融合小波的D2D技术在电力应急通信中的应用

为在公网盲区建立高效、稳定的应急通信网络,文中首先介绍了D2D自组网通信系统,其次研究并提出一种改进的基于小波变换的图像压缩技术,并应用于D2D自组网络,最后分别进行了对比实验。证明该压缩技术的编解码效率更高、重构图像的质量更好,图像在融合该压缩技术的D2D自组网下传输时延更小,提高了数据传输速率和网络畅通性。利用融合小波变换的D2D技术能够更高效地传输突发事件现场的数据信息,更好满足电力应急通信的实际需求。     

广播与电视

1410 声象理论与技术0503386小波分析在图像消嗓中的应用[刊,中]/李春鑫//光电技术应用.—2004,19(5).—25-27,32(D)针对图像消噪同题,提出了二维快速小波算法和改进小波包分析算法,通过对图像的消噪处理,二维快速小波算法消噪效果明显,但由于小波包分析算法对上一层的低频部分和高频部分同时进行细分,具有更为精确的局部分析能力,对小波包分析算法进行了改进,消除了频带混叠问题,其消噪效果更佳,可得到更为广泛的应用。参4     

Dialog半导体推出首款2D到3D视频转换芯片

<正>Dialog半导体股份有限公司日前宣布推出全球首款2D/3D影像转换实时处理芯片:DA8223。该芯片为包括智能手机和平板电脑等在内的各种便携式设备提供了2D/3D视频影像实时转换处理的功能。该器件同时也集成了一个视差栅栏(parallax barrier)屏幕驱动器,允许用户在不需要眼镜的情况下观看3D内容。该芯片对每一帧2D视频图像进行分析,通过分离前景图像和背景图像,创造出一个分层的深度映射图(Z-depth)。     

小波基对红外目标图像SPIHT算法性能的影响

针对红外目标图像边缘模糊、噪声点多、对比度较低等特点,从分析小波基函数的三个评估指标入手,进行了大量的实验和统计,研究不同的小波基对红外目标图像SPIHT算法性能的影响,通过实验结果的分析与讨论,得出双正交小波D9/7小波和bior4.4小波适于红外目标图像实时压缩的结论,这一结论也有助于将来更好地发挥SPIHT算法的性能。     

基于区域融合的单视点图像深度信息提取

将2D转换为3D是目前3D多媒体成功应用的重要解决方法之一.而在2D至3D转换过程中,单视点图像中深度信息提取是最具挑战的任务.提出一种基于区域融合的单视点图像深度信息提取方法,利用区域融合对单视点图像进行区域融合,用先验假设的深度梯度图对区域进行深度分配,得到基于区域融合的深度图.实验结果表明通过所提方法得到的深度图...     

一维小波变换在时域光学相干层析成像中的应用

时域光学相干层析(OCT)系统通常采用短时傅里叶变换(STFT)完成干涉信号的解调和图像重构。短时傅里叶变换算法简单,但是在干涉信号解调时难以获得好的去噪效果,通常还需在二维(2D)图像域对重构图像进行去噪。该方法数据运算量大,集成度不高。将一维(1D)小波变换(WT)应用于时域光学相干层析成像技术,同时实现干涉信号解调、去噪和图像重构。算法将时域光学相干层析的干涉信号分解到各个不同的频率空间,保留包含调制频率的频率空间的小波系数,对保留的小波系数进行滤波去噪后进行逆变换即可实现对干涉信号的解调和去噪,对解调信号等间距采样实现图像重构。该方法数据运算量小,集成度高,结合先进的小波去噪技术可以大大提高重构图像的分辨率,具有良好的应用前景。     

加权SIFT流深度迁移的单幅图像2D转3D

2D视频转3D视频是解决3D片源不足的主要手段,而单幅图像的深度估计是其中的关键步骤.提出基于加权SIFT流深度迁移和能量模型优化的单幅图像深度提取方法.首先利用图像的全局描述符从深度图数据库中检索出近邻图像;其次通过SIFT流建立输入图像和近邻图像之间像素级稠密对应关系;再次由SIFT流误差计算迁移权重,将近邻图像对应像素点的深度乘以权重后迁移到输入图像上;然后利用均值滤波对迁移后的近邻图像深度进行融合;最后建立深度图优化能量模型,在尽量接近迁移后近邻图像深度的前提下,平滑梯度较小区域的深度.实验结果表明,该方法降低了估计深度图的平均相对误差,增强了深度图的均匀性.     

基于静态多小波变换的图像融合

综合了多小波分析和平移不变性质的优势,将多小波分析扩展到静态多小波的范畴,提出了一种基于静态多小波变换(SMWT)的图像融合方法。通过对源图像进行SMWT,根据变换系数的尺度内多子带联合窗口(CBWI)特性,实现多源图像的融合。该方法应用于一类多聚焦图像融合的仿真实验中,从视觉效果和信息量指标(互信息量和交叉熵)2个方面对融合图像进行主观评判和量化评价。结果表明,相比于传统小波域内的图像融合算法,该方法得到的融合结果具有更良好的视觉质量和更优的量化指标,体现出更强的融合性能。     

基于小波分析的图像压缩

从小波分析的理论及研究现状出发,阐述了小波分析在图像压缩中的应用,并通过实验说明小波分析在图像处理中的作用。实验以matlab7.0作为平台,使用wavedec2和appcoef2函数进行二维小波分解和获取小波分解的近似分量,并且使用detcoef2函数来获取两层二维小波分解的细节分量,最后使用wrcoef2函数对各层的分量进行重构。实验表明,利用小波分析对图像进行压缩可以得到非常好的压缩效果。     

小波核极限学习机在人脸识别中的应用

文章提出了一种基于小波核极限学习机(Wavelet Kernel Extreme Learning Machine,WK-ELM)的人脸识别算法。首先,使用2D盖博小波变换对人脸图片进行初步的人脸特征提取。为了从所有提取的特征中选择出与人脸识别相关的、必要的特征,使用主成分分析法(Principal Component Analysis,PCA)对经过初步处理后的图像再进行进一步处理,有效地降低了特征维数。然后使用小波核极限学习机对提取到的图像进行分类。实验证明,小波核极限学习机不仅识别性能高,而且训练速度也优于其他算法。     

Research on a face recognition algorithm based on 3D face data and 2D face image matching

Under the condition of weak light or no light, the recognition accuracy of the mature 2D face recognition technology decreases sharply. In this paper, a face recognition algorithm based on the matching of 3D face data and 2D face images is proposed. Firstly, 3D face data is reconstructed from the 2D face in the database based on the 3DMM algorithm, and the face depth image is obtained through orthogonal projection. Then, the average curvature map of the face depth image is used to enhance the data of the depth image. Finally, an improved residual neural network based on the depth image and curvature is designed to compare the scanned face with the face in the database. The method proposed in this paper is tested on the 3D face data in three public face datasets (Texas 3DFRD, FRGC v2.0, and Lock3DFace), and the recognition accuracy is 84.25%, 83.39%, and 78.24%, respectively.     

Depth Mapping of Integral Images Through Viewpoint Image Extraction With a Hybrid Disparity Analysis Algorithm

Integral imaging is a technique capable of displaying 3D images with continuous parallax in full natural color. It is one of the most promising methods for producing smooth 3D images. Extracting depth information from integral image has various applications ranging from remote inspection, robotic vision, medical imaging, virtual reality, to content-based image coding and manipulation for integral imaging based 3D TV. This paper presents a method of generating a depth map from unidirectional integral images through viewpoint image extraction and using a hybrid disparity analysis algorithm combining multi-baseline, neighborhood constraint and relaxation strategies. It is shown that a depth map having few areas of uncertainty can be obtained from both computer and photographically generated integral images using this approach. The acceptable depth maps can be achieved from photographic captured integral images containing complicated object scene.     

No-reference Stereoscopic Image Quality Assessment Using Binocular Self-similarity and Deep Neural Network

Quality assessment of three-dimensional (3D) images is more challenging than that of 2D images. The quality of 3D visual experience is one of the most challenging areas of human binocular perception and is affected by multiple factors such as asymmetric stereo image/video compression, depth perception, visual discomfort, and single view quality. In this paper, we propose a new no-reference quality assessment method for stereoscopic images based on Binocular Self-similarity (BS) and Deep Neural Networks (DNN). To be more specific, a BS index is defined and computed according to binocular rivalry and suppression based on the depth image-based rendering technique. Then, a DNN is trained in an opinion unaware way to predict local quality. Binocular integration (BI) index is calculated by using the trained DNN, accounting for binocular integration behaviors. Finally, the final quality score of stereoscopic image is obtained by combining the BS and BI indexes together. Experimental results on four public 3D image quality assessment databases demonstrate that compared with existing methods, the proposed method can achieve high consistency with subjective perception on stereoscopic images with both symmetric and asymmetric distortions.     

对象引导的单幅散焦图像深度提取方法

2D图像转3D图像是解决3D影视内容缺乏的主要手段之一,而深度提取是其中的关键步骤.考虑到影视作品中存在大量散焦图像,提出单幅散焦图像深度估计的方法:首先通过高斯卷积将散焦图像转换成两幅模糊程度不同的图像;其次计算这两幅图像在边缘处的梯度幅值比例,进而根据阶跃信号与镜头的卷积模型得到边缘处的模糊度;再次将边缘处的模糊度转换成图像的稀疏深度并利用拉普拉斯矩阵插值得到稠密深度图;最后通过图像的视觉显著度提取前景对象,建立对象引导的深度图优化能量模型,使前景的深度趋于一致并平滑梯度较小区域的深度.该方法利用对象引导的深度优化,剔除了拉普拉斯矩阵插值引入深度图的纹理信息.模拟图像的峰值信噪比和真实图像的视觉对比均表明该算法比现有方法有较大改善.     

Real-Time 3D Depth Generation for Stereoscopic Video Applications with Thread-Level Superscalar-Pipeline Parallelization

We propose a low-complexity algorithm for stereoscopic video applications that generates a high-quality 3D image depth map from a single 2D image. Based on their characteristics, 2D images are classified into one of three categories before being processed by the proposed low-complexity algorithm to generate corresponding depth maps. We also extend the 3D depth algorithm to construct a parallel 3D video system. A thread-level superscalar-pipelining approach is developed to parallelize the 3D video system. Experimental results for HD1080 resolution images demonstrate that the algorithm can generate high-quality depth maps with an average reduction in the computational complexity of 98.2 % compared with a conventional algorithm. The parallel 3D video system can achieve a processing speed of 63.66 fps for HD720 resolution video.     

无深度反转的集成成像一次拍摄方法

针对传统集成成像显示技术存在深度反转,需要进行二次成像的问题,提出一种无深度反转的集成成像一次拍摄方法。该方法采用离轴平行式集成成像拍摄结构对三维(3D)场景进行拍摄,通过设计合理的拍摄参数,重排图像元,生成无梯形畸变的图像阵列(EIA),直接用于集成成像显示,解决了传统集成成像的深度反转问题,避免了复杂且繁琐的图像校正和二次成像过程,可快速生成具有正确深度信息的EIA。该方法所获取的EIA在集成成像3D显示实验中重建的3D图像具有正确的深度和逼真清晰的立体显示效果,验证了本文方法的正确性。     

A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency

With the emerging development of three-dimensional (3D) related technologies, 3D visual saliency modeling is becoming particularly important and challenging. This paper presents a new depth perception and visual comfort guided saliency computational model for stereoscopic 3D images. The prominent advantage of the proposed model is that we incorporate the influence of depth perception and visual comfort on 3D visual saliency computation. The proposed saliency model is composed of three components: 2D image saliency, depth saliency and visual comfort based saliency. In the model, color saliency, texture saliency and spatial compactness are computed respectively and fused to derive 2D image saliency. Global disparity contrast is considered to compute depth saliency. Particularly, we train a visual comfort prediction function to distinguish stereoscopic image pair as high comfortable stereo viewing (HCSV) or low comfortable stereo viewing (LCSV), and devise different computational rules to generate a visual comfort based saliency map. The final 3D saliency map is obtained by using a linear combination and enhanced by a “saliency-center bias” model. Experimental results show that the proposed 3D saliency model outperforms the state-of-the-art models on predicting human eye fixations and visual comfort assessment.     

Depth estimation of supervised monocular images based on semantic segmentation

In recent years, the research method of depth estimation of target images using Convolutional Neural Networks (CNN) has been widely recognized in the fields of artificial intelligence, scene understanding and three-dimensional (3D) reconstruction. The fusion of semantic segmentation information and depth estimation will further improve the quality of acquired depth images. However, how to deeply combine image semantic information with image depth information and use image edge information more accurately to improve the accuracy of depth image is still an urgent problem to be solved. For this purpose, we propose a novel depth estimation model based on semantic segmentation to estimate the depth of monocular images in this paper. Firstly, a shared parameter model of semantic segmentation information and depth estimation information is built, and the semantic segmentation information is used to guide depth acquisition in an auxiliary way. Then, through the multi-scale feature fusion module, the feature information contained in the neural network on different layers is fused, and the local feature information and global feature information are effectively used to generate high-resolution feature maps, so as to achieve the goal of improving the quality of depth image by optimizing the semantic segmentation model. The experimental results show that the model can fully extract and combine the image feature information, which improves the quality of monocular depth vision estimation. Compared with other advanced models, our model has certain advantages.     

2D/3D 距离选通成像的低对比度目标探测

低对比度目标探测在司法取证、反恐维安、远距离监控、搜救等应用中具有重要意义,然而传统二维成像方法则难以对其有效探测。由此,提出了基于2D/3D 距离选通成像探测低对比度目标的方法:通过2D 距离选通成像直接获取目标无背景或背景部分滤除的二维选通图像,从而突显目标,简化目标提取图像处理;当复杂背景无法有效滤除时,可进一步通过3D 距离选通成像重建二维选通图像中丢失的三维空间信息,通过距离图区分目标与背景,实现目标有效探测。在该方法中,3D 距离选通成像是基于上述二维选通图像采用超分辨率三维成像反演实现的,因此无需耗时获取新数据,从而提高了实时性,并压缩了数据量。研究和实验表明:该方法可有效解决低对比度目标探测问题,在低照度及恶劣天气环境下均可有效工作。