期刊
IET SIGNAL PROCESSING
卷 7, 期 8, 页码 720-730出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-spr.2013.0139
关键词
evolutionary computation; image denoising; particle swarm optimisation; remote sensing; improved subband adaptive thresholding function; satellite image denoising; evolutionary algorithm; stochastic global optimisation technique; Cuckoo search algorithm; artificial bee colony technique; particle swarm optimisation; CS algorithm; ABC technique; PSO technique; edge preservation index; edge keeping index; peak signal-to-noise ratio; signal-to-noise ratio
In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the CS algorithm and ABC algorithm-based denoising approach give better performance in terms of edge preservation index or edge keeping index (EPI or EKI) peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) as compared to PSO-based denoising approach. The proposed technique has been tested on satellite images. The quantitative (EPI, PSNR and SNR) and visual (denoised images) results show superiority of the proposed technique over conventional and state-of-the-art image denoising techniques.
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