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A new algorithm for image noise reduction using mathematical morphology.

R I Peters1

  • 1Dept. of Electr. Eng., Vanderbilt Univ., Nashville, TN.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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A new morphological image cleaning (MIC) algorithm effectively removes noise from grayscale images while preserving essential thin features. This method is ideal for scanned or video images, improving image quality and compression.

Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Morphological operations like openings and closings smooth grayscale images but often remove thin features alongside noise.
  • Existing morphological filters struggle with dense, low-amplitude noise common in scanned or video imagery.

Purpose of the Study:

  • To introduce and analyze a novel morphological image cleaning (MIC) algorithm designed to preserve thin features while effectively reducing noise.
  • To address limitations of traditional morphological filters in handling specific noise types in grayscale images.

Main Methods:

  • The MIC algorithm processes residual images, which are differences between the original and smoothed versions.
  • It utilizes a morphological size distribution to calculate residuals at multiple scales.

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  • Noise is identified and discarded in residual images before recombining with a smoothed version to create the cleaned image.

    • Main Results:

    • The MIC algorithm successfully preserves thin image features while removing noise, particularly dense, low-amplitude, random, or patterned noise.
    • Experimental results demonstrate effective noise removal in scanned and still-video images, including scanner noise.
    • The algorithm significantly enhances JPEG compression efficiency for grayscale images.

    Conclusions:

    • The MIC algorithm offers a robust solution for noise reduction in grayscale images where feature preservation is critical.
    • It provides a significant improvement over traditional methods for specific noise types and enhances subsequent image compression.
    • MIC is a valuable tool for preprocessing images from sources prone to noise, such as scanners and video cameras.