GSM Based Security Analysis for Add-SS Watermarking

Compressed Sensing (CS) theory has gained so much attention recently in the areas of signal processing. The sparsity of the transform coefficients has been widely employed in the early CS recovery techniques. However, except for the sparsity, there are other priors about transform coefficients such as the tree structure and the statistical dependencies that could be employed in CS reconstruction. In this paper, we propose to introduce the Gaussian Scale Mixtures (GSM) model into the tree structure based Orthogonal Matching Pursuit (TSOMP) reconstruction algorithm. This GSM model can efficiently denote the statistical dependencies between wavelet coefficients. And these statistical dependencies will improve the accuracy of the searching of the tree structure subspace in TSOMP algorithm. When both the inter-scale dependences (such as GSM model) of the coefficients and the intra-scale dependences (such as tree structure) of the coefficients are combined into the Orthogonal Matching Pursuit reconstruction algorithm, the noise and instability in TSOMP reconstruction are well reduced. Some state-of-the-art methods are compared with the proposed method. Experimental results show that the proposed method improves reconstruction accuracy for a given number of measurements and more image details are recovered.

This paper aims at developing mutual statistical descriptions of images in a visual sensor network. A two-dimensional Gaussian scale mixture (GSM) model is proposed to describe the joint distribution of wavelet coefficients in the same subband of two different images. This model is tested on two image databases, and it is shown that the proposed model always achieves a smaller divergence from the empirical distribution than a (jointly) Gaussian model. The GSM model is then extended to blocks of wavelet coefficients in the same subband and scale of non-overlapping images. A new denoising algorithm is devised that uses this model and achieves better performance than denoising based on marginal models.

In multi-slice magnetic resonance imaging (MRI), the resolution in the slice direction is usually reduced to allow faster acquisition times and to reduce the amount of noise in each 2D slice. To address this issue, a number of super resolution (SR) methods have been proposed to improve the resolution of 3D MRI volumes. Most of the methods involve the use of prior models of the MRI data as regularization terms in an ill-conditioned inverse problem. The use of user-defined parameters produces better results for these approaches but an inappropriate choice may reduce the overall performance of the algorithm. In this paper, we present a wavelet domain SR method which uses a Gaussian scale mixture (GSM) model in a sparseness constraint to regularize the ill-posed SR inverse problem. The proposed approach also makes use of an extension of the Dual Tree Complex Wavelet Transform to provide the ability to analyze the wavelet coefficients with sub-level precision. Our results show that the 3D MRI volumes reconstructed using this approach have quality superior to volumes produced by the best previously proposed approaches.

In this work, we propose a Bayesian structured sparse coding (BSSC) framework containing a nonlocal extension of Gaussian scale mixture (GSM) model by exploiting structured sparsity. It is shown that the variances of sparse coefficients (the field of Gaussian scalars) − if treated as a latent variable − can be jointly estimated along with the unknown sparse coefficients via the the method of alternative optimization. When applied to image restoration, BSSC leads to closed-form solutions involving iterative shrinkage/filtering and therefore admits computationally efficient implementation. Our experimental results have shown that BSSC-based image restoration often delivers reconstructed images with higher subjective/objective qualities than other competing approaches including IDD-BM3D and NCSR.

该文提出了一种新的结合非下采样Contourlet变换(NSCT)和自适应全变差模型的图像去噪方法。首先通过NSCT对含噪图像进行分解，根据高斯比例混合(GSM)模型建立图像模型；然后利用贝叶斯估计进行图像去噪，重构后得到初次去噪图像；最后，结合自适应全变差模型对初次去噪图像进行重构滤波，得到最终的去噪图像。实验结果表明，该方法可以有效地消除图像中的Gibbs伪影及噪声，在去噪图像峰值信噪比(PSNR)和边缘保持性能上都优于已有的算法。

In this paper we derive the Modified Cramér-Rao Bound (MCRB) for symbol timing, phase and frequency offset in presence of non-linear self-phase modulation (SPM) in a dispersion compensated long-haul coherent fiber link. We consider dual polarization multilevel quadrature amplitude modulation and multiple span of fiber each associated with optical amplifier. In presence of a realistic low pass filter (LPF) at the receiver front end, we find that SPM degrades the MCRB significantly. Moreover, SPM induces underdamped oscillation on the bounds. Simulation is carried out with self-generated asymmetric pulse to verify the theoretical results.

The conditional maximum likelihood (CML) principle, well known in the context of sensor array processing, is applied to the problem of timing recovery. A new self-noise free CML-based timing error detector is derived. Additionally, a new (conditional) Cramer-Rao bound (CRB) for timing estimation is obtained, which is more accurate than the extensively used modified CRB (MCRB).

Adaptivity is important in remote sensing image fusion because of the data-intensive and mass-driven processing platforms that call for reliable evaluation metrics to assess the runtime fusion procedure. Spatial distortion, including poor detail, visual disorder, or over-injection, has not been measured as effectively as in the spectral domain. A new metric, namely normalized mean potential energy (NMPE), is proposed in this paper to check the generalized spatial distortion of fused images by calculating the potential energy of marginal filtering distributions using information of high-order Markov random fields. NMPE is defined based on the GFoE model, which is a new high-order model that we built for remote sensing image applications. To incorporate the evaluation experience of human vision system into the GFoE model, the real zero-mean Gabor filters with multiple directions and scales are used as feature extractors, and the Gaussian scale mixture model as the expert function. The model parameters are trained from 200 images of the Berkeley segmentation dataset. Poor detail in a fused image tends to result in small NMPE evaluation with respect to small Gabor scales. Our observation shows that scale invariance exists only for “good” details, so we use large scales of Gabor functions to detect visual disorder. Over-injection is checked when the fused NMPE is much higher than 1. In the experimental procedure, satellite images from Quickbird, LandSat-7, and SPOT-5 were put to fusion with five popular methods to produce different images for visual and digital comparison. It can be concluded from the experiment that NMPE is in line with our subjective judgment to measure the pansharpening quality in terms of enhanced detail, visual distortion, and over-injection.

This paper proposes a digital watermarking algorithm of combining the chaos with the wavelet transform. Firstly, three-color separation is performed over dual carrier images; secondly, chaotic scrambling is carried out over RGB components of the watermark using different secret keys, followed by wavelet transform; then, corresponding components are embedded, allowing for improved watermark security, as different secret keys are used for different components; furthermore, according to the property of human vision system being variably sensitive to different regions of the wavelet transformed image, the watermark is embedded into medium and low frequency domain of wavelet transform, so as to balance transparency and robustness of the watermark. Experimental results show that this watermarking algorithm has kept image quality relatively well with high security, and exhibited good robustness to such attacks as Gaussian and compression etc.

Sparse acoustical holography from iterated Bayesian focusing

The authors investigated the performance of the iterative Steiglitz-McBride (SM) algorithm on an autoregressive moving average (ARMA) model of signals from a fast, sparsely sampled, multiecho, chemical shift imaging (CSI) acquisition using simulation, phantom, ex vivo, and in vivo experiments with a focus on its potential usage in magnetic resonance (MR)-guided interventions. The ARMA signal model facilitated a rapid calculation of the chemical shift, apparent spin-spin relaxation time (T2*), and complex amplitudes of a multipeak system from a limited number of echoes (< or equal 16). Numerical simulations of one- and two-peak systems were used to assess the accuracy and uncertainty in the calculated spectral parameters as a function of acquisition and tissue parameters. The measured uncertainties from simulation were compared to the theoretical Cramer-Rao lower bound (CRLB) for the acquisition. Measurements made in phantoms were used to validate the T2* estimates and to validate uncertainty estimates made from the CRLB. We demonstrated application to real-time MR-guided interventions ex vivo by using the technique to monitor a percutaneous ethanol injection into a bovine liver and in vivo to monitor a laser-induced thermal therapy treatment in a canine brain. Simulation results showed that the chemical shift and amplitude uncertainties reached their respective CRLB at a signal-to-noise ratio (SNR) > or =5 for echo train lengths (ETLs) > or =4 using a fixed echo spacing of 3.3 ms. T2* estimates from the signal model possessed higher uncertainties but reached the CRLB at larger SNRs and/or ETLs. Highly accurate estimates for the chemical shift (<0.01 ppm) and amplitude (<1.0%) were obtained with > or =4 echoes and for T2*(<1.0%) with > or =7 echoes. We conclude that, over a reasonable range of SNR, the SM algorithm is a robust estimator of spectral parameters from fast CSI acquisitions that acquire < or =16 echoes for one- and two-peak systems. Preliminary ex vivo and in vivo experiments corroborated the results from simulation experiments and further indicate the potential of this technique for MR-guided interventional procedures with high spatiotemporal resolution approximately 1.6 x 1.6 x 4 mm3 in < or =5 s.

A novel method for image thresholding using interval type-2 fuzzy set and Bat algorithm

Blind channel estimation in CDMA are studied in this paper. MSE (mean squared error) of parameter estimation is important for evaluating the estimation algorithm and its lower bound is CRB (Cramer Rao bound). In this paper, FIM and CRB for CDMA complex channel estimation are analyzed. Furthermore FIM and CRB for CDMA blind complex channel estimation under linear constraints is studied in complex Gaussian signal model. In the end, subspace based blind channel estimation in CDMA are simulated and compared with CRB under linear constraints.

We develop a secret key generation scheme using phase estimation in ultra-wideband (UWB) wireless fading channels. Based on the reciprocity theorem, two terminals extract the phase of the channel as a common random source to generate secret bits. Moreover, we study the secret key rate by a pair of nodes observing correlated sources and communicating to achieve secret key agreement over public communication channels. As our main results, we establish a more practical upper bound from Cramer-Rao bound (CRB) and compare it with a universally theoretical upper bound on the shared maximum key rate from mutual information of correlated random sources. Derivation and numerical examples are presented to demonstrate the bound. Simulation studies are also provided to validate feasibility and efficiency of the proposed scheme.

Recently watermarking algorithms of digital images based on singular value decomposition (SVD) have been proposed. Most SVD-based watermarking techniques use singular values as the embedding watermark information. These SVD-based techniques are advantageous for watermarking images since slight changes in the singular values do not significantly affect the image quality. However, it has been reported that these SVD-based watermarking algorithms, as implemented, suffer from a very high probability of false positive detections of watermarks. The false positive detection vulnerability of two recent SVD-based watermarking schemes is exposed. Two solutions are proposed to mitigate this vulnerability without changing the design principle of these watermarking algorithms.