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An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Author

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  • Kai Hu
  • Aiguo Song
  • Min Xia
  • Zhiyong Fan
  • Xiaoying Chen
  • Ruidong Zhang
  • Zhichen Zheng

Abstract

Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO) algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR).

Suggested Citation

  • Kai Hu & Aiguo Song & Min Xia & Zhiyong Fan & Xiaoying Chen & Ruidong Zhang & Zhichen Zheng, 2015. "An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, October.
    • Handle: RePEc:hin:jnlmpe:414561
    DOI: 10.1155/2015/414561
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    Cited by:

    1. Mariusz KorzeĊ„ & Maciej Kruszyna, 2023. "Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section," IJERPH, MDPI, vol. 20(6), pages 1-15, March.

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