From Handbook of Image and Video Processing
- 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
The University of Texas at Austin
Scott T. Acton,
University of Virginia
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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