3D Echocardiogram Reconstruction Employing a Flip Directional Texture Pyramid

Three dimensional(3D)echocardiogram enables cardiologists to visua-lize suspicious cardiac structures in detail.In recent years,this three-dimensional echocardiogram carries important clinical value in virtual surgical simulation.However,this 3D echocardiogram involves a trade-off difficulty between accu-racy and efficient computation in clinical diagnosis.This paper presents a novel Flip Directional 3D Volume Reconstruction(FD-3DVR)method for the recon-struction of echocardiogram images.The proposed method consists of two main steps:multiplanar volumetric imaging and 3D volume reconstruction.In the crea-tion of multiplanar volumetric imaging,two-dimensional(2D)image pixels are mapped into voxels of the volumetric grid.As the obtained slices are discontin-uous,there are some missing voxels in the volume data.To restore the structural and textural information of 3D ultrasound volume,the proposed method creates a volume pyramid in parallel with theflip directional texture pyramid.Initially,the nearest neighbors of missing voxels in the multiplanar volumetric imaging are identified by 3D ANN(Approximate Nearest Neighbor)patch matching method.Furthermore,aflip directional texture pyramid is proposed and aggregated with distance in patch matching tofind out the most similar neighbors.In the recon-struction step,structural and textural information obtained from differentflip angle directions can reconstruct 3D volume well with the desired accuracy.Com-pared with existing 3D reconstruction methods,the proposed Flip Directional 3D Volume Reconstruction(FD-3DVR)method provides superior performance for the mean peak signal-to-noise ratio(40.538 for the proposed method I and 39.626 for the proposed method II).Experimental results performed on the cardi-ac datasets demonstrate the efficiency of the proposed method for the reconstruc-tion of echocardiogram images.

Joint User Association and Caching Placement for Cache-Enabling UAV Networks

Cache-enabling unmanned aerial vehicles(UAVs)are considered for storing popular contents and providing downlink data offloading in cellular networks.In this context,we formulate a joint optimization problem of user association,caching placement,and backhaul bandwidth allocation for minimizing content acquisition delay with consideration of UAVs’energy constraint.We decompose the formulated problem into two subproblems:i)user association and caching placement and ii)backhaul bandwidth allocation.We first obtain the optimal bandwidth allocation with given user association and caching placement by the Lagrangian multiplier approach.After that,embedding the backhaul bandwidth allocation algorithm,we solve the user association and caching placement problem by a threedimensional(3D)matching method.Then we decompose it into two two-dimensional(2D)matching problems and develop low-complexity algorithms.The proposed scheme converges and exhibits a low computational complexity.Simulation results demonstrate that the proposed cache-enabling UAV framework outperforms the conventional UAV-assisted cellular networks in terms of content acquisition delay and the proposed scheme achieves significantly lower content acquisition delay compared with other two benchmark schemes.

Image Restoration Using Hybrid Features Improvement on Morphological Component Analysis

Images are generally corrupted by impulse noise during acquisition and transmission.Noise deteriorates the quality of images.To remove corruption noise,we propose a hybrid approach to restoring a random noisecorrupted image,including a block matching 3D(BM3D)method,an adaptive non-local mean(ANLM)scheme,and the K-singular value decomposition(K-SVD)algorithm.In the proposed method,we employ the morphological component analysis(MCA)to decompose an image into the texture,structure,and edge parts.Then,the BM3D method,ANLM scheme,and K-SVD algorithm are utilized to eliminate noise in the texture,structure,and edge parts of the image,respectively.Experimental results show that the proposed approach can effectively remove interference random noise in different parts;meanwhile,the deteriorated image is able to be reconstructed well.

Coordinate difference homogenization matching method for motion correction in 3D range-intensity correlation laser imaging

This Letter proposes a coordinate difference homogenization matching method to solve motion influence in three-dimensional（3D） range-intensity correlation laser imaging. Firstly, features and feature pairs of gate images are obtained by speeded-up robust figures and bi-directional feature matching methods. The original mean value of the feature-pair coordinate differences is calculated. Comparing the coordinate differences with the original mean value, the wrong feature pairs are removed, and then an optimized mean value is updated. The final feature-pair coordinates are re-registered based on the updated mean value. Thus, an accurate transformation is established to rectify motion gate images for 3D reconstruction. In the experiment, a 3D image of a tower at 780 m is successfully captured by our laser gated imaging system on a pan-tilt device.

A study of projections for key point based registration of panoramic terrestrial 3D laser scan

This paper surveys state-of-the-art image features and descriptors for the task of 3D scan registration based on panoramic reflectance images.As modern terrestrial laser scanners digitize their environment in a spherical way,the sphere has to be projected to a two-dimensional image.To this end,we evaluate the equirectangular,the cylindrical,the Mercator,the rectilinear,the Pannini,the stereographic,and the z-axis projection.We show that the Mercator and the Pannini projection outperform the other projection methods.

A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence

In this paper,a novel compression framework based on 3D point cloud data is proposed for telepresence,which consists of two parts.One is implemented to remove the spatial redundancy,i.e.,a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box.The other part is applied to remove the temporal redundancy of the 3D point cloud data.The temporal redundancy between point clouds is removed by using the motion vector,i.e.,the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame.The hrst,the B-SHOT(binary signatures of histograms orientation)descriptor is applied to represent the point feature for matching the corresponding point between two frames.The second,the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame.The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames.Finally,the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the curren t and the motion vectors are transmit ted into the rem ote end.In order to reduce calculation time of the B-SHOT descriptor,we introduce an octree structure into the B-SHOT descriptor.In particular,in order to improve the robustness of the matching operation,we design the cluster feature to estimate the similarity bet ween two clusters.Experimen tai results have shown the bet ter performance of the proposed method due to the lower calculation time and the higher compression ratio.The proposed met hod achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.

局部均值噪声估计的盲3维滤波降噪算法

目的图像在获取和传输的过程中很容易受到噪声的干扰,图像降噪作为众多图像处理系统的预处理模块在过去数十年中得到了广泛的研究。在已提出的降噪算法中,往往采用加性高斯白噪声模型AWGN(additive white Gaussian noise)为噪声建模,噪声水平(严重程度)由方差参数控制。经典的BM3D 3维滤波算法属于非盲降噪(non-blind denoising algorithm)算法,在实际使用中需要由人工评估图像噪声水平并设置参数,存在着噪声评估值随机性大而导致无法获得最佳降噪效果的问题。为此,提出了一种新的局部均值噪声估计(LME)算法并作为BM3D算法的前置预处理模块。方法本文专注于利用基于自然统计规律(NSS)的图像质量感知特征和局部均值估计技术构建图像噪声水平预测器,并通过它高效地获得噪声图像中准确的噪声水平值。关于自然场景统计方面的研究表明,无失真的自然场景图像在空域或者频率域上具有显著的统计规律,一旦受到噪声干扰会产生规律性的偏移,可以提取这些特征值作为反映图像质量好坏的图像质量感知特征。另外,局部均值估计因其简单而高效率的预测特性被采用。具体实现上,在具有广泛代表性且未受噪声干扰图像集合上添加不同噪声水平的高斯噪声构建失真图像集合,然后利用小波变换对这些失真图像进行不同尺度和不同方向的分解,再用广义高斯分布模型(GGD)提取子带滤波系数的统计信息构成描述图像失真程度的特征矢量,最后用每幅失真图像上所提取的特征矢量及对其所施加的高斯噪声水平值构成了失真特征矢量库。在降噪阶段,用相同的特征提取方法提取待降噪的图像的特征矢量并在失真特征矢量库中检索出与之类似的若干特征矢量及它们所对应的噪声水平值,然后用局部均值法估计出待降噪图像中高斯噪声大小作为经典BM3D算法的输入参数。结果改进后的BM3D算法转换为盲降噪算法,称为BM3D-LME(block-matching and 3D filtering based on local means estimation)算法。准确的噪声估计对于诸如图像降噪,图像超分辨率和图像分割等图像处理任务非常重要。已经验证了所提出噪声水平估计算法的准确性、鲁棒性和有效性。结论相对人工进行噪声估计,LME算法能够准确、快速地估算出任意待降噪图像中的噪声大小。配合BM3D算法使用后,有效提高了它的实际降噪效果并扩大它的应用范围。

Improved image denoising via RAISR with fewer filters

In recent years,accurate Gaussian noise removal has attracted considerable attention for mobile applications,as in smart phones.Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time.Therefore,we propose a rapid post-processing method for Gaussian noise removal in this paper.Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise.Although these nonlocal image denoising methods have quantitatively high performance,some fine image details are lacking due to the loss of high frequency information.To tackle this problem,an improvement to the pioneering RAISR approach(rapid and accurate image super-resolution),is applied to rapidly post-process the denoised image.It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost,preserving important image structures well.Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements:geometric conversion and reduction of the strength classes.In addition,following RAISR,the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results.Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods,efficiently and with low memory requirements.