E.I. Plotkin

Despeckling of medical ultrasound images using data and rate adaptive lossy compression

A novel technique for despeckling the medical ultrasound images using lossy compression is presented. The logarithm of the input image is first transformed to the multiscale wavelet domain. It is then shown that the subband coefficients of the log-transformed ultrasound image can be successfully modeled using the generalized Laplacian distribution. Based on this modeling, a simple adaptation of the zero-zone and reconstruction levels of the uniform threshold quantizer is proposed in order to achieve simultaneous despeckling and quantization. This adaptation is based on: 1) an estimate of the corrupting speckle noise level in the image; 2) the estimated statistics of the noise-free subband coefficients; and 3) the required compression rate. The Laplacian distribution is considered as a special case of the generalized Laplacian distribution and its efficacy is demonstrated for the problem under consideration. Context-based classification is also applied to the noisy coefficients to enhance the performance of the subband coder. Simulation results using a contrast detail phantom image and several real ultrasound images are presented. To validate the performance of the proposed scheme, comparison with two two-stage schemes, wherein the speckled image is first filtered and then compressed using the state-of-the-art JPEG2000 encoder, is presented. Experimental results show that the proposed scheme works better, both in terms of the signal to noise ratio and the visual quality.

A modified PCA algorithm for face recognition

In principal component analysis (PCA) algorithm for face recognition, the eigenvectors associated with the large eigenvalues are empirically regarded as representing the changes in the illumination; hence, when we extract the feature vector, the influence of the large eigenvectors should be reduced. In this paper, we propose a modified principal component analysis (MPCA) algorithm for face recognition, and this method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify our method and compare it with the commonly used algorithms, namely, PCA and linear discriminant analysis (LDA). The simulation results show that the proposed method results in a better performance than the conventional PCA and LDA approaches, and the computation at cost remains the same as that of the PCA, and much less than that of the LDA.

Synthesis of discrete time-varying null filters for frequency-varying signals using the time-warping technique

A synthesis of two second-order discrete time-varying filters (DTVF), capable of suppressing a sine signal with arbitrary frequency variation, is presented. These structures are obtained by using the time-warping concept. A noise analysis is carried out for these two DTVFs and their performance is compared. It is shown that for the particular application considered, the rule of time-dependence of the DTVF (the synchronization) may be effectively obtained by using a phase-locked loop (PLL) system. As an example, the DTVF-PLL system is used for synchronous phase estimation of a sine wave corrupted by closely spaced FM interference. Simulation results are provided to substantiate the analysis and demonstrate the efficiency of the proposed time-varying system. The main feature of the TLM method is its formulation and programming simplicity. The method also generates a large amount of information; not only is the impulse response of a structure obtained, yielding in turn its response to any excitation, but the characteristics of the dominant and higher order modes are also accessible in the frequency domain through the Fourier transform. >

A nonlinear adaptive filter for narrowband interference mitigation in spread spectrum systems

This paper presents a novel approach based on the estimation of the jammer instantaneous frequency for the excision of narrowband interferences in spread spectrum systems. Using a three-coefficient finite impulse response (FIR) model for the interference, an augmented state-space representation of the received signal (spread spectrum signal+ noise + interference signal) is developed. Based on the state-space representation developed, a Kalman-type nonlinear adaptive filter, namely, augmented-state approximate-conditional-mean (ASACM) filter, is formulated to estimate the unknown jammer instantaneous frequency from the received signal. The receiver performance and the problem of stability of the proposed filter are addressed. The improvement in the performance achieved with the proposed nonlinear Kalman-type filter is quantified in comparison with the linear one based on three-coefficient FIR excision filter, and an adaptive nonlinear prediction filter. The efficacy of the proposed filter is corroborated with simulation examples for interference suppression in spread spectrum systems. Simulation results show that the proposed filter is effective in suppressing powerful narrowband interference in spread spectrum systems with good receiver output SNRs.

Low-complexity video noise reduction in wavelet domain

This paper proposes a novel spatio-temporal filter for video denoising that operates entirely in the wavelet domain and is based on temporal decorrelation. For effective noise reduction, the spatial and the temporal redundancies, which exist in the wavelet domain representation of a video signal, are exploited. Using simple and closed form expressions, the temporal information in the wavelet domain is first decorrelated in order to minimize the redundancy. The decorrelated noise-free coefficients are then modeled using a generalized Gaussian prior. For spatial filtering of the noisy wavelet coefficients, a new, low-complexity wavelet shrinkage method, which utilizes the correlation that exists between subsequent resolution levels, is proposed. Experimental results show that the proposed scheme outperforms state-of-the-art spatio-temporal filters in time and wavelet domains, both in terms of PSNR and visual quality.

Statistically optimal null filter based on instantaneous matched processing

A novel approach is proposed for solving the problem of enhancement/suppression of narrowband signals of short-record length based on combining the maximum signal-to-noise ratio (SNRo) and the least-squares (LS) optimization criteria. This two-fold optimization is implemented by scaling the output of an instantaneous matched filter used for the maximization of theSNRo, over a variable-time observation interval, with the locally generated function λ(t) whose gain is optimized through the LS procedure. The intrinsic property of the proposed statistically optimal null filter (SONF) is its ability to track rapidly, leading to a more practical processing of short duration signals (transients). The theoretical analysis and simulation studies show that the SONF, based on this proposed two-fold optimization procedure, is closely related to the Kalman filter. On the other hand, the design of the SONF does not require the solution of a nonlinear equation of the Ricatti type that is necessary in finding the gains of the Kalman filter. Consequently, the proposed algorithm may be considered as an alternate approach to Kalman filtering. The paper also presents some simulation results illustrating the application of the proposed SONF.

Automatic modulation type recognition

Identification of the modulation type of a received signal is a problem encountered in radio spectrum surveillance and control. It is attractive to design methods that use only one or two time-domain classification parameters, in order to minimize the computational complexity. A number of new classification parameters are proposed and studied in this paper. They are compared to an existing one-parameter method. Also, a new method of envelope extraction, which does not require Hilbert transform computation, is proposed. The proposed methods achieve better recognition rates at short data records, e.g. 89% vs. 82% at 10 dB, 1024 samples, and 95% for a combination of two parameters.

A novel iterative method for the reconstruction of signals from nonuniformly spaced samples

Abstract In this paper we address the problem of reconstruction of signals from their nonequally spaced samples. Exploiting the close-to-band structure of the composing matrix, a two-stage procedure for the recovery of uniform samples from nonuniform samples has been suggested by Plotkin and Swamy (1987). In order to reduce the computational complexity, a special procedure of partitioning the composing matrix into a set of overlapping submatrices was used. Then the error in the estimate was reduced by applying an iterative procedure. The present paper is an extension of results presented by Plotkin and Swamy (1987). In this we propose a modification to their procedure, so as to recover an equal number of uniformly spaced samples as are those in the nonuniform set. We show that the iterative algorithm converges conditionally and the conditions are weak and may be implemented easily. Computer simulation results have been presented which show that the proposed technique performs well even for deviations of nonuniform sample positions well beyond the corresponding uniform positions. The proposed method is attractive from computational point of view also.

Bayesian algorithm for video noise reduction in the wavelet domain

The paper proposes a Bayesian algorithm for the reduction of additive video noise in the wavelet domain. Spatial and temporal redundancies that exist in a video sequence in the time domain also persist in the wavelet domain. This allows video motion to be captured in the wavelet domain. Based on this fact, a new statistical model is proposed for video sequences. We not only model the subband coefficients in individual frames, but also the wavelet coefficient difference occurring between two consecutive frames using the generalized Laplacian distribution. Following this model, a Bayesian processor is developed that estimates the noise-free wavelet coefficients in the current frame, conditioned on the noisy coefficients in the current frame and the filtered coefficients in the past frame. Rigorous experimental results show that the proposed scheme outperforms several state-of-the-art spatio-temporal filters in time and wavelet domains in terms of quantitative performance as well as visual quality.

Signal processing based on parameter structural modeling and separation of highly correlated signals of known structure

Results in the study of signal processing based on the use of parameter structural modeling (PSM) are presented. First, we introduce a special form of time-series modeling based on signal-dependent building blocks. Such modeling is used in the design of a nestedform transversal structure, known as a composite filter, based on a shift-invariant finite impulse resonse (FIR) as well as infinite impulse response (IIR) building blocks. The newly proposed composite PSM model (CPSM) possesses a unique feature, namely, its ability to suppress one signal of a given structure, while at the same time being ideally transparent to another one. The intrinsic property of this proposed CPSM is its enhanced insensitivity with respect to noise as well as its ability to fast track, in contrast to the commonly used linear line-enhancer based on conventional autoregressive moving average (ARMA), thus leading to a more practically sound processing of short-duration signals. It is shown that the proposed time-series modeling based on CPSM can be effectively applied towards the separation of superimposed signals of heavily overlapping spectra. Next, the parameter-invariant nonlinear structural signal representation based on shift-invariant CPSM is presented. The use of this model in the design of annihilation operators (AO) is described, and composite parameter-free structural modeling (CPFSM) is developed. Based on this model, two canonical forms of the parameter-invariant null filters (PINF) are presented, and their use in the suppression of a given class of signals, independently of the values of theira priori unknown parameters, is illustrated. The paper also presents some simulation examples illustrating the application of the proposed CPSM and CPFSM in solving problems of detection and parameter estimation in the presence of highly non-Gaussian, mainly signal-like interferences.