“Bovik: 16-ch12-p374457” — 2009/1/4 — 2:17 — page 263 — #1 CHAPTER 12 Nonlinear Filtering for Image Analysis and Enhancement Gonzalo R. Arce 1 , Jan Bacca 1 , and José L. Paredes 2 1 University of Delaware; 2 Universidad de Los Andes 12.1 INTRODUCTION Digital image enhancement and analysis have played, and will continue to play, an important role in scientific, industrial, and military applications. In addition to these applications, image enhancement and analysis are increasingly being used in consumer electronics. Internet Web users, for instance, rely on built-in image processing proto- cols such as JPEG and interpolation and in the process have become image processing users equipped with powerful yet inexpensive software such as Photoshop. Users not only retrieve digital images from the Web but are now able to acquire their own by use of digital cameras or through digitization services of standard 35 mm analog film. The end result is that consumers are beginning to use home computers to enhance and manipulate their own digital pictures. Image enhancement refers to processes seeking to improve the visual appearance of an image. As an example, image enhancement might be used to emphasize the edges within the image. This edge-enhanced image would be more visually pleasing to the naked eye, or perhaps could serve as an input to a machine that would detect the edges and perhaps make measurements of shape and size of the detected edges. Image enhancement is important because of its usefulness in virtually all image processing applications. Image enhancement tools are often classified into (a) point operations and (b) spa- tial operations. Point operations include contrast stretching, noise clipping, histogram modification, and pseudo-coloring. Point operations are, in general, simple nonlinear operations that are well known in the image processing literature and are covered else- where in this Guide. Spatial operations used in image processing today are, on the other hand, typically linear operations. The reason for this is that spatial linear operations are simple and easily implemented. Although linear image enhancement tools are often adequate in many applications, significant advantages in image enhancement can be attained if nonlinear techniques are applied [1]. Nonlinear methods effectively preserve 263