In this paper, a comparative study of different wavelet filters using improved sub-band adaptive thresholding function for denoising of satellite images, based on evolutionary algorithms, has been performed. In this approach, the stochastic global optimisation techniques such as Cuckoo
Search (CS) algorithm, artificial bee colony (ABC) and particle swarm optimisation (PSO) are used for obtaining the parameters of adaptive thresholding function required for optimum performance. The visual and quantitative results clearly show the increased efficiency and flexibility of the
proposed CS algorithm based on Meyer wavelet filter over various other wavelet filters for image denoising. From the comparative study of different wavelet filters, it is found that the proposed Meyer wavelet-based CS algorithm denoising approach gives better performance in terms of signal-to-noise
ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE) and mean as compared to ABC- and PSO-based denoising approach. The proposed technique has been tested on several satellite images. The quantitative (EKI or EPI, mean, MSE, SNR and PSNR) and visual (denoised images) results
show the superiority of the proposed technique over conventional and state-of-art image denoising techniques.
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Cuckoo search algorithm;
artificial bee colony;
particle swarm optimisation;
satellite image denoising;
Document Type: Research Article
PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, 482005, Madhya Pradesh, India
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India
Publication date: March 3, 2016
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