Adaptive reconstruction technique for the lost information of the rectangular image area

The adaptive reconstruction for the lost information of the rectangular image area is very important for the robust transmission and restoration of the image. In this paper, a new reconstruction method based on the Discrete Cosine Transform (DCT) domain has been put forward. According to the low pass character of the human visual system and the energy distribution of the DCT coefficients on the rectangular boundary, the DCT coefficients of the rectangular image area are adaptively selected and recovered. After the Inverse Discrete Cosine Transform (IDCT), the lost information of the rectangular image area can be reconstructed. The experiments have demonstrated that the subjective and objective qualities of the reconstructed images are enhanced greatly than before.     

Scalable perceptual audio representation with an adaptive three time-scale sinusoidal signal model

This work is concerned with the development and optimization of a signal model for scalable perceptual audio coding at low bit rates. A complementary two-part signal model consisting of Sines plus Noise (SN) is described. The paper presents essentially a fundamental enhancement to the sinusoidal modeling component. The enhancement involves an audio signal scheme based on carrying out overlap-add sinusoidal modeling at three successive time scales, large, medium, and small. The sinusoidal modeling is done in an analysis-by-synthesis overlapadd manner across the three scales by using a psychoacoustically weighted matching pursuits. The sinusoidal modeling residual at the first scale is passed to the smaller scales to allow for the modeling of various signal features at appropriate resolutions. This approach greatly helps to correct the pre-echo inherent in the sinusoidal model. This improves the perceptual audio quality upon our previous work of sinusoidal modeling while using the same number of sinusoids. The most obvious application for the SN model is in scalable, high fidelity audio coding and signal modification.     

色噪声背景下正弦参量估计的互谱矩和SVD方法

互谱估计方法是用于色噪声背景下正弦信号参量估计的一个十分有效的方法。但以往互谱估计都是采用FFT方法和互周期图法。本文首次将现代谱估计方法引入到互谱估计中,从理论上建立了互相关函数的Yule-Walker方程,并在此基础上进而提出了互谱估计的矩估计法和SVD方法。给出了仿真实例结果。     

散射成象的似视原理

本文在严格的远区散射场的框架下,探讨了物象重建问题。指出在散射一逆散射问题中,只存在两维傳氏对偶空间。文中提出在单一波数,单一入射并且小成象角条件下成象的似视原理。揭示了在散射成象问题中存在着成象角与工作波长的互补关系。理论结果由计算机模拟得到验证。最后对用波的理论解释人的视觉机制作了尝试。     

CROSS-SPECTRAL MOMENT AND SVD METHODS ESTIMATING THE PARAMETERS OF CLOSE SINUSOIDS IN COLORED NOISE

The cross-spectral estimation methods are efficient in estimating the parameters of sinusoidal signals embedded in colored noise. But up to now, only FPT and cross-periodogram methods are used in this field, the modern auto-spectral estimation method is introduced into cross-spectral estimation in this paper, meanwhile the cross-correlation based Yule-Walker equation is proposed theoretically and the moment and singular-value decomposition (SVD)) algorithms for cross-spectral estimation have been developed. Finally, a numerical example is given for comparing the presented methods with the well-known Cadzow's SVD method.     

利用子波变换的图象压缩编码技术

从1988年Malial将子波交换用于信号处理提出多分辨率分析概念，给出了信号或图象分解为不同频率通道的算法和重构算法，开创了子波变换在图象处理中的应用．于波变换在时域和频域同时具有良好的局部化性能，使子波变换成为视频图象压缩编码的主要技术，专家预计在1996年将会高清晰度电视标准的子波变换视频图象压缩技术将推出问世．本文将综述子波变换图象压缩编码技术当前的进展。     

空间滤波预处理法及其在单次快摄阵处理中的应用

本文提出了一种基于空间滤波预处理的阵处理方法,旨在提高现有谱估计方法的分辨力。此法大大地提高了有效的信噪比(SNR)和自相关矩阵的估计精度。文中将此法应用于单次快摄的阵处理。模拟结果表明,用新方法可得到比用现有谱估计方法更好的结果。     

燃油喷雾内部构造可视化的激光CT技术(2)——对柴油机喷雾的应用

本文介绍了作者设计的燃油喷雾的激光CT测试系统,其中采用红宝石脉冲激光器作光源,CCD(Charge coupled device)摄象机作探测器,并配置了P540数字图象处理系统。利用红宝石脉冲激光和CCD的特性,实时地将燃油喷雾采集到数字图象处理系统上。用ART对喷雾图象进行重建,成功地实现了燃油喷雾内部构造的三维可视化。     

AAL层抗信元丢失技术

ＡＴＭ网络中传输视频信号存在的信元丢失会严重影响接收图象的质量，可以从视频编解码端与ＡＴＭ适配层采取抗信元丢失技术以减少该影响。不同ＡＡＬ有不同的抗信元丢失原理。本文分别对用ＡＡＬ１，ＡＡＬ２，ＡＡＬ５适配ＭＰＥＧ－２视频信号时在ＡＡＬ层采取的抗信元丢失技术进行了分析。     

A NEW METHOD OF DECONVOLUTION IN THE FREQUENCY DOMAIN

A new deconvolution method has been presented in this paper. Like conventional Discrete Fourier Transform(DFT) method, it is also a frequency domain method. Since there are only DFT, IDFT and multiplication calculations in the new method, the singular problem appeared in the conventional DFT method can be avoid, and the deconvolution problem can always be solved with the new method. The relationship between the presented method and conventional DFT method is discussed. A numerical example is also presented to illustrate the new method.