基于小波变换与稀疏傅里叶变换相结合的光场重构方法

随着计算机图形学和计算机视觉技术的发展,光场开始进入人们的视线并被迅速应用于各个领域.然而光场的获取需要大量的图像,具有数据量大,获取成本高等特点,因此学者们越来越关注如何利用少量的光场数据获取整个光场这一问题,并且做出了大量的工作.针对上述问题,本文将小波变换与稀疏傅里叶变换相结合,利用光场在角度域的稀疏性提出一种新的光场重构方法.首先,利用小波变换多分辨率分析的特点,通过小波变换将原始图像分解为多个不同频率的子图像;然后分别对每个子图像通过傅里叶切片定理恢复其频率位置,从而可以分别得到它们的二维角度谱;最后将每个子图像的二维角度谱合并,进行小波逆变换获得整个光场.本文方法利用小波变换将原图像分解为多个不同频率的子图像分别同时处理,不仅降低了算法的复杂度,大大减少了算法的运行时间,为光场的广泛应用提供了条件,而且相比于单独运用稀疏傅里叶算法重构,本方法有效地抑制了窗口效应,使重构结果更加准确.此外,本文方法将高频信息和低频信息分开重构,可以有效地改善并网恢复中小频率丢失的问题,进一步改进重构结果.最后通过仿真验证了算法的有效性.     

基于混合属性的零样本图像分类

对于具有相似属性的类别而言,在有限维度的语义属性下,基于属性的零样本图像分类器难以对它们进行正确区分.考虑到语义属性描述类别的有限性,在直接属性预测(Direct Attribute Prediction,DAP)模型的基础上,提出一种基于混合属性的零样本图像分类模型(Hybrid Attribute-Based DAP,HA-DAP).首先,对样本的底层特征进行稀疏编码并利用编码后的非语义属性来辅助现有的语义属性;将非语义属性与语义属性构成混合属性并将其作为DAP模型的属性中间层,利用属性预测模型的思想进行混合属性分类器的训练;最后,根据预测的混合属性以及属性与类别之间的关系进行测试样本类别标签的预测.在OSR、Pub Fig以及Shoes数据集上的实验结果表明,HA-DAP的分类性能优于DAP,不仅能够取得较高的零样本图像分类精度,而且还获得了较高的AUC值.     

基于几何和纹理特征的表情层级分类方法

针对表情识别,为提取对个体差异鲁棒性更强的特征,并有效利用特征自身分布特性,本文提出基于几何和纹理特征的表情层级分类方法.首先,构建基于中性脸相似度的几何特征提取方法,自动匹配样本相似中性脸,提取特征点比例系数几何特征;然后,利用充分矢量三角形提取纹理特征;最后,给出表情层级分类框架,在三个层级下分别利用提取特征判定表情类别.所提方法在JAFFE库和CK库上的实验结果表明,本文方法取得了比基于一般几何和纹理特征的识别方法更好的效果,证明了本文方法的有效性.     

一种新型上行SCMA系统接收机

稀疏码多址接入(SCMA)是一种码域非正交多址接入技术,以其优异性能成为5G多址接入技术的热门候选方案.上行SCMA系统一般采用消息传递算法(MPA)接收机,检测过程中存在由先验信息带来的误差.针对这一问题提出一种新型接收机,称为环MPA (R-MPA)接收机,其通过一种联合检测方案来消除上述误差对最终检测结果的影响.理论分析和仿真验证表明,与现有的log-MPA接收机及经典MPA接收机相比,所提R-MPA接收机是一种检测精度更高而实施复杂度较低的上行SCMA系统接收机.     

基于稀疏贝叶斯学习的电力线载波通信接收机设计

针对多径信道和脉冲噪声对电力线载波通信系统性能影响的问题,提出了一种能有效对抗多径信道和脉冲噪声影响的电力线通信系统接收机设计方案.该方案将时域上的电力线信道参数和脉冲噪声联合视作稀疏向量,然后利用稀疏贝叶斯理论联合估计电力线信道和脉冲噪声,从而在接收端得以去除脉冲噪声及补偿信道增益.仿真结果表明,与传统将信道估计与脉冲噪声抑制单独考虑的传统接收机相比,本文提出的接收机方案在误符号率和误比特率等性能指标上有较好的提升.     

Sparse adaptive constant blind equalization algorithm for sparse multipath channel

In order to improve the convergence rate of the blind equalizer for sparse multipath channel,a novel blind equalization approach called l₀-norm constraint proportionate normalized least mean square constant algorithm was proposed for M-order phase-shift keying (MPSK) signal.Based on the constant modulus characteristics of MPSK signal and the sparse property of equalizer,a new blind equalization cost function with the l₀-norm penalty on the equalizer tap coefficients was firstly constructed.Then the update formula of the tap coefficients was derived according to the gradient descent algorithm.Moreover,the iteration step was updated by drawing upon the normalized proportionate factor.The algorithm not only assigned step sizes proportionate to the magnitude of the current individual tap weights,but also attracted the inactive taps to zero adaptively.Theoretical analysis and simulation results show that the proposed algorithm outperforms the existing blind equalization algorithms for sparse channel in reducing ISI and improving convergence rate.     

6G的价值展望和技术特征

首先结合5G的价值认知,对6G进行了价值展望,并从覆盖性、开放性、安全性、自生性4个方面做了6G的技术特征分析。得出结论,如果把6G作为移动互联网下半场的所有技术创新和模式创新产生的基础,并且始终将持续提升用户对应用和内容的体验作为第一优先级目标,那么6G将比5G多出远远不止“1G”。     

非局部群稀疏表示的图像去噪模型

针对图像的非局部稀疏表示忽略图像中结构相似信息的缺点,将群稀疏表示引入到图像的最优滤波中,提出了一种有效去除图像高斯噪声的非局部群稀疏表示模型。该模型首先选择图像非局部相似块构建相似矩阵,在群稀疏限制下对相似矩阵进行正交分解得到正交矩阵;在已知噪声服从高斯分布的情况下,再通过求得的正交矩阵结合贝叶斯最小均方误差准则实现对特征矩阵的最优估计;最后通过正交矩阵与特征矩阵重构去噪后的图像。实验对比证明,所提的非局部群稀疏表示的图像去噪模型在去除噪声的同时更好地保留了图像的结构信息,获得了更好的主客观评价指标,去噪的峰值信噪比提高1 dB以上。     

Kernel eigenmaps based multiscale sparse model for hyperspectral image classification

Hyperspectral imaging (HSI) is the emerging method that combines traditional imaging and spectroscopy to provide the image with both the spatial and spectral information of the object present in the image. The major challenges of the existing techniques for HSI classification are the high dimensionality of data and its complexity in classification. This paper devises a new technique to classify the HSI named Spatial–Spectral Schroedinger Eigen Maps based Multi-scale adaptive sparse representation (S²SEMASR). In this, two different phases are employed for the accurate classification of the HSI, namely, Schroedinger Eigen maps (SE) based spatial–spectral feature extraction and multi-scale adaptive sparse classification for the feature extracted image. SE makes use of spatial–spectral cluster potentials which allows the extraction of features that best describes the characteristics of different classes of HSI. The multiscale adaptive sparse representation (MASR) applied over the SE features provides the sparse coefficients that includes distinct scale level sparsity with same class level sparsity. With the obtained coefficients, the class label of each pixel is determined. The proposed HSI classifier well utilizes the spectral and spatial characteristics to exploit the within-class variability and thus reduces the misclassification of similar test pixels Experimental results demonstrated that the proposed S²SEMASR approach outperforms the traditional results both qualitatively and quantitatively with an overall accuracy of 98.3%.     

Multi-focus image fusion via Joint convolutional analysis and synthesis sparse representation

Recently, various sparse representation methods have been successfully used in multi-focus image fusion. Most of them produce some spatial artifacts and blurring effects because they only consider the image local information due to the patch processing strategy. In order to reduce the spatial artifacts and blurring effects on the edge details and improve the robustness of the multi-focus image fusion, a novel fusion method based on joint convolutional analysis and synthesis (JCAS) sparse representation is presented. The JCAS model, which integrates the analysis sparse representation and the synthesis sparse representation by using convolutional operation, can effectively separate large-scale structures and fine-scale textures of a single image. First, each source image is decomposed into a base layer and a detail layer using the JCAS model. Second, a Laplacian pyramid transform method is used to fuse the base layers, and a weighted regional energy method is used to fuse the detail layers. Finally, the fused image is reconstructed by combining the fused base and detail layers. Experimental results demonstrate that the proposed method can obtain clearer edge details compared with some popular multi-focus image fusion methods, thus exhibiting state-of-the-art performance in terms of both visual quality and objective assessment.