, the original images are fused through the decision map. Experimental results indicate that the proposed algorithm outperforms the other series of algorithms in terms of both subjective and objective evaluations.

Efficient Compressive Multi-Focus Image Fusion

Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.

Joint Multi-Focus Fusion and Bayer ImageRestoration

In this paper, a joint multifocus image fusion and Bayer pattern image restoration algorithm for raw images of single-sensor colorimaging devices is proposed. Different from traditional fusion schemes, the raw Bayer pattern images are fused before colorrestoration. Therefore, the Bayer image restoration operation is only performed one time. Thus, the proposed algorithm is moreefficient than traditional fusion schemes. In detail, a clarity measurement of Bayer pattern image is defined for raw Bayer patternimages, and the fusion operator is performed on superpixels which provide powerful grouping cues of local image feature. Theraw images are merged with refined weight map to get the fused Bayer pattern image, which is restored by the demosaicingalgorithm to get the full resolution color image. Experimental results demonstrate that the proposed algorithm can obtain betterfused results with more natural appearance and fewer artifacts than the traditional algorithms.

Multi-focus non-periodic scanning method for femtosecond lasers based on DMD and galvanometer scanners[Invited]

Multi-focus parallel scanning can effectively increase laser fabrication throughput.However,the conventional approach of using a spatial light modulator(SLM)to generate multi-foci and scan this fixed number of foci with galvanometer scanners can only achieve a periodic scanning trajectory due to the low switching speed of the SLM.Here we demonstrate a multifocus non-periodic scanning method for femtosecond lasers by using,instead,a fast-switching digital micromirror device(DMD)to generate a dynamic number of foci.The number of effective foci is quickly switched by introducing aberration to the undesired focus.In this way,the intensity allocated to each focus will not be affected by the number of foci,and a uniformity of 98%with different numbers of foci is achieved without adjusting the total laser energy.Finally,we validate the effectiveness of this scanning method by demonstrating corneal flap fabrication of porcine cornea in vitro.

Multi-focus optical fiber lens based on all-dielectric metasurface

A multi-focus optical fiber lens is numerically demonstrated based on an all-dielectric metasurface structure.The metasurface consists of an array of rectangular silicon resonators with varying widths in order to obtain the required phase distribution.The core diameter of the multimode fiber is large enough to contain sufficient resonance units.The spatial distribution of the dielectric resonators is dictated by spatial multiplexing,including interleaving meta-atoms and lens aperture division,to achieve multi-focus properties.The proposed optical fiber metalens can produce two or three focal points along the longitudinal direction with high focusing efficiency.The size of every focal point is close to the diffraction limit,and the relative intensity on each focus can be controlled by adjusting the number of the respective resonators.The proposed optical fiber lens will have a great potential in the fields of integrated optics and multifunctional micro/nano devices.

Multi-focus image fusion based on fully convolutional networks

We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection(FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.

Multi-focus image fusion based on fractional-orderderivative and intuitionistic fuzzy sets

Multi-focus image fusion is an increasingly important component in image fusion,and it plays a key role in imaging.In this paper,we put forward a novel multi-focus image fusion method which employs fractional-order derivative and intuitionistic fuzzy sets.The original image is decomposed into a base layer and a detail layer.Furthermore,a new fractional-order spatial frequency is built to reflect the clarity of the image.The fractional-order spatial frequency is used as a rule for detail layers fusion,and intuitionistic fuzzy sets are introduced to fuse base layers.Experimental results demonstrate that the proposed fusion method outperforms the state-of-the-art methods for multi-focus image fusion.

High quality multi-focus polychromatic composite image fusion algorithm based on filtering in frequency domain and synthesis in space domain

A novel multi-focus polychromatic image fusion algorithm based on filtering in the frequency domain using fast Fourier transform(FFT) and synthesis in the space domain(FFDSSD) is presented in this paper.First,the original multi-focus images are transformed into their frequency data by FFT for easy and accurate clarity determination.Then a Gaussian low-pass filter is used to filter the high frequency information corresponding to the image saliencies.After an inverse FFT,the filtered images are obtained.The deviation between the filtered images and the original ones,representing the clarity of the image,is used to select the pixels from the multi-focus images to reconstruct a completely focused image.These operations in space domain preserve the original information as much as possible and are relatively insensitive to misregistration scenarios with respect to transform domain methods.The polychromatic noise is well considered and successfully avoided while the information in different chromatic channels is preserved.A natural,nice-looking fused microscopic image for human visual evaluations is obtained in a dedicated experiment.The experimental results indicate that the proposed algorithm has a good performance in objective quality metrics and runtime efficiency.

Multi-focus image fusion with the all convolutional neural network

A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network(CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN(ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.

多向聚焦波作用下透水潜堤消波特性数值模拟研究

本文采用非静压模型NHWAVE建立高精度三维波浪数值水槽,对多向聚焦波作用下透水和不透水潜堤消波特性进行数值模拟研究。通过计算结果与实验数据的对比,验证了该模型模拟多向聚焦波产生传播及多孔介质内流动的能力,进一步考虑了有效波高、水深、谱峰周期和传播角度等因素对潜堤消波特性的影响。结果表明:潜堤对多向聚焦波的最大波幅、最大水位、沿水深平均最大流速和波能均有显著消减作用,且透水潜堤因孔隙介质的存在消波性能更好。随着有效波高增大,堤顶处波浪破碎点前移,不透水潜堤消波性能受波高影响较大。水深越大,潜堤消波性能越低。透水潜堤对长周期波浪消减效果较差。传播角度越小,聚焦位置处波幅越大,波浪非线性作用越强。研究结果可为我们进一步保护沿海设施免受极端海洋环境的影响提供一定的参考。

基于智能多终端质量控制系统的聚焦功能在临床护理质量管理中的应用

目的:探讨基于智能多终端质量控制系统的聚焦功能在临床护理质量管理中的应用效果。方法:选择2020年7—12月南京医科大学第一附属医院统计系统运行前的170个护理单元的护士长及415名护士设为运行前组,选择2021年7—12月基于智能多终端质量控制系统的聚焦功能运行后的170个护理单元的护士长及415名护士设为运行后组,比较两组的临床护理问题发现率、护理质量管理效率、护士知晓三级护理质控检查结果的时间、护理人员对质控方式满意度。结果:运行后组的问题发现率和巡查执行率结果均高于运行前组,差异有统计学意义(P<0.05)。运行后组计划制定耗时、信息录入耗时、统计分析各环节耗时均短于运行前组,差异有统计学意义(P<0.05)。运行后组护士知晓范围大于运行前,知晓时间短于运行前组,差异有统计学意义(P<0.05);运行后组护士对质控的满意度高于运行前组,差异有统计学意义(P<0.05)。结论:基于智能多终端质量控制系统的聚焦功能让护理质量管理环节质控和过程质控简单化,节约了护理管理时间,有效提高了质量管理效率,提升了护理风险管理,促进了科学决策、智能管理,并提高了护士对质控的满意度。

多靶点重复经颅磁刺激联合目标导向式功能锻炼在脑出血患者康复治疗中的应用

目的探讨多靶点重复经颅磁刺激(MTF-rTMS)联合目标导向式功能锻炼在脑出血患者康复治疗中的应用效果。方法78例脑出血患者随机分为两组,对照组采用目标导向式功能锻炼进行干预,研究组在对照组基础上进行MTF-rTMS干预,比较两组的步态情况、神经功能和日常生活能力。结果干预后,研究组的步频、步速高于对照组,步幅大于对照组(P<0.05)。干预后,研究组的NIHSS评分低于对照组,BI评分高于对照组(P<0.05)。结论MTF-rTMS联合目标导向式功能锻炼在脑出血患者康复治疗中的应用效果较好,可改善患者的步态情况、神经受损情况,提高患者的日常生活能力。

基于深度卷积网络的多聚焦图像自动融合方法

不同聚焦状态的图像融合易产生模糊的问题,因此提出基于深度卷积网络的多聚焦图像自动融合方法。首先检测图像的多聚焦区域,利用深度卷积网络选择图像块的大小,以便于后续的图像融合。选取好图像块大小后,通过小波变换进一步确定频子带系数。最后,结合深度卷积网络的自动融合方法,融合不同聚焦状态的图像,得到一幅完整、视觉效果好的融合图像。实验证明,在融合过程中使用深度卷积网络的多聚焦图像自动融合方法可以得到清晰的图像,具有良好的应用效果。

基于被动波束成像的高强度聚焦超声焦点定位

针对高强度聚焦超声治疗中实际焦点偏离引导治疗的几何焦点这一问题,基于被动波束成像原理,将治疗探头与影像探头联合,探讨一种新的焦点定位策略。首先,基于k-Wave声学软件创建了具有多层组织结构的超声传播模型,通过理论仿真探究了不同脂肪、肌肉厚度条件下被动波束成像方法定位焦点的准确性;除此之外,还通过仿体在实验环境下对该方法的有效性进行了初步验证。结果表明,在不同脂肪厚度的仿真模型中,被动波束合成所定位的焦点和几何焦点在轴向距离上分别与实际焦点相差(0.54±0.15)mm和(4.76±0.95)mm。在不同肌肉厚度的仿真模型中,由合成算法定位的焦点和几何焦点在轴向距离上分别与实际焦点相差(0.51±0.26)mm、(4.95±0.47)mm。仿体实验也验证了被动波束成像定位的焦点比几何焦点更接近实际损伤位置。该文从理论仿真和实验两个角度验证了被动波束成像方法在高强度聚焦超声焦点定位中的优越性,可为该方法进一步走向临床应用提供支撑,对提升高强度聚焦超声手术治疗的安全性有推动作用。

基于信道处理的时间反演幅度可调控多目标聚焦方法

在微波无线输能领域中,如何实现多目标点的电磁波可调控聚焦是一个值得关注的问题.本文提出了一种基于时间反演多径环境下的多目标电磁波聚焦的新方法.该方法基于多个输出之间的信道相关性,将输入和输出节点之间的信道信息进行提取、筛选、加权和重构后在单个发送端上重建反演信号,利用时间反演的空间选择特性实现均衡的电磁波聚焦.基于这种方法,设计了两组在多径环境下的实验.实验结果表明,通过这种方法可以使弱相关模型下不同输出端口获得均衡稳定的聚焦峰,在强相关模型下使不同输出端口的分辨效果进一步提升.此外,6个额外的实验验证了所提出的方法可以在弱相关或强相关的单输入多输出信道模型下,通过改变不同的权值灵活地调整不同接收端的输出峰值电压比.

高强度聚焦超声联合宫腔镜下清宫术对瘢痕妊娠疗效预测模型的建立

目的建立高强度聚焦超声联合宫腔镜下清宫术对瘢痕妊娠疗效预测模型。方法收集138例行高强度聚焦超声联合宫腔镜下清宫术治疗瘢痕妊娠患者的一般资料、妊娠相关指标及手术相关指标,根据疗效将患者分为手术成功组和手术失败组,比较2组患者一般资料、妊娠相关指标及手术相关指标;以LASSO回归筛选变量后行多因素logistic回归分析、筛选出独立性影响因素,根据logistic回归结果构建列线图模型。结果138例高强度聚焦超声联合宫腔镜下清宫术患者中,有15例(10.87%)手术失败;LASSO回归筛选出5个潜在变量,多因素二元logistic逐步回归分析结果显示孕周≥6周、孕囊≥3 cm、术前血人绒毛膜促性腺激素(HCG)水平≥10^(5)IU/L、瘢痕妊娠分型外生型为高强度聚焦超声联合宫腔镜下清宫术失败的危险因素,瘢痕厚度≥3 cm为保护性因素(P<0.05);以R软件构建列线图模型,ROC曲线下面积(AUC)为0.911,95%CI为0.827~0.999(P<0.05);以Bootstrap法对列线图进行内部验证、原始数据重复抽样1000次,平均绝对误差为0.032,H-L拟合优度检验结果显示列线图模型预测概率与实际观察的手术失败率存在较好的一致性(P>0.05),列线图模型预测概率阈值为0.1~0.95时列线图模型临床获益率最高。结论高强度聚焦超声联合宫腔镜下清宫术治疗瘢痕妊娠疗效受孕周、孕囊大小、术前血HCG水平等因素的影响,本研究构建的列线图模型对患者疗效具有较高的预测准确度与区分度,在预测概率阈值为0.1~0.95时列线图模型临床获益率最高。

面向经颅聚焦超声的多阵元相控阵关键技术

经颅聚焦超声是一种有效的神经调控技术,具有非侵入性、聚焦靶点多和焦点可调控等优势。但由于颅骨的强声衰减和非均质特性,聚焦超声经颅后存在焦点偏移、焦域能量不足以及颅骨烫伤等问题。多阵元超声相控阵可以修正超声经颅后的相位偏差和幅值衰减,实现准确、有效的颅内聚焦。本文首先介绍了换能器的阵元排布方式,进一步归纳了相控阵激励信号的调控方法,最后对其基础研究和临床应用进行了回顾与展望。

接管焊缝超声相控阵多模全聚焦检测技术

接管角焊缝是承压设备的关键部位,也是易于出现危害性缺陷的薄弱环节,因此,针对该薄弱区域的定期检测非常重要。相对于传统超声无损检测,超声相控阵全聚焦对焊缝内部缺陷的定位和定量具有优势。提出了一种针对焊缝内部缺陷的超声相控阵全聚焦方法,将多模成像方法应用于小口径接管焊缝的缺陷检测,实现对焊缝内部缺陷的有效检出。同时,根据检测对象的实际尺寸,通过智能算法计算声束入射参数,选择最佳成像区域,获得了一种有效区域多模融合成像方法。试验结果表明,本方法可检出直径为0.53 mm的角焊缝内部气孔缺陷,相对于单视图成像和普通超声能够有效提高缺陷定量和定位的准确性。

“磁性物理与磁性材料”课程多模式教学设计与探索

基于“强化基础、科教融通”的教学理念,提出针对性小班教学,将“知识点教学+翻转课堂+课堂研讨解决问题”等多模式融合的教学手段应用于“磁性物理与磁性材料”课程的教学实践。重视基础拓展的同时,紧跟科技前沿,激发学生研究兴趣,使其积极主动学习。着眼于授课学生的科学研究兴趣和方向,开展针对性教学内容,引导学生将专业理论知识与科研实践相结合,激发学生对未来科研工作的兴趣与热情,助力培养基础知识扎实、自主学习能力强和创新意识高的新时代人才。