基于卡尔曼滤波的序列图像超分辨率重建算法

针对给定一序列低分辨率图像重建出一序列高分辨率图像的问题,应用卡尔曼滤波理论,将时间轴加入重建算法,把利用多幅低分辨率图像生成高分辨率图像的模型转化为利用当前低分辨率图像和上一幅高分辨率图像来构建当前高分辨率图像.通过引入信息对的概念对算法运算复杂度进行改进.实验证明与已有算法相比该方法有较好的时效性和实用性.     

A super-resolution algorithm for spectral estimation and time series extrapolation

Recent developments in the signal processing field of electrical engineering have resulted in several frequency domain methods of extrapolating a time series. Insight gained in testing one such method, the Papoulis algorithm, has been used to suggest modifications which greatly improve its performance under most operating conditions where real data are concerned. The modified Papoulis method thus developed has been applied to electricity load forecasting over the short and medium term, as well as to world economic and energy data, to assess the cyclic structure present in each series about a trend.     

模型误差对阵列信号特征结构的影响及一种校准方法

本文针对阵列信号处理中存在的阵元位置扰动和通道特性不一致两种模型误差，讨论了相位误差对阵列信号特征结构的影响。提出了一组扰动前后特征结构变化的关系式。基于这组公式，提出了一种基于辅助源的校准方法，这种方法可以避免多维搜索和受随机噪声的影响。计算机仿真结果表明了这种方法的有效性。进一步的研究工作尚在进行。     

基于小波变换和残差处理的人脸图像超分辨率技术研究

超分辨率图像复原技术常见的有插值法,POCS等,它们有细节模糊,运算复杂度大的弱点,针对以上问题,在基于学习的超分辨率图像复原中,提出了一种全新的小波系数特征向量匹配方法.算法分为两步：（1）采用基于补偿残差向量和多样本平均的低分辨率人脸图像的小波特征向量匹配及人脸图像复原.（2）用边缘提取和特定区域平滑的方法去除Gibbs效应等噪声.经实验和传统的插值法以及常规匹配方法比较,在细节复原和运算复杂度方面都有一定的提高.     

—种使用小波变换的凸集投影超分辨图像重建

凸集投影（POCS）是一种把多帧低分辨率图像重构为高分辨率图像的算法,但是该算法在图像的高频信息恢复和降噪方面不是很理想。在本文中,在凸集投影（POCS）算法的基础上利用小波变换来提取出隐藏在低分辨图像中的高频信息和降低低分辨图像的噪声,可以更好地恢复出图像的细节并提高信噪比。实验仿真的结果表明,此方法在细节重构方面确实更优于凸集投影（POCS）算法。     

基于去卷积距离超分辨方法的机理及限制条件

从频谱均衡的角度解释了去卷积方法能获得距离超分辨性能的原理。结果表明超分辨的原理在于利用了 3dB带宽以外的频率分量。经过均衡以后 ,3dB带宽以外的所有频谱分量的幅度将会相同 ,即信号的带宽得到了外推 ,从而分辨率得到提高。分析了实际系统对该方法性能所产生的可能限制。去卷积方法能否在系统中应用需视系统的具体情况而定 ,如采样率、噪声模型及多普勒频移等。     

一种改进的多帧图像超分辨率重建算法

文中构建了超分辨率重建图像的一般框架.在对图像模糊的不确定性和复杂性作一定限制条件下,讨论采用最小二乘方规整化方法重建除运动外其它因素引起降质的低分辨率图像;并进一步提出了采用改进的递归最速下降迭代算法实现多帧图像的超分辨率重建.计算机模拟结果表明,该方法具有较好的重建图像质量.     

一种有效的二维MUSIC超分辨SAR成像算法

多重信号分类(MUSIC)方法是一种高分辨率到达角(DOA)估计方法,将MUSIC方法应用于合成孔径雷达(SAR)可以很好地提高图像质量。提出了一种简单有效的2D MUSIC超分辨成像算法,该方法对已聚焦的SAR图像进行处理,减小了处理过程的复杂性,并结合幅度估计完成超分辨率图像重建。实验结果验证了这种超分辨率方法的有效性。     

急性运动中超分辨率图像重构方法研究

采用当前急性运动中超分辨率图像重构方法得到的重构图像存在全局误差,导致重构图像质量低下,重构效果不佳。为此,提出一种新的急性运动中超分辨率图像重构方法,设计急性运动中超分辨率图像重构模型,将小波稀疏字典作为急性运动中超分辨率图像重构的理论依据。将低分辨率急性运动图像分割成低分辨率图像块,对无噪高分辨率急性运动图像块相应的无噪低分辨率图像块进行分析。通过OMP方法对稀疏系数进行求解,依据得到的稀疏系数估计出高分辨率急性运动图像块的高频小波系数,将高分辨率小波系数急性运动图像块返回高分辨率小波系数急性运动图像,通过逆小波变换得到最终的高分辨率图像,对全局误差进行修正。实验结果表明,采用所提方法得到的重构图像质量高,重构效果好。     

Structured illumination microscopy for super-resolution and optical sectioning

Optical microscopy plays an essential role in biological studies due to its capability and compatibility of non-contact, minimally invasive observation and mea- surement of live specimens. However, the conventional optical microscopy only has a spatial resolution about 200 nm due to the Abbe diffraction limit, and also lacks the ability of three-dimensional imaging. Super-resolution far- field optical microscopy based on special illumination schemes has been dramatically developed over the last decade. Among them, only the structured illumination microscopy （SIM） is of wide-field geometry that enables it easily compatible with the conventional optical microscope. In this article, the principle of SIM was introduced in terms of point spread function （PSF） and optical transform function （OTF） of the optical system. The SIM for super-resolution （SIM-SR） proposed by Gustafsson et al. and the SIM for optical sectioning （SIM-OS） pro- posed by Neil et al. are the most popular ones in the research community of microscopy. They have the same optical configuration, but with different data post- processing algorithms. We mathematically described the basic theories for both of the SIMs, respectively, and pre- sented some numerical simulations to show the effects of super-resolution and optical sectioning. Various approaches to generation of structured illumination patterns were reviewed. As an example, a SIM system based on DMD- modulation and LED-illumination was demonstrated. A lateral resolution of 90 nm was achieved with gold nanoparticles. The optical sectioning capability of the microscope was demonstrated with Golgi-stained mouse brain neurons, and the sectioning strength of 930 nm was obtained.