Image Enhancement In Machine Learning

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Image Enhancement

The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to provide `better' input for other automated image processing techniques.

Image enhancement techniques can be divided into two broad categories:

Image Enhancement Techniques
  • Spatial domain methods, which operate directly on pixels, and​

  • frequency domain methods, which operate on the Fourier transform of an image.

  • Point processing

  • Median and Max/Min filtering

  • Image Subtraction

  • Histogram Equalization

  • Frequency Domain Method

  • Image Smoothing

  • Neighbourhood Averaging

  • Edge preserving smoothing

  • Image sharpening

Examples and methods of image enhancement:
  • Filtering with morphological operators

  • Noise removal using a Wiener filter

  • Linear contrast adjustment

  • Median filtering

  • Unsharp mask filtering

  • Contrast-limited adaptive histogram equalization (CLAHE)

  • Decorrelation stretch