Encoding true-color images with a limited palette via soft vector clustering as an instance of dithering multidimensional signals

One of the classic problems of digital image processing is to encode true-color images for the optimal viewing on displays with a limited set of colors. A major manifestation of optimal viewing in this regard is to maximally remove parasitic artifacts in the degraded encoded images such as the contouring effect. Several robust attempts have been made to solve this problem over the past 50 years, and the first contribution of this paper is to introduce a simple – yet effective – novel solution that is based on soft vector clustering.The other contribution of this paper is to propose the application of the soft clustering methodology deployed in our color-encoding solution for the dithering of multidimensional signals. Dithering essentially adds controlled noise to the analog signal upon its digitization so that the resulting quantization noise is dispersed over a much wider band of the frequency domain and is therefore less perceptible in the digitized signal. This comes of course at the price of more overall quantization noise. Dithering is a vital operation that is performed via well-known simple schemes upon the analog-to-digital conversion of one-dimensional signals; however, the published literature is still missing a general neat scheme for the dithering of multidimensional signals that is able to handle arbitrary dimensionality, arbitrary number and distribution of quantization centroids, and with computable and controllable noise power. This gap is also filled by this paper.     

Enlargement or reduction of digital images with minimum loss of information.

The purpose of this paper is to derive optimal spline algorithms for the enlargement or reduction of digital images by arbitrary (noninteger) scaling factors. In our formulation, the original and rescaled signals are each represented by an interpolating polynomial spline of degree n with step size one and Delta, respectively. The change of scale is achieved by determining the spline with step size Delta that provides the closest approximation of the original signal in the L(2)-norm. We show that this approximation can be computed in three steps: (i) a digital prefilter that provides the B-spline coefficients of the input signal, (ii) a resampling using an expansion formula with a modified sampling kernel that depends explicitly on Delta, and (iii) a digital postfilter that maps the result back into the signal domain. We provide explicit formulas for n=0, 1, and 3 and propose solutions for the efficient implementation of these algorithms. We consider image processing examples and show that the present method compares favorably with standard interpolation techniques. Finally, we discuss some properties of this approach and its connection with the classical technique of bandlimiting a signal, which provides the asymptotic limit of our algorithm as the order of the spline tends to infinity.     

基于有界成分分析的带内全双工数字自干扰抵消

凭借能够提升频谱利用率的优势,带内全双工(In-Band Full Duplex, IBFD)技术有望成为现代无线通信系统的潜在方案。然而,在应用过程中却面临自干扰抵消(Self-Interference Cancellation, SIC)的巨大挑战。SIC可以从空域、模拟域和数字域3个方面来单独或组合实现。该文重点研究了IBFD数字SIC。针对传统数字SIC性能受到收发链路器件非理想因素限制的问题,该文建立了一种射频辅助链路的IBFD系统,利用有用信号和自干扰信号的有界性,设计了一种基于有界成分分析的数字SIC方法。在视距(Line Of Sight, LOS)和非视距(Non-Line Of Sight, NLOS)两种信道场景下,利用仿真和实测数据进行了验证分析。结果表明,相比较于最小二乘方法和独立成分分析方法,所提有界成分分析方法改善了SIC效果,并提高了系统误码率性能。     

基于压缩感知的数字图像水印算法

压缩感知理论作为新一代信息处理理论基础,在很多领域得到了应用,但在数字水印方面的应用还有待发展。利用压缩感知理论,在图像稀疏化后的观测域中实现水印嵌入过程。用小波变换将原图像稀疏化,选择一个固定高斯随机矩阵对其进行观测,在观测压缩域中嵌入与提取水印,用匹配追踪算法恢复稀疏信号。实验结果表明,该算法满足透明性、鲁棒性和安全性要求;同时抗提取实验表明,该算法能有效抵御非法提取水印信息,增加了安全性。     

信号压缩与重构的交替方向外点持续法

针对压缩感知（Compressed Sensing,CS）中信号重构的l1-正则化问题中的l1-正则项非光滑,求解比较困难,提出了交替方向外点持续法（Alternating Direction Exterior Point Continuation Method,ADEPCM）.该算法首先将信号的稀疏域的l1-正则化问题通过变量分裂（Variable Splitting,VS）技术转化为与之等价的约束优化问题;然后采用一步Gauss-Seidel思想,对优化问题中的变量最小化,并采用持续的思想更新罚参数,重构出信号的稀疏系数;最后进行正交反变换,重构出原始信号.并将ADEPCM用于图像重构,进行了仿真实验及对实验结果进行了分析.实验结果表明：与现有的一些重构算法相比,ADEPCM具有稍高的峰值信噪比（Peak Signal to Noise Ratio,PSNR）和更快速的收敛速度.     

A secure and improved self-embedding algorithm to combat digital document forgery

The recent digital revolution has facilitated communication, data portability and on-the-fly manipulation. Unfortunately, this has brought along some critical security vulnerabilities that put digital documents at risk. The problem is in the security mechanism adopted to secure these documents by means of encrypted passwords; however, this security shield does not actually protect the documents which are stored intact. We propose here a solution to this real world problem through a 1D hash algorithm coupled with 2D iFFT (irreversible Fast Fourier Transform) to encrypt digital documents in the 2D spatial domain. Further by applying an imperceptible information hiding technique we can add another security layer which is resistant to noise and to a certain extent JPEG compression. We support this assertion by showing a practical example which is drawn from our set of experiments. This work exploits Jarvis’ kernel to generate the error diffusion signal and the Wavelet-based Inverse Halftoning via De-convolution (WInHD) to recover the approximation of the original signal. Our method not only points out forgery but also allows legal or forensics expert gain access to the original document despite being manipulated. This would undoubtedly be very useful in cases of disputes or claims.     

L/M-fold image resizing in block-DCT domain using symmetric convolution

Image resizing is to change an image size by upsampling or downsampling of a digital image. Most still images and video frames on digital media are given in a compressed domain. Image resizing of a compressed image can be performed in the spatial domain via decompression and recompression. In general, resizing of a compressed image in a compressed domain is much faster than that in the spatial domain. We propose a novel approach to resize images with L/M resizing ratio in the discrete cosine transform (DCT) domain, which exploits the multiplication-convolution property of DCT (multiplication in the spatial domain corresponds to symmetric convolution in the DCT domain). When an image is given in terms of its 8/spl times/8 block-DCT coefficients, its resized image is also obtained in 8/spl times/8 block-DCT coefficients. The proposed approach is computationally fast and produces visually fine images with high PSNR.     

Digital watermarking for telltale tamper proofing andauthentication

In this paper, we consider the problem of digital watermarking to ensure the credibility of multimedia. We specifically address the problem of fragile digital watermarking for the tamper proofing of still images. Applications of our problem include authentication for courtroom evidence, insurance claims, and journalistic photography. We present a novel fragile watermarking approach which embeds a watermark in the discrete wavelet domain of the image by quantizing the corresponding coefficients. Tamper detection is possible in localized spatial and frequency regions. Unlike previously proposed techniques, this novel approach provides information on specific frequencies of the image that have been modified. This allows the user to make application-dependent decisions concerning whether an image, which is JPEG compressed for instance, still has credibility. Analysis is provided to evaluate the performance of the technique to varying system parameters. In addition, we compare the performance of the proposed method to existing fragile watermarking techniques to demonstrate the success and potential of the method for practical multimedia tamper proofing and authentication     

Generalized compressive detection of stochastic signals using Neyman–Pearson theorem

Compressive sensing (CS) enables reconstructing a sparse signal from fewer samples than those required by the classic Nyquist sampling theorem. In general, CS signal recovery algorithms have high computational complexity. However, several signal processing problems such as signal detection and classification can be tackled directly in the compressive measurement domain. This makes recovering the original signal from its compressive measurements not necessary in these applications. We consider in this paper detecting stochastic signals with known probability density function from their compressive measurements. We refer to it as the compressive detection problem to highlight that the detection task can be achieved via directly exploring the compressive measurements. The Neyman–Pearson (NP) theorem is applied to derive the NP detectors for Gaussian and non-Gaussian signals. Our work is more general over many existing literature in the sense that we do not require the orthonormality of the measurement matrix, and the compressive detection problem for stochastic signals is generalized from the case of Gaussian signals to the case of non-Gaussian signals. Theoretical performance results of the proposed NP detectors in terms of their detection probability and the false alarm rate averaged over the random measurement matrix are established. They are verified via extensive computer simulations.     

基于分段压缩和原子范数的跳频信号参数估计

针对压缩域跳频信号参数估计方法需借助测量矩阵寻找压缩采样数据的数字特征,造成运算复杂度高,且存在基不匹配的问题,提出一种压缩域数字特征和原子范数的跳频信号参数估计方法。建立块对角化的测量矩阵,实现信号分段压缩,分析压缩采样数据的数字特征,实现跳变时刻粗估计;分离出未发生频率跳变的信号段,利用原子范数最小化方法实现跳变频率的精确估计;最后依据精确估计的跳变频率,设计原子字典,并在压缩域实现跳变时刻的精确估计。基于该算法的跳变频率估计性能高于基于压缩感知的跳变频率估计,亦能精确估计跳频信号的跳变时刻。仿真结果显示,在信噪比高于-2 dB,压缩比高于0.5时,基于该算法的归一化跳变频率估计误差低于10-4,归一化跳变时刻估计误差低于10-2。     

基于ATmega16的电液伺服阀反馈控制器的设计

针对电液伺服阀在实际工程应用中出现输出压力不稳定、输出压力偏高或偏低的问题,设计了基于ATmega16平台的数字式液压伺服阀反馈控制器,采集伺服阀输出压力作为反馈信号构成闭环控制系统,采用经典的增量式数字PID控制算法进行压力调节。实际应用结果表明该数字式电液伺服阀反馈控制系统响应快速、输出稳定、输出压力精度高。     

可用于印刷品防伪的数字水印算法

文献已报道多种可用于印刷品防伪的数字水印算法。由于打印/扫描过程的特殊性,使得其中有些算法的水印检测效果达不到实用要求。文章在研究打印/扫描对数字图像所引起的失真的规律基础上,提出了一种在离散傅里叶变换(DFT)域实现的、可用于印刷品防伪的数字水印嵌入算法。实验结果表明:不需原始图像,即可检测出用此算法嵌入印刷图像中的不可见标记。     

一种用于条带式SAR的自聚焦算法

本文将相位梯度法（Phase Gradient Algorithm,PGA）用于条带式合成孔径雷达（SAR）的自聚焦。根据条带SAR与聚束SAR回波信号差异并参考经典相位梯度法提出了一种适用于条带SAR的自聚焦算法SPGA。用这种新的条带SAR自聚焦算法聚焦所得SAR图像不会发生传统条带SAR下PGA常有的拼接问题：同时由于相位误差曲面的引入,对于任何场景SPGA均能达到良好的聚焦效果。     

基于FrFT域滤波的灵巧噪声干扰抑制方法

基于数字射频存储器(DRFM)的灵巧噪声干扰兼具压制式干扰效果和欺骗式干扰效果,难以被传统抗干扰方法有效抑制。针对这一问题,本文结合分数阶傅里叶变换(FrFT)与分数阶域滤波方法,提出了一种线性调频脉冲压缩雷达体制下的灵巧噪声干扰抑制方法。首先对接收信号进行分数阶傅里叶变换,随后根据发射信号在分数阶域的特征参数,设计分数阶域滤波器,对接收信号进行分数阶域滤波以滤除干扰,最后进行分数阶反变换还原目标信号。仿真结果表明,该方法能够对卷积噪声干扰与乘积噪声干扰两种典型的灵巧噪声干扰进行有效抑制,准确检测目标的位置。      

Reconstruction of nonuniformly sampled images in spline spaces.

This paper presents a novel approach to the reconstruction of images from nonuniformly spaced samples. This problem is often encountered in digital image processing applications. Nonrecursive video coding with motion compensation, spatiotemporal interpolation of video sequences, and generation of new views in multicamera systems are three possible applications. We propose a new reconstruction algorithm based on a spline model for images. We use regularization, since this is an ill-posed inverse problem. We minimize a cost function composed of two terms: one related to the approximation error and the other related to the smoothness of the modeling function. All the processing is carried out in the space of spline coefficients; this space is discrete, although the problem itself is of a continuous nature. The coefficients of regularization and approximation filters are computed exactly by using the explicit expressions of B-spline functions in the time domain. The regularization is carried out locally, while the computation of the regularization factor accounts for the structure of the nonuniform sampling grid. The linear system of equations obtained is solved iteratively. Our results show a very good performance in motion-compensated interpolation applications.     

一种新的图像水印方案

目标区域是图像中视觉上最重要的部分。针对现有的水印算法很少考虑图像中确定的目标区域这一问题，提出了一种基于目标区域的水印方案(WIOR)。方案首先利用小波变换分析图像的目标特征，并依据图像的小波高频系数特征对其进行聚类，获得水印的嵌入域，在此嵌入域中，选择小波系数并对其进行量化调制来嵌入二值扩谱水印；同时，通过只在图像的局部区域内嵌入较少的水印信息来改善水印的隐蔽性。实验结果表明，该方案对于一类具有突出目标特征的图像具有较好的效果。嵌入的水印主要优势在于真正把水印嵌入图像的视觉重要部分，从而能有效地抵御剪切攻击，而且可以实现盲检测。     

Recent progress on high-speed optical transmission

The recently reported high spectral efficiency (SE) and high-baud-rate signal transmission are all based on digital coherent optical communications and digital signal processing (DSP). DSP simplifies the reception of advanced modulation formats and also enables the major electrical and optical impairments to be processed and compensated in the digital domain, at the transmitter or receiver side. In this paper, we summarize the research progress on high-speed signal generation and detection and also show the progress on DSP for high-speed signal detection. We also report the latest progress on multi-core and multi-mode multiplexing.     

基于图形小波变换的遥感图像表示与去噪

离散小波变换(Discrete Wavelet Transform,DWT)通常用于图像的表示。然而,对于具有不规则形状边缘的图像,尤其是对于纹理和细节信息较多的遥感图像,DWT却很难有效表示,进而影响后续去噪效果。针对该问题,提出了一种基于图形小波变换(Graphic Wavelet Transform,GWT)的图像去噪方法。首先,将图像表示为图形信号,并通过该图形信号的谱表示构造相应的变换矩阵;然后,设计了一种改进自适应阈值的图像去噪方法,在GWT变换域内对图像去噪。实验结果表明,与常用的图像去噪方法相比,所提算法能够提供更好的图像主观质量。采用均方根误差(Root Mean Square Error,RMSE)和峰值信噪比(Peak Signal-to-Noise Ratio,PSNR)作为客观指标,结果表明,采用所提方法得到的重建图像客观质量更优。     

内嵌于DDS的DAC线性参数测试

文章重点介绍了内嵌于DDS的DAC线性参数的一种测试方法。该测试方法的优点是开发周期短、测试成本低、可在多种测试机台上实现。解决了DDS中不能对DAC输入端直接操作的难题,同时不影响其他参数的测试。文章基于LabView软件,使用示波器或NI板卡,结合其他仪器对DAC的静态参数进行了研究并试验。其中示波器或NI板卡完成信号采集,其他仪器完成芯片配置,微机对整个流程进行控制并将测试结果按序输出,实现测试自动化。通过试验证明该方法可行,测试效率高,测试结果达标并稳定。     

Maximum-likelihood estimation of circle parameters via convolution.

The accurate fitting of a circle to noisy measurements of circumferential points is a much studied problem in the literature. In this paper, we present an interpretation of the maximum-likelihood estimator (MLE) and the Delogne-K?sa estimator (DKE) for circle-center and radius estimation in terms of convolution on an image which is ideal in a certain sense. We use our convolution-based MLE approach to find good estimates for the parameters of a circle in digital images. In digital images, it is then possible to treat these estimates as preliminary estimates into various other numerical techniques which further refine them to achieve subpixel accuracy. We also investigate the relationship between the convolution of an ideal image with a "phase-coded kernel" (PCK) and the MLE. This is related to the "phase-coded annulus" which was introduced by Atherton and Kerbyson who proposed it as one of a number of new convolution kernels for estimating circle center and radius. We show that the PCK is an approximate MLE (AMLE). We compare our AMLE method to the MLE and the DKE as well as the Cramér-Rao Lower Bound in ideal images and in both real and synthetic digital images.