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PSO-based learning of sub-band adaptive thresholding function for image denoising

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Abstract

In this paper, a new computationally efficient approach has been proposed for denoising the images which are corrupted by Gaussian noise. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) technique have been proposed for learning the parameters of adaptive thresholding function required for optimum performance. The proposed PSO-based denoising approach not only speeds up the optimization but also improves the performance in comparison with wavelet transform-based thresholding neural network (WT-TNN) approach. The results obtained shows better edge preservation performance with bior6.8 wavelet filter when compared to db8 wavelet filter. Further, problem of dependency of learning time on initial value of thresholding parameters and noise level in the image have been sorted out in the proposed approach.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India

    G. G. Bhutada, R. S. Anand & S. C. Saxena

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  1. G. G. Bhutada
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  2. R. S. Anand
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  3. S. C. Saxena
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Correspondence to G. G. Bhutada.

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Bhutada, G.G., Anand, R.S. & Saxena, S.C. PSO-based learning of sub-band adaptive thresholding function for image denoising. SIViP 6, 1–7 (2012). https://doi.org/10.1007/s11760-010-0167-7

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  • DOI: https://doi.org/10.1007/s11760-010-0167-7

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