Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis.
Here are some useful examples and methods of image enhancement:
- Filtering with morphological operators
- Histogram equalization
- Noise removal using a Wiener filter
- Linear contrast adjustment
- Median filtering
- Unsharp mask filtering
- Contrast-limited adaptive histogram equalization (CLAHE)
- Decorrelation stretch
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:
1. Spatial domain methods, which operate directly on pixels
2. frequency domain methods, which operate on the Fourier transform of an image.
Unfortunately, there is no general theory for determining what is `good' image enhancement when it comes to human perception. If it looks good, it is good! However, when image enhancement techniques are used as pre-processing tools for other image processing techniques, then quantitative measures can determine which techniques are most appropriate.
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