M.F. Fahmy

B-spline wavelets for signal denoising and image compression

In this paper we propose to develop novel techniques for signal/image decomposition, and reconstruction based on the B-spline mathematical functions. Our proposed B-spline based multiscale/resolution representation is based upon a perfect reconstruction analysis/synthesis point of view. Our proposed B-spline analysis can be utilized for different signal/imaging applications such as compression, prediction, and denoising. We also present a straightforward computationally efficient approach for B-spline basis calculations that is based upon matrix multiplication and avoids any extra generated basis. Then we propose a novel technique for enhanced B-spline based compression for different image coders by preprocessing the image prior to the decomposition stage in any image coder. This would reduce the amount of data correlation and would allow for more compression, as will be shown with our correlation metric. Extensive simulations that have been carried on the well-known SPIHT image coder with and without the proposed correlation removal methodology are presented. Finally, we utilized our proposed B-spline basis for denoising and estimation applications. Illustrative results that demonstrate the efficiency of the proposed approaches are presented.

Design of selective linear phase bandpass switched-capacitor filters with equiripple passband amplitude response

A novel approach is presented to the design and synthesis of switched-capacitor (SC) filters. An efficient iterative algorithm is described for the construction of a class of selective linear-phase bandpass filter with equiripple passband amplitude response. The solution is obtained in terms of digital linear phase bandpass polynomials. A novel SC implementation is performed using an approach based on the reflection filter concept. The synthesis of the SC filter follows the steps used in microwave filter synthesis. The resulting SC circuits are completely insensitive to parasitic capacitances. Design tables and an illustrative example are also given. >

Blind source separation using time-frequency distribution

In this paper, a new approach based on the signals time-frequency characterization, is described for blind source separation. Only two types of TFD algorithms are considered in signal separation applications, namely the short time Fourier transform STFT and Wigner-Ville distribution WVD. The main features of this approach apart from being suitable for nonstationary signals lie in the fact that it works satisfactorily even when the time-frequency signatures of the sources, overlap, or when the mixing system is convolutional and no longer linear. Illustrative examples are given to show that the proposed approach can successfully separate mixed signals when other approaches fail.

A method for frequency-domain analysis of switched-capacitor filters

In this paper, a new method is proposed for the frequencyresponse evaluations of switched-capacitor networks of any topology. It relies on reducing the z -transform impulse response of the SCN into a rational function form. Closed-form expressions for the coefficients of the rational expansion, as well as its degree, are given. Illustrative examples showing the merits of this new approach over the existing one .are also included.

System identification using discrete orthogonal functions

In this paper a novel method is described for the design of adaptive IIR filters used in system identification. the adaptive filter is implemented as a parallel connection of subsections whose transfer functions constitute a set of discrete orthogonal systems. the adaptation algorithm used, which is of the Gauss-Newton type, adjusts the parameters of these discrete orthogonal systems in order to match the desired output data of the unknown plant in a least squares sense. Owing to the orthogonality property, which ensures complete independence of subsequent sections, convergence is very rapid. Closed-form expressions for the gradient signals required to update the filter are given. Illustrative examples have shown that this method always results in much improved adaptation properties compared with the already existing approaches.

A fingerprint segmentation technique based on Morphological processing

Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system (AFIS). It is used to separate a fingerprint area (foreground) from the image background. Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and discard many spurious minutiae. In this paper, a new segmentation algorithm is presented. Apart from its simplicity, it is characterized by being neither depend on empirical thresholds chosen by experts or a learned model trained by elements generated from manually segmented fingerprints. The algorithm uses the block range as a feature to achieve fingerprint segmentation. Then, some Morphological closing and opening operations are performed, to extract the foreground from the image. The performance of the proposed technique is checked by evaluating the classification error (Err). Experimental results have shown that when analyzing FVC2004, FVC2002, and FVC2000 databases using the proposed algorithm, the average classification error rates are much less than those obtained by other approaches. Several illustrative examples are given to verify this conclusion.

Digital bandpass filters with maximally flat amplitude and delay responses

An exact method is presented, for the construction of bandpass digital, (or microwave) filters with maximally flat delay at arbitrary specified frequency. It is based upon the solution of a second-order differential equation with eigenvalues. The case of filters with simultaneous flat amplitude and delay is also considered. Illustrative examples are also provided.

C12. Image compression using exponential B-spline functions

Exponential B-spline functions are more flexible than cardinal B-spline polynomials due to the extra degrees of freedom inherited by the arbitrary choice of its parameters. In this paper, independent simple proofs of some of the important features of Exponential B-spline functions are given. A novel efficient technique has also been proposed for decomposing a signal in terms of its exponential B-spline expansion. Applications of Exponential B-spline functions in spatial image compression are demonstrated. Our illustrative results show that Exponential B-splines outperform cardinal B-splines in image compression.

Exponential spline perfect reconstruction, decomposition and reconstruction with applications in compression and denoising

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade-off relationship. However, they have less impact with continuous-time applications as they are constructed from piecewise polynomials. On the other hand, exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper, we present a complete analysis for an E-spline-based subband coding (wavelet) perfect reconstruction (PR) system. Derivations for the scaling and wavelet functions are presented, along with application of the proposed system in image compression and image denoising. In image compression, a comparison of the proposed technique compared with the B-spline-based PR system as well as the basic wavelet subband system with the SPIHT image codec is presented. In image denoising, we report the enhancement achieved with the proposed E-spline-based denoising approach compared with B-spline-based denoising and another basic denoising technique. In both applications, E-splines show superior performance as will be illustrated.

A Fast Iterative Blind image restoration algorithm

Successful blind image deconvolution algorithms require the exact estimation of the Point Spread Function size, PSF. In the absence of any priori information about the imagery system and the true image, this estimation is normally done by trial and error experimentation, until an acceptable restored image quality is obtained. This paper, presents an exact estimation of the PSF size that yields the optimum restored image quality. The paper also describes a least squares PSF estimation, instead of the slow iterative update, that is commonly used in Iterative Blind Deconvolution software, IBD. Moreover, a technique is also proposed to improve the sharpness of the deconvolved images, by constrained maximization of the detail wavelet-coefficient entropies. Several simulations are given to verify these results.