基于稀疏分解的图像压缩

图像稀疏分解是一种新的图像分解方法,可以将图像分解为很简洁的近似表达形式。在图像稀疏分解的基础上,对分解出的数据进行压缩。通过对分解结果数据分布规律进行分析,提出了量化和编码的方案,期望在低比特率情况下,得到较好的解码图像质量。实验结果表明基于稀疏分解的图像压缩方法得到的解压图像有较好的主观图像质量。     

基于局部方差的模糊小波阈值图像去噪

针对小波阈值去噪中的VisuShrink阈值(统一阈值)“过扼杀”细节系数和SUREShrink阈值(Stein无偏估计阈值)“过保留”噪声系数的特点,提出了一种新的模糊小波阈值去噪方法。该方法根据小波系数局部方差能体现信号受噪声影响程度的特点,在局部方差之间引入模糊区域,通过模糊区域内局部方差的大小来计算相应小波系数的噪声隶属度。根据不同的模糊隶属度在VisuShrink阈值与SUREShrink阈值之间选取去噪阈值,并用软阈值函数消减。实验结果与仿真表明,提出的模糊阈值方法有较好的去噪效果。     

基于隐Markov树的设备状态综合诊断模型

为了充分利用小波系数之间的统计相依性以更有效地诊断设备状态,提出了一种基于隐Markov树(HMT)的综合诊断模型。首先通过主成分分析将来自多个传感器的信号转换为主成分,求出各主成分对应的频谱,然后通过比较对已训练的各HMT模型的适应度,运用Bayes决策融合法则得到设备状态综合诊断决策。为了克服HMT模型存在的计算溢出困难,采用尺度变换对EM算法进行了改进。通过两个实例验证了该综合诊断模型具有较高的诊断准确率。     

高频雷达中提取海洋回波信号的新方法

雷达信号经过海面反射时,由于电离层的作用和外部的干扰,必然会有一些噪声夹杂在海洋回波中。噪声是一些无规则的频谱,对分析有用信号是极其不利的,严重时甚至淹没有用的信号。将噪声中的信号提取出来成了一个很关键的问题。基于海洋回波信号是1/f类分形信号的特性,利用不同尺度下小波系数方差来估计噪声方差及分形信号参数,进而从白噪声背景下估计出海洋回波信号。实验结果表明:用新方法处理过的海洋回波的多普勒谱的信噪比提高了10-15 dB。     

η_(e,k)~j算子作用下的小波变换误差分析

在研究拟微分算子及预小波基础上,探讨了jeη,k算子作用下的小波变换,得到了一些新的有用的结果.     

Optimal motion planning of an underactuated spacecraft using wavelet approximate method

An optimal motion planning scheme using wavelet approximation is proposed for an underactuated spacecraft. The motion planning of an underactuated spacecraft can be formulated as an optimal control of a drift-free system. A cost functional is used to incorporate the control energy and the final state errors. The motion planning is to determine control inputs to minimize the cost functional. Using the method of wavelet, one can transform an infinite-dimensional optimal control problem to a finite-dimensional one and use Gauss-Newton algorithm to solve it for a feasible trajectory which satisfies nonholonomic constraints. The proposed scheme has been applied to a rigid spacecraft with two momentum wheels. The numerical simulation results indicate that optimal control with wavelet approximation is an effective approach to steering an underactuated spacecraft system from the initial configuration to the final configuration.     

fMRI time series analysis based on stationary wavelet and spectrum analysis

The low signal to noise ratio (SNR) of functional MRI (fMRI) prefers more sensitive data analysis methods. Based on stationary wavelet transform and spectrum analysis, a new method with high detective sensitivity was developed for analyzing fMRI time series, which does not require any prior assumption of the characteristics of noises. In the proposed method, every component of fMRI time series in the different time-frequency scales of stationary wavelet transform was discerned by the spectrum analysis, then the components from noises were removed using the stationary wavelet transform, finally the components of real brain activation were detected by cross-correlation analysis. The results obtained from both simulated and in vivo visual experiments illustrated that the proposed method has much higher sensitivity than the traditional cross-correlation method.     

基于时间轴小波变换的运动对象分割算法研究

对时间轴一维小波变换的视频运动对象分割算法进行了研究。将视频序列进行时间轴一维小波变换，利用变换后的高频帧信息提取出初步的运动掩模图像；进行数学形态学后处理，以消除各种噪声的影响和对象的不连通性，得到理想的运动掩模图像。实验结果表明该方法分割效果较好，分割出的运动对象可直接应用于基于对象的视频编码，且算法耗时较少。     

Digital Watermarking Algorithm Based on Wavelet Transform and Neural Network

An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system （HVS） characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.     

一种基于小波提升变换的多尺度边缘提取算法

提出了一种基于小波提升变换的改进图像边缘的检测算法.本算法首先对源图像进行小波提升分解,然后分别对高、低频子图像进行边缘提取.对于低频信息使用Canny算子进行边缘检测,而高频信息先用相邻尺度小波系数相乘的方法去除噪声,消噪后再对高频分量进行边缘检测.最后通过一定的融合规则进行融合,得到最终的边缘图像.实验结果表明,该方法具有运算速度快,能有效地抑制噪声,边缘检测精度高等特点,是一种有效的图像边缘提取算法.