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<p>The reduction of impulse noise is crucial in processing pictures since it directly impacts the patterns of noise present. This paper proposes a two-step technique, known as DCIFF (DBSCAN clustering identified fuzzy filter), to effectively eliminate impulsive noise from digital images. The suggested DCIFF methodology employs the DBSCAN clustering algorithm to identify and classify noise. Subsequently, a noise reduction technique is presented, which relies on the principles of fuzzy reasoning. The density-based clustering approach DBSCAN is utilized to identify commonalities throughout all 5 × 5 sized windows that are created by centering each of the pixels in the picture. Next, to properly reduce noise, a sized 7 × 7 window is formed with the centre of each identified disturbed pixel. The non-noisy pixel intensities are multiplied by their triangle fuzzy membership values, and then the products are added together. The total result is then divided by the sum of the fuzzy membership of all the non-noisy pixels’ intensities. The outcome is utilized to eliminate the noise from the central pixel that is affected by noise. The DCIFF is assessed using standard image evaluation criteria for various pictures. On the Lena image modified by random valued impulsive noise at 80% noise density, DCIFF provided peak-signal-to-noise ratios (PSNR) of 25.86&#xa0;dB and structural similarity indexes (SSIM) of 0.8429. In contrast to prior research on the same topic, the current study findings consistently demonstrate striking outcomes.</p>

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Density Based Clustering Approach Combined with Fuzzy Logic for Elimination of Random Valued Impulse Noise

  • 期刊论文

  • 发表日期：

    2025年1月20日

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  • Sulagna Basu,
  • Aritra Bandyopadhyay

摘要

The reduction of impulse noise is crucial in processing pictures since it directly impacts the patterns of noise present. This paper proposes a two-step technique, known as DCIFF (DBSCAN clustering identified fuzzy filter), to effectively eliminate impulsive noise from digital images. The suggested DCIFF methodology employs the DBSCAN clustering algorithm to identify and classify noise. Subsequently, a noise reduction technique is presented, which relies on the principles of fuzzy reasoning. The density-based clustering approach DBSCAN is utilized to identify commonalities throughout all 5 × 5 sized windows that are created by centering each of the pixels in the picture. Next, to properly reduce noise, a sized 7 × 7 window is formed with the centre of each identified disturbed pixel. The non-noisy pixel intensities are multiplied by their triangle fuzzy membership values, and then the products are added together. The total result is then divided by the sum of the fuzzy membership of all the non-noisy pixels’ intensities. The outcome is utilized to eliminate the noise from the central pixel that is affected by noise. The DCIFF is assessed using standard image evaluation criteria for various pictures. On the Lena image modified by random valued impulsive noise at 80% noise density, DCIFF provided peak-signal-to-noise ratios (PSNR) of 25.86 dB and structural similarity indexes (SSIM) of 0.8429. In contrast to prior research on the same topic, the current study findings consistently demonstrate striking outcomes.