Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 10, Issue 12, Pages 5245-5257Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2017.2755068
Keywords
Adaptive filter algorithm; cuckoo search (CS) algorithm; metaheuristic optimization algorithms; satellite image denoising; two-dimensional finite-impulse response (2-D FIR) Wiener filter
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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 2D-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.
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