Handbook of Image and Video Processing

Section III: Image and Video Processing

Chapters List

Chapter 3.1: Basic Linear Filtering with Application to Image Enhancement
Chapter 3.2: Nonlinear Filtering for Image Analysis and Enhancement
Chapter 3.3: Morphological Filtering for Image Enhancement and Feature Detection
Chapter 3.4: Wavelet Denoising for Image Enhancement
Chapter 3.5: Basic Methods for Image Restoration and Identification
Chapter 3.6: Regularization in Image Restoration and Reconstruction
Chapter 3.7: Multichannel Image Recovery
Chapter 3.8: Multi-Frame Image Restoration
Chapter 3.9: Iterative Image Restoration
Chapter 3.10: Motion Detection and Estimation
Chapter 3.11: Video Enhancement and Restoration
Chapter 3.12: Local and Global Stereo Methods
Chapter 3.13: Image Sequence Stabilization, Mosaicking, and Superresolution
Alan C. Bovik,
The University of Texas at Austin
Scott T. Acton,
University of Virginia

1 Introduction

Linear system theory and linear filtering play central roles in digital image and video processing. Many potent techniques for modifying, improving, or representing digital visual data are expressed in terms of linear systems concepts. Linear filters are used for generic tasks such as image/video contrast improvement, denoising, and sharpening, as well as for more object- or feature-specific tasks such as target matching and feature enhancement.

Much of this Handbook deals with the application of linear filters to image and video enhancement, restoration, reconstruction, detection, segmentation, compression, and transmission. The goal of this chapter is to introduce some of the basic supporting ideas of linear systems theory as they apply to digital image filtering, and to outline some of the applications. Special emphasis is given to the topic of linear image enhancement.

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