Overview

Image enhancement is the processing of images to improve their appearance to human viewers, in terms of better contrast and visibility of features of interest, or to enhance their performance in subsequent computer-aided analysis and diagnosis. Because the objective of image enhancement is dependent on the application context and is often poorly defined, and the criteria are often subjective, image enhancement techniques tend to be ad hoc. Enhancement techniques include point operations, where the output pixel value depends only on its corresponding input value, and local or neighborhood operations, where the eventual output pixel value depends on the neighborhood of input pixel values. These latter operations include convolution, which uses appropriate masks or kernels to produce smoothing or sharpening of an image.

Learning Objectives

After reading this chapter you will be able to:

• describe the effect on the signal-to-noise ratio (SNR) of averaging noisy images;

• remove uneven background in an image;

• outline applications of image multiplication and division;

• describe the applications of logical and geometric operations;

• explain the process of convolution and the role of the point spread function (PSF) in imaging;

• recognize the benefits of using a variety of masks, including the median and Gaussian masks, for smoothing;

• distinguish between convolution masks used for smoothing and those used for sharpening an image;

• separate two-dimensional masks into one-dimensional masks where possible;

• choose appropriate techniques to sharpen an image, including an image with significant noise;

• compare the use...