Summary
In this chapter, we discussed different image enhancement methods, starting from point transformations (for example, contrast stretching and thresholding), then techniques based on histogram processing (for example, histogram equalization and histogram matching), followed by image denoising techniques with linear (for example, mean and Gaussian) and non-linear (for example, median, bilateral, and non-local means) filters.
By the end of this chapter, the reader should be able to write Python codes for point transformations (for example, negative, power-law transform, and contrast stretching), histogram-based image enhancements (for example, histogram equalization/matching), and image denoising (for example, mean/median filters).
In the following chapter, we shall continue discussing more image enhancement techniques based on image derivatives and gradients.
