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Adaptive image denoising using cuckoo algorithm

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Abstract

This paper presents a novel denoising approach based on smoothing linear and nonlinear filters combined with an optimization algorithm. The optimization algorithm used was cuckoo search algorithm and is employed to determine the optimal sequence of filters for each kind of noise. Noises that would be eliminated form images using the proposed approach including Gaussian, speckle, and salt and pepper noise. The denoising behaviour of nonlinear filters and wavelet shrinkage threshold methods have also been analysed and compared with the proposed approach. Results show the robustness of the proposed filter when compared with the state-of-the-art methods in terms of peak signal-to-noise ratio and image quality index. Furthermore, a comparative analysis is provided between the said optimization algorithm and the genetic algorithm.

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Abbreviations

AI:

Artificial intelligence

CSA:

Cuckoo search algorithm

db4:

Daubechies-4 wavelet

IQI:

Image quality index

GA:

Genetic algorithms

PSO:

Particle swarm optimization

PSNR:

Peak signal-to-noise ratio

SNR:

Signal-to-noise ratio

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Authors and Affiliations

  1. Comsats Institute of Information Technology, Islamabad, Pakistan

    Memoona Malik & Sajjad Mohsin

  2. PMAS-UIIT, Rawalpindi, Pakistan

    Faraz Ahsan

Authors
  1. Memoona Malik
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  2. Faraz Ahsan
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  3. Sajjad Mohsin
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Correspondence to Memoona Malik.

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Communicated by V. Loia.

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Malik, M., Ahsan, F. & Mohsin, S. Adaptive image denoising using cuckoo algorithm. Soft Comput 20, 925–938 (2016). https://doi.org/10.1007/s00500-014-1552-x

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  • DOI: https://doi.org/10.1007/s00500-014-1552-x

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