基于Turbo码的图像数字水印技术

在详细分析数字水印系统等效信道的基础上，提出了一种基于Turbo码的图像数字水印算法．该算法利用Turbo码的纠错性能和人眼视觉特性，以实现静止图像中数字水印的嵌入和提取；在水印嵌入之前，对水印序列进行Turbo码编码，然后利用人眼视觉特性进行水印嵌入，并采用了Turbo迭代译码算法进行水印提取，最后进行了数值仿真和算法比较．实验结果表明，该算法降低了水印在传输过程中的误码率，提高了水印的抗攻击能力，同时较好地解决了水印的鲁棒性和不可见性之间的矛盾．     

实用型模块化成像光谱仪多光谱图像的信噪比估算及压缩方法研究

采用局部标准差法和去相关法对实用型模块化成像光谱仪多光谱图像的信噪比进行估算。这两种方法已将地物变化的影响降低到很低的程度。这样在大气订正后，图像的信噪比性能充分反映出遥感仪器的信噪比性能。针对图像压缩，提出控制各波段恢复图像的峰值信噪比刚好大于原始图像的信噪比，使由压缩算法本身所带来的噪声限制在原始图像的噪声范围之内。结合这种压缩思想，用基于离散余弦变换和基于离散小波变换的压缩算法，对实用型模块化成像光谱仪多光谱图像进行压缩。实验表明，利用这种方法，对于高信噪比的波段，图像信息得到了保真；对低信噪比的波段，压缩倍数提高迅速且恢复图像视觉无失真，对整幅成像光谱图像，压缩性能提升显著——当压缩比等于37．95倍时，峰值信噪比等于45．86dB。     

干涉测量波面重构DCT算法研究

采用一种空间移相干涉仪测量凹形非球面反射镜光学表面,使用偏振元件和多幅图像同步采集实现移相,并对测量得到的干涉条纹采用基于离散余弦变换的相位解包裹算法,对不连续相位分布采用非加权的最小二乘法进行优化目标函数,最终重构出被测光学表面的面形.这种结构形式的干涉仪具有一定的抗振能力,同时在数字图像处理上优化了算法,能够快速稳定地得到被测面形,而且对硬件要求不高.结果表明:这种算法能够适用于非实验室条件下的光学测量,在一定干扰条件下可以达到较高的测量精度.     

基于整型可逆时域交叠变换的遥感图像压缩

提出了一种无乘法整型可逆时域交叠变换方法,并以此为核心变换技术设计了一种新的有损到无损渐进的图像压缩系统.利用所提出的压缩系统从一个单一的码流文件中既可以恢复出完全无损的图像,也可以在高压缩比下得到高质量的有损重构图像.算法通过前后向滤波器改进离散余弦变换性能,并在矩阵分解基础上通过多阶提升实现完全可逆整型变换.光学遥感图像的实验结果显示,该算法在绝大多数情况下可以达到优于图像压缩国际标准JPEG、JPEG2000以及新一代压缩算法HD-Photo的率失真性能以及高质量的主观视觉效果.     

影响MPEG-4图像恢复质量的参数研究

MPEG_4作为一种适合各种多媒体应用的图像压缩编码标准,已经得到广泛应用。介绍了I帧、P帧、B帧的运动图像编码。论述了影响图像恢复质量的几个因素,包括以下几个方面:DCT(Discrete cosine Transform)离散余弦变换;QP(Quantize Parameter)量化系数;SR(Search Range)运动估计中搜索范围以及半像素搜索(half_pixel searching)等。通过压缩比,峰值信噪比,编解码时间等大量实验数据比较各因素影响大小。实验证明量化系数对图形恢复质量影响最大,其余几个因素也有一定影响。     

适合光学图像数据压缩的图像对称化方法研究

研究了对图像进行对称化处理和频域规范化处理后信息熵和数据总量的变化,将需传输的数据总量作为衡量标准,找到了适合光学图像数据压缩的最佳对称化处理方法,理论推导和实验结果均表明,采用四个象限全对称处理后的图像在频域全为实数且数据量较小,具有与离散余弦变换相同的频域特性,从而得到了采用光学方法实现离散余弦变换及图像数据压缩的一种可行的方法。     

Discussion about the DCT/FFT phase-unwrapping algorithm for interferometric applications

After discussing Schofield's fast Fourier transform (FFT) algorithm, a (weighted) discrete-cosine-transform-based (DCT-based) phase-unwrapping (PU) algorithm is described. The algorithm could unwrap some high-phase-variation map which the FFT algorithm could not solve. Although the DCT algorithm is derived from Schofield's FFT PU algorithm, unlike FFT, it avoids the mirroring operation in implementing the boundary conditions so that it has higher efficiency in PU. Furthermore, in order to improve the reliability and noise robustness, we introduce the phase derivative variance (PDV) quality weight extracted from quality map into the DCT algorithm. The unwrapped results of both the computer-simulated and interferometric synthetic aperture radar (IFSAR) data demonstrate that the (weighted) DCT PU algorithm is generally effective, especially in dealing with the high-phase-variation and/or irregular-phase-distribution maps.     

Control of a twin clutch transmission for smooth gearshifts

The flow of torque in a twin clutch transmission is investigated and the different phases of torque transfer between the two clutches are studied. In order to prevent torque backlash and intense wear in dry clutch plates, a proper clamp force regulation is used. A full vehicle simulation that includes vehicle and powertrain components is set up. A Fuzzy logic control system is found suitable for clamp force and engine throttle controls. For upshift and downshift cases, the design of controllers for the gearshift process is carried out by defining proper membership functions and Fuzzy rules using Matlab/Simulink^TM software. The effectiveness of the control system is investigated by simulating two upshift and downshift cases. Results indicate that the control system is successful in regulating the clutch clamp forces and the engine throttle in such a way that a smooth torque flow in the transmission is achieved in all cases.     

A two-phase face recognition method in frequency domain

In this paper, we propose a two-phase face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectra are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. This method is as follows: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectra to test the effect on our algorithms. The experimental results show that our method outperforms the two-phase face recognition method based on space domain of face images.     

Supercharacters and the discrete Fourier,cosine, and sine transforms

Using supercharacter theory, we identify the matrices that are diagonalized by the discrete cosine and discrete sine transforms, respectively. Our method affords a combinatorial interpretation for the matrix entries.