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Genetic-based fuzzy image filter and its application to image processing.

Chang-Shing Lee1, Shu-Mei Guo, Chin-Yuan Hsu

  • 1Department of Information Management, Chang Jung Christian University, Tainan 711, Taiwan, ROC. leecs@mail.cju.edu.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 1, 2005
PubMed
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This study introduces a Genetic-based Fuzzy Image Filter (GFIF) for removing impulse noise from corrupted images. GFIF demonstrates superior performance in noise reduction and image restoration compared to existing methods.

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Additive identical independent distribution (i.i.d.) impulse noise significantly degrades image quality.
  • Existing filters often struggle with highly corrupted images, necessitating advanced noise removal techniques.

Purpose of the Study:

  • To propose a novel Genetic-based Fuzzy Image Filter (GFIF) for effective impulse noise removal.
  • To enhance image restoration quality in highly corrupted digital images.

Main Methods:

  • The GFIF integrates fuzzy number construction, fuzzy filtering (including fuzzy inference, fuzzy mean, and fuzzy decision), and a genetic learning process.
  • An image knowledge base is constructed and optimized using a genetic algorithm for adaptive parameter tuning.

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Main Results:

  • GFIF outperforms state-of-the-art filters in objective metrics like Peak-Signal-to-Noise-Ratio (PSNR), Mean-Square-Error (MSE), and Mean-Absolute-Error (MAE).
  • Subjective evaluations confirm GFIF's superior global restoration quality for filtered images.

Conclusions:

  • The proposed Genetic-based Fuzzy Image Filter (GFIF) offers a robust solution for impulse noise removal in digital images.
  • GFIF provides significant improvements in both quantitative and qualitative image restoration, outperforming existing methods.