一种多径环境下基于四阶累积量的阵列扩展测向方法

本文分析了基于四阶累积量的测向方法不能正确测量多径信号方向的原因，提出了一种多径环境下的基于四阶累积量的新型测向方法。该方法利用零点领处理方法抑制多径信号，然后采用四阶累积量进行阵列扩展测向。计算机模拟结果表明该方法在多径环境下具有良好的性能并且可能实现对足够多的信号进行测向。     

Limits of Learning-Based Superresolution Algorithms

Learning-based superresolution (SR) is a popular SR technique that uses application dependent priors to infer the missing details in low resolution images (LRIs). However, their performance still deteriorates quickly when the magnification factor is only moderately large. This leads us to an important problem: “Do limits of learning-based SR algorithms exist?” This paper is the first attempt to shed some light on this problem when the SR algorithms are designed for general natural images. We first define an expected risk for the SR algorithms that is based on the root mean squared error between the superresolved images and the ground truth images. Then utilizing the statistics of general natural images, we derive a closed form estimate of the lower bound of the expected risk. The lower bound only involves the covariance matrix and the mean vector of the high resolution images (HRIs) and hence can be computed by sampling real images. We also investigate the sufficient number of samples to guarantee an accurate estimate of the lower bound. By computing the curve of the lower bound w.r.t. the magnification factor, we could estimate the limits of learning-based SR algorithms, at which the lower bound of the expected risk exceeds a relatively large threshold. We perform experiments to validate our theory. And based on our observations we conjecture that the limits may be independent of the size of either the LRIs or the HRIs.     

基于混合POCS的视频图像超分辨率重建

提出混合凸集投影算法HPOCS对视频图像进行超分辨率重建,利用连续视频图像间的互异信息生成更高分辨率的视频图像.该算法首先采用图像匹配估计视频图像间的运动位移;然后进行基于的APEX盲解卷积,估计点扩散函数和理想视频图像;最后在凸集投影的理论框架下进行图像重建.实验表明,HPOCS重建后,视频图像的分辨率相对于原始图像、双线性内插图像和POCS重建图像明显提高,图像边缘更加清晰,细节信息更加突出.     

An efficient algorithm for superresolution in medium field imaging

In this paper, we study the problem of reconstruction of a high-resolution (HR) image from several blurred low-resolution (LR) image frames in medium field. The image frames consist of blurred, decimated, and noisy versions of a HR image. The HR image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, a HR image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore HR images in the aperiodic boundary condition. Computer simulations are given to illustrate the effectiveness of the proposed approach. This research was conducted with support from the Army Research Office Grant DAAD 19-03-1-0261 and the National Science Foundation Grant CCF-0429481. Research supported in part by RGC Grant Nos. 7130/02P, 7046/03P, 7035/04P and 7035/04P and FRG/04-05/II-51.     

最大熵线性预测超分辨算法的误差灵敏度分析

本文分析了Burg提出的最大熵线性预测超分辨算法对阵列幅度和相位误差的灵敏度。给出了具有两个目标、不同阵元数和不同间距等情况下的数值计算结果。结果表明最大熵算法较最大似然估计(MLM)算法和MUSIC算法对幅相误差的影响更加敏感。文中还给出了最大熵算法和MUSIC算法分辨目标的实验结果。     

A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution)

Superresolution produces high-quality, high-resolution images from a set of degraded, low-resolution images where relative frame-to-frame motions provide different looks at the scene. Superresolution translates data temporal bandwith into enhanced spatial resolution. If considered together on a reference grid, given low-resolution data are nonuniformly sampled. However, data from each frame are sampled regularly on a rectangular grid. This special type of nonuniform sampling is called interlaced sampling. We propose a new wavelet-based interpolation-restoration algorithm for superresolution. Our efficient wavelet interpolation technique takes advantage of the regularity and structure inherent in interlaced data, thereby significantly reducing the computational burden. We present one- and two-dimensional superresolution experiments to demonstrate the effectiveness of our algorithm.This work was supported in part by the National Science Foundartion Grant CCR-9984246.     

特征值平移超分辨率算法

本文分析了逆幂迭代算法的收敛速度。为了克服逆幂迭代算法矩阵求逆的过程,提出了特征值平移幂迭代算法,并分析了它的收敛速度,给出了计算机模拟结果。     

正交化超分辨算法的误差灵敏度分析

本文给出了基于Gram-Schmidt正交化方法的超分辨算法,并分析了该算法对阵列幅度和相位误差的灵敏度.给出了两个目标、不同阵元数和不同阵元间距等情况下的灵敏度计算结果。结果表明,本文的算法在目标小角度间隔、大阵元数的情况下,受幅相误差的影响没有最大似然估计(MLM)和MUSIC算法敏感。     

超分辨方法在多普勒波束锐化中的应用

Relax超分辨算法基于信号和噪声参数化模型的谐波恢复方法,具有比傅里叶变换更高的分辨率。本文讨论了Relax超分辨方法,并把它应用于实测数据多普勒波束锐化(DBS)成像中;提出了一种基于Relax超分辨算法的目标特征提取方法。实验表明Relax超分辨算法比传统的基于傅里叶变换的多普勒波束锐化(DBS)方法具有较好的效果．     

EIGNVALUE SHIFT SUPPERRESOLUTION ALGORITHM

This paper provides a new approach to spatial spectral estimation, called eigenvalue shift superresolution algorithm. This method avoids the inverse covariance matrix. It can be applied to the processing of data received by spatially distributed array of sensors.