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Journal ArticleDOI

A realization of 2-d adaptive filters using affine projection algorithm

TL;DR: In this paper, a realization of two-dimensional (2D) adaptive finite impulse response (FIR) filters using affine projection method is proposed, which is superior to that of the 2-D least mean square (LMS) and normalized LMS (NLMS) algorithms.
Abstract: This paper proposes a realization of two-dimensional (2-D) adaptive finite impulse response (FIR) filters using affine projection method. The convergence property of the proposed algorithm is superior to that of the 2-D least mean square (LMS) and normalized LMS (NLMS) algorithms. This algorithm has properties that lie between those of NLMS and recursive least squares (RLS) algorithms. The convergence of this filter can be proved. The generalization of the proposed algorithm is also discussed. To illustrate the utility of the proposed technique, this filter is applied to the 2-D system identification.
Citations
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Journal ArticleDOI
TL;DR: Seven proportionate normalized subband adaptive filter algorithms are established and are suitable for sparse system identification in network echo cancellation, and SM-SPU-IPNSAF algorithm, the concepts of SM and SPU are combined which leads to a reduction in computational complexity.
Abstract: In this paper, the concept of proportionate adaptation is extended to the normalized subband adaptive filter (NSAF), and seven proportionate normalized subband adaptive filter algorithms are established. The proposed algorithms are proportionate normalized subband adaptive filter (PNSAF), μ ‐law PNSAF (MPNSAF), improved PNSAF (IPNSAF), the improved IPNSAF (IIPNSAF), the set-membership IPNSAF (SM-IPNSAF), the selective partial update IPNSAF (SPU-IPNSAF), and SM-SPU-IPNSAF which are suitable for sparse system identification in network echo cancellation. When the impulse response of the echo path is sparse, the PNSAF has initial faster convergence than NSAF but slows down dramatically after initial convergence. The MPNSAF algorithm has fast convergence speed during the whole adaptation. The IPNSAF algorithm is suitable for both sparse and dispersive impulse responses. The SM-IPNSAF exhibits good performance with significant reduction in the overall computational complexity compared with the ordinary IPNSAF. In SPU-IPNSAF, the filter coefficients are partially updated rather than the entire filter at every adaptation. In SM-SPU-IPNSAF algorithm, the concepts of SM and SPU are combined which leads to a reduction in computational complexity. The simulation results show good performance of the proposed algorithms.

60 citations

Journal ArticleDOI
TL;DR: This paper proposes a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels and reveals the possibility of extending the ACSWF for real-time applications as well.
Abstract: Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well.

40 citations


Cites methods from "A realization of 2-d adaptive filte..."

  • ...The performance analysis of the proposed ACSWF is carried out by comparing it with basic 2-D adaptive filtering methods, such as 2-D-NLMS algorithm [19], [61] and 2-D-APA [19], [62]....

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Journal ArticleDOI
TL;DR: A novel 2D artificial bee colony (2D-ABC) adaptive filter algorithm was firstly proposed and it has a better performance than the other classical adaptive filter algorithms and its denoising efficiency is quite well on noisy images with different characteristics.

27 citations

Journal ArticleDOI
TL;DR: A novel 2-D Cuckoo search adaptive Wiener filtering algorithm (2D-CSAWF) is proposed for the denoising of satellite images contaminated with Gaussian noise and outperforms others both quantitatively and qualitatively.
Abstract: In the recent years, researchers are quite much attracted in designing two-dimensional (2-D) adaptive finite-impulse response (FIR) filters driven by an optimization algorithm to self-adjust the filter coefficients, with applications in different domains of research. For signal processing applications, FIR Wiener filters are commonly used for noisy signal restorations by computing the statistical estimates of the unknown signal. In this paper, a novel 2-D Cuckoo search adaptive Wiener filtering algorithm (2D-CSAWF) is proposed for the denoising of satellite images contaminated with Gaussian noise. Till date, study based on 2-D adaptive Wiener filtering driven by metaheuristic algorithms was not found in the literature to the best of our knowledge. Comparisons are made with the most studied and recent 2-D adaptive noise filtering algorithms, so as to analyze the performance and computational efficiency of the proposed algorithm. We have also included comparisons with recent adaptive metaheuristic algorithms used for satellite image denoising to ensure a fair comparison. All the algorithms are tested on the same satellite image dataset, for denoising images corrupted with three different Gaussian noise variance levels. The experimental results reveal that the proposed novel 2D-CSAWF algorithm outperforms others both quantitatively and qualitatively. Investigations were also carried out to examine the stability and computational efficiency of the proposed algorithm in denoising satellite images.

25 citations


Cites methods from "A realization of 2-d adaptive filte..."

  • ...Performance of the proposed 2D-CSAWF algorithm is compared with 2-D adaptive filtering techniques like 2D-LMS [17], [30], 2D-NLMS [30], [39], and 2D-APA [19], [30], since those are the most studied and compared algorithms in the literature....

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Journal ArticleDOI
TL;DR: The derivation of the proposed FAPA algorithm is based on the spatial shift-invariant properties of the 2-D discrete time signals, and has low computational complexity, comparable to that of the2-D LMS algorithm.

14 citations

References
More filters
Book
01 Jan 1986
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Abstract: Background and Overview. 1. Stochastic Processes and Models. 2. Wiener Filters. 3. Linear Prediction. 4. Method of Steepest Descent. 5. Least-Mean-Square Adaptive Filters. 6. Normalized Least-Mean-Square Adaptive Filters. 7. Transform-Domain and Sub-Band Adaptive Filters. 8. Method of Least Squares. 9. Recursive Least-Square Adaptive Filters. 10. Kalman Filters as the Unifying Bases for RLS Filters. 11. Square-Root Adaptive Filters. 12. Order-Recursive Adaptive Filters. 13. Finite-Precision Effects. 14. Tracking of Time-Varying Systems. 15. Adaptive Filters Using Infinite-Duration Impulse Response Structures. 16. Blind Deconvolution. 17. Back-Propagation Learning. Epilogue. Appendix A. Complex Variables. Appendix B. Differentiation with Respect to a Vector. Appendix C. Method of Lagrange Multipliers. Appendix D. Estimation Theory. Appendix E. Eigenanalysis. Appendix F. Rotations and Reflections. Appendix G. Complex Wishart Distribution. Glossary. Abbreviations. Principal Symbols. Bibliography. Index.

16,062 citations

Book
01 Jan 1985
TL;DR: This chapter discusses Adaptive Arrays and Adaptive Beamforming, as well as other Adaptive Algorithms and Structures, and discusses the Z-Transform in Adaptive Signal Processing.
Abstract: GENERAL INTRODUCTION. Adaptive Systems. The Adaptive Linear Combiner. THEORY OF ADAPTATION WITH STATIONARY SIGNALS. Properties of the Quadratic Performance Surface. Searching the Performance Surface. Gradient Estimation and Its Effects on Adaptation. ADAPTIVE ALGORITHMS AND STRUCTURES. The LMS Algorithm. The Z-Transform in Adaptive Signal Processing. Other Adaptive Algorithms and Structures. Adaptive Lattice Filters. APPLICATIONS. Adaptive Modeling and System Identification. Inverse Adaptive Modeling, Deconvolution, and Equalization. Adaptive Control Systems. Adaptive Interference Cancelling. Introduction to Adaptive Arrays and Adaptive Beamforming. Analysis of Adaptive Beamformers.

5,645 citations

Journal ArticleDOI
TL;DR: This paper presents a geometrical discussion as to the origin of that defect, and a new adaptive algorithm is proposed based on the result of the investigation, called APA (affine projection algorithm).
Abstract: The LMS algorithm and learning identification, which presently are typical adaptive algorithms, have a problem in that the speed of convergence may decrease greatly depending on the property of the input signal. To avoid this problem, this paper presents a geometrical discussion as to the origin of that defect, and proposes a new adaptive algorithm based on the result of the investigation. Comparing the convergence speeds of the proposed algorithm and the learning identification by numerical experiment by computer, great improvement was verified. The algorithm is extended to a group of algorithms which includes the original algorithm and the learning identification, which are called APA (affine projection algorithm). It is shown that APA has some desirable properties, such as, the coefficient vector approaches the true value monotonically and the convergence speed is independent of the amplitude of the input signal. Clear conclusions are also obtained for the problem as to what noise is included in the output signal when an external disturbance is impressed or the degree of the adaptive filter is not sufficient.

843 citations

Journal ArticleDOI
TL;DR: In this article, a two-dimensional least-mean-square (TDLMS) adaptive algorithm based on the method of steepest decent is proposed and applied to noise reduction in images.
Abstract: A two-dimensional least-mean-square (TDLMS) adaptive algorithm based on the method of steepest decent is proposed and applied to noise reduction in images. The adaptive property of the TDLMS algorithm enables the filter to have an improved tracking performance in nonstationary images. The results presented show that the TDLMS algorithm can be used successfully to reduce noise in images. The algorithm complexity is 2(N*N) multiplications and the same number of additions per image sample, where N is the parameter-matrix dimension. Analysis and convergence properties of the LMS algorithm in the one-dimensional case presented by other authors is shown to be applicable to this algorithm. The algorithm can be used in a number of two-dimensional applications such as image enhancement and image data processing. >

342 citations

Proceedings ArticleDOI
09 May 1995
TL;DR: This paper provides a fast projection algorithm and a step size control to obtain the same steady-state excess mean squared error (MSE) for various projection orders.
Abstract: Of the many adaptive filtering algorithms, the normalized LMS (NLMS) algorithm is generally used in practice because of its simplicity. The computational complexity of the NLMS algorithm is low, however, convergence is very slow and tracking is poor for a colored input signal such as speech. The projection algorithm was proposed as a generalization of the NLMS algorithm. This paper provides a fast projection algorithm and a step size control to obtain the same steady-state excess mean squared error (MSE) for various projection orders. Computer simulations for colored noise and speech input signal confirm the effectiveness of the projection algorithm and the step size control.

83 citations