Adaptive Periodic Noise Reduction in Digital Images Using Fuzzy Transform

Abstract

Periodic noise degrades the image quality by overlaying similar patterns. This noise appears as peaks in the image spectrum. In this research, a method based on fuzzy transform has been developed to identify and reduce the peaks adaptively. We convert the periodic noise removal task as image compression and a smoothing problem. We first utilize the direct and inverse fuzzy transform of the spectrum to detect periodic noise peaks. Second, we propose a fuzzy transform-based notch filter for spectral smoothing and separating the original image from the periodic noise components. This noise correction approach filters out a portion (given by fuzzy transform) of the noise component. Extensive experiments on both synthetic and non-synthetic noisy images have been carried out to validate the effectiveness and efficiency of the proposed algorithm. The simulation results demonstrate that the proposed method outperforms state of the art algorithms both visually and quantitatively.

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Correspondence to AliMohammad Latif.

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Alibabaie, N., Latif, A. Adaptive Periodic Noise Reduction in Digital Images Using Fuzzy Transform. J Math Imaging Vis (2021). https://doi.org/10.1007/s10851-020-01004-0

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Keywords

  • Image noise removal
  • Fuzzy transform
  • Periodic noise
  • Stripping noise