In this chapter, we will discuss some of the most basic tools in image processing, such as mean/median filtering and histogram equalization, which are still among the most powerful tools. The objective of image enhancement is to improve the quality of an image or make particular features appear more prominent. These techniques are more general purpose techniques, and a strong model of the degradation process is not assumed (unlike image restoration). Some examples of image enhancement techniques are contrast stretching, smoothing, and sharpening. We will describe the basic concepts and implementation of these techniques using Python library functions and using PIL, scikit-image, and scipy ndimage libraries. We will become familiar with simple and still-popular approaches.

We will start with point-wise intensity transformation, and then discuss contrast stretching, thresholding, half-toning, and dithering algorithms, and the corresponding Python library functions. Then we will discuss different histogram processing techniques such as histogram equalization (both its global and adaptive version) and histogram matching. Then, a few techniques to denoise an image will be described. First, a few linear smoothing techniques such as the average filter and Gaussian filter will be described, followed by relatively more recent non-linear noise smoothing techniques such as median filtering, bilateral filtering, and non-local means filtering, along with how to implement them in Python. Finally, different image operations with mathematical morphology and their applications, along with implementations, will be described.

The topics to be covered in this chapter are as follows:

• Point-wise intensity transformations – pixel transformation
• Histogram processing, histogram equalization, histogram matching
• Linear noise smoothing (mean filter)
• Non-linear noise smoothing (median filter)