Mohammad Naser Sabet Jahromi

Image equalization based on singular value decomposition

In this paper, a novel image equalization technique which is based on singular value decomposition (SVD) is proposed. The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed technique converts the image into the SVD domain and after normalizing the singular value matrix it reconstructs the image in the spatial domain by using the updated singular value matrix. The technique is called the singular value equalization (SVE) and compared with the standard grayscale histogram equalization (GHE) method. The visual and quantitative results suggest that the proposed SVE method clearly outperforms the GHE method.

Convergence analysis of the zero-attracting variable step-size LMS algorithm for sparse system identification

The variable step-size least-mean-square algorithm (VSSLMS) is an enhanced version of the least-mean-square algorithm (LMS) that aims at improving both convergence rate and mean-square error. The VSSLMS algorithm, just like other popular adaptive methods such as recursive least squares and Kalman filter, is not able to exploit the system sparsity. The zero-attracting variable step-size LMS (ZA-VSSLMS) algorithm was proposed to improve the performance of the variable step-size LMS (VSSLMS) algorithm for system identification when the system is sparse. It combines the

Mean-square deviation analysis of the zero-attracting variable step-size LMS algorithm

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Modified segment constrained vector quantizer design

ℓ1-norm penalty function with the original cost function of the VSSLMS to exploit the sparsity of the system. In this paper, we present the convergence and stability analysis of the ZA-VSSLMS algorithm. The performance of the ZA-VSSLMS is compared to those of the standard LMS, VSSLMS, and ZA-LMS algorithms in a sparse system identification setting.

Design of a sparse recursive inverse adaptive algorithm for system identification

In this paper, new adaptive algorithms are proposed to improve the performance of the variable step-size LMS (VSSLMS) algorithm when the system is sparse. The first proposed algorithm is the zero-attracting (ZA) VSSLMS. This algorithm outperforms the standard VSSLMS if the system is highly sparse. However, the performance of the ZA-VSSLMS algorithm deteriorates when the sparsity of the system decreases. To further improve the performance of the ZA-VSSLMS filter, the weighted zero-attracting (WZA)-VSSLMS algorithm is introduced. The algorithm performs the same or better than the ZA-VSSLMS if the system is highly sparse. On the other hand, when the sparsity of the system decreases, it performs better than the ZA-VSSLMS and better or the same as the standard VSSLMS algorithm. Also, both proposed algorithms have the same order of computational complexity as that of the VSSLMS algorithm (O(N)). For a system identification setting, the results indicate the high performance of the proposed algorithms in convergence speed and/or steady-state error under sparsity condition compared with the standard VSSLMS algorithm.

Image illumination enhancement with an objective no-reference measure of illumination assessment based on Gaussian distribution mapping

The well-known variable step-size least-mean-square (VSSLMS) algorithm provides faster convergence rate while maintaining lower mean-square error than the conventional LMS algorithm. The performance of the VSSLMS algorithm can be improved further in a channel estimation problem if the impulse response of the channel is sparse. Recently, a zero-attracting (ZA)-VSSLMS algorithm was proposed to exploit the sparsity of a channel. This was done by imposing an

OCCLUSION-AWARE PEDESTRIAN DETECTION

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