Ali M. Reza

Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement

Acquired real-time image sequences, in their original form may not have good viewing quality due to lack of proper lighting or inherent noise. For example, in X-ray imaging, when continuous exposure is used to obtain an image sequence or video, usually low-level exposure is administered until the region of interest is identified. In this case, and many other similar situations, it is desired to improve the image quality in real-time. One particular method of interest, which extensively is used for enhancement of still images, is Contrast Limited Adaptive Histogram Equalization (CLAHE) proposed in [1] and summarized in [2]. This approach is computationally extensive and it is usually used for off-line image enhancement. Because of its performance, hardware implementation of this algorithm for enhancement of real-time image sequences is sought. In this paper, a system level realization of CLAHE is proposed, which is suitable for VLSI or FPGA implementation. The goal for this realization is to minimize the latency without sacrificing precision.

Spatially adaptive multiplicative noise image denoising technique

A new image denoising technique in the wavelet transform domain for multiplicative noise is presented. Unlike most existing techniques, this approach does not require prior modeling of either the image or the noise statistics. It uses the variance of the detail wavelet coefficients to decide whether to smooth or to preserve these coefficients. The approach takes advantage of wavelet transform property in generating three detail subimages each providing specific information with certain feature directivity. This allows the ability to combine information provided by different detail subimages to direct the filtering operation. The algorithm uses the hypothesis test based on the F-distribution to decide whether detail wavelet coefficients are due to image related features or they are due to noise. The effectiveness of the proposed technique is tested for orthogonal as well as biorthogonal mother wavelets in order to study the effect of the smoothing process under different wavelet types.

The Relevance Sample-Feature Machine: A Sparse Bayesian Learning Approach to Joint Feature-Sample Selection

This paper introduces a novel sparse Bayesian machine-learning algorithm for embedded feature selection in classification tasks. Our proposed algorithm, called the relevance sample feature machine (RSFM), is able to simultaneously choose the relevance samples and also the relevance features for regression or classification problems. We propose a separable model in feature and sample domains. Adopting a Bayesian approach and using Gaussian priors, the learned model by RSFM is sparse in both sample and feature domains. The proposed algorithm is an extension of the standard RVM algorithm, which only opts for sparsity in the sample domain. Experimental comparisons on synthetic as well as benchmark data sets show that RSFM is successful in both feature selection (eliminating the irrelevant features) and accurate classification. The main advantages of our proposed algorithm are: less system complexity, better generalization and avoiding overfitting, and less computational cost during the testing stage.

Automatic Earthquake Signal Onset Picking Based on the Continuous Wavelet Transform

This paper presents a method for automatic earthquake signal phase picking based on the continuous wavelet transform (CWT) of the seismogram, namely, the wavelet phase picker (WPP). A characteristic function is defined using the envelope function of CWT coefficients of the vertical component seismogram. The performance of the WPP method is evaluated on a database consisting of more than 460 local seismograms, and the P-phases detected by the algorithm are compared with those reported in the database. This evaluation confirms the high accuracy of automatically detected picks for the majority of seismograms. In addition, the results obtained by the algorithm are compared with a well-known method of P-phase picking referred to as the autoregressive Akaike information criteria. This comparison demonstrates the reliability of the proposed method.

Fuzzy cluster filter

A generalized nonlinear filter called the fuzzy cluster filter is introduced. This filter applies fuzzy clustering inside a running-window to estimate the clean output (i.e., geometrical center of the window). This filter is capable of cancelling the heavy-tailed contaminated Gaussian noise with a good performance. The results on some signals and images demonstrate the efficacy of this approach. The performance of the method is found by calculating the mean-square-error (MSE) for different signal-to-noise ratios using Monte Carlo simulations.

A new simultaneous estimation of directions of arrival and channel parameters in a multipath environment

We present two new blind techniques for simultaneous estimation of the direction-of-arrival (DOA) and the channel parameters for a uniform linear array in a multipath environment. The first method is based on the sum of weighted complex exponentials and Pade/spl acute/ approximation, and the second method determines the transfer function of a linear time-invariant system given its impulse response. For each method, we introduce a rational function whose complex poles contain the desired DOA, the magnitude of its residues are the channel gains, and the phase of its residues are in terms of the channel delays. We also derive and numerically evaluate the pertinent Crame/spl acute/r-Rao lower bound (CRLB) for the estimation process. Our error analysis based on a perturbation series formulation of the parameter estimation reveals that DOA and the channel parameters are extracted from a Gaussian signal-dependent noise. For both methods, the amount of computations are also determined. Some simulations are provided to assess the capabilities of the new methods.

FPGA implementation of adaptive temporal Kalman filter for real time video filtering

Filtering noise in real-time image sequences is required in some applications like medical imaging. The optimum approach in this case is in the form of adaptive 3-D spatial-temporal filter, which is generally very complex and prohibitive for real-time implementation. Independent processing of the image sequences, in spatial and temporal domains can resolve some of these implementation difficulties. Some of the existing spatial filters can easily be modified for real-time implementation. Adaptive temporal filters, however, are more involved. In this paper, an adaptive temporal filter is proposed that lend itself to hardware implementation for real-time temporal processing of image sequences. The proposed algorithm is based on adaptive Kalman filtering which is relatively simple and effective in its performance. Adaptation in this case is with respect to motion in the image sequence as well as variation of noise statistics. An efficient hardware implementation of this algorithm, based on FPGA technology, is proposed.

A DFT-based approximate eigenvalue and singular value decomposition of polynomial matrices

In this article, we address the problem of singular value decomposition of polynomial matrices and eigenvalue decomposition of para-Hermitian matrices. Discrete Fourier transform enables us to propose a new algorithm based on uniform sampling of polynomial matrices in frequency domain. This formulation of polynomial matrix decomposition allows for controlling spectral properties of the decomposition. We set up a nonlinear quadratic minimization for phase alignment of decomposition at each frequency sample, which leads to a compact order approximation of decomposed matrices. Compact order approximation of decomposed matrices makes it suitable in filterbank and multiple-input multiple-output (MIMO) precoding applications or any application dealing with realization of polynomial matrices as transfer function of MIMO systems. Numerical examples demonstrate the versatility of the proposed algorithm provided by relaxation of paraunitary constraint, and its configurability to select different properties.

FPGA implementation of a nonlinear two dimensional fuzzy filter

Nonlinear filtering has found many practical applications in digital signal and image processing. The computation complexity of these filtering algorithms make them difficult for real-time hardware implementation. One of these nonlinear filters, which is based on fuzzy classification of each pixel to subgroups of its neighboring pixels, is considered hen for hardware implementation. The criteria of this filter are based on the local context which form the basis of the fuzzy rule. The filtering algorithm is slightly modified for implementation into a Xilinx Virtex series of FPGA for real-time processing of image sequences. Implementation details and recommendations for further improvement are discussed. Result of a simulation example from the proposed hardware implementation is also presented.

Perceptually tuned JPEG coder for echocardiac image compression

In this work, we propose an efficient framework for compressing and displaying medical images. Image compression for medical applications, due to available Digital Imaging and Communications in Medicine requirements, is limited to the standard discrete cosine transform-based joint picture expert group. The objective of this work is to develop a set of quantization tables (Q tables) for compression of a specific class of medical image sequences, namely echocardiac. The main issue of concern is to achieve a Q table that matches the specific application and can linearly change the compression rate by adjusting the gain factor. This goal is achieved by considering the region of interest, optimum bit allocation, human visual system constraint, and optimum coding technique. These parameters are jointly optimized to design a Q table that works robustly for a category of medical images. Application of this approach to echocardiac images shows high subjective and quantitative performance. The proposed approach exhibits objectively a 2.16-dB improvement in the peak signal-to-noise ratio and subjectively 25% improvement over the most useable compression techniques.