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
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Spatial domain methods, which operate directly on pixels, and
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frequency domain methods, which operate on the Fourier transform of an image.
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Point processing
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Median and Max/Min filtering
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Image Subtraction
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Histogram Equalization
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Frequency Domain Method
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Image Smoothing
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Neighbourhood Averaging
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Edge preserving smoothing
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Image sharpening
Examples and methods of image enhancement:
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Filtering with morphological operators
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Noise removal using a Wiener filter
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Linear contrast adjustment
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Median filtering
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Unsharp mask filtering
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Contrast-limited adaptive histogram equalization (CLAHE)
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Decorrelation stretch