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2017 | OriginalPaper | Buchkapitel

4. Image Enhancement

verfasst von : Klaus D. Toennies

Erschienen in: Guide to Medical Image Analysis

Verlag: Springer London

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Abstract

Two reasons exist for applying an image enhancement technique. Enhancement can increase perceptibility of objects in an image to the human observer or it may be needed as a preprocessing step for subsequent automatic image analysis. Enhancement methods differ for the two purposes. An enhancement method requires a criterion by which its success can be judged. This will be a definition of image quality, since improving quality is the goal of such method. Various quality definitions will be presented and discussed. Different enhancement techniques will be presented covering methods for contrast enhancement, for the enhancement of edges, and for noise reduction. Edge-preserving smoothing based on different assumptions about noise and edges in images will be presented in detail including the presentation of several popular methods for edge-preserving smoothing.

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Vorheriges Kapitel Image Storage and Transfer

Nächstes Kapitel Feature Detection

Compression rates of a lossless compression method using signal compression are bounded by the entropy. These methods compress pixels independent of their neighborhood. It can be shown that the maximum compression rate is the ratio of bits per pixel in the image to bits per pixel as predicted by entropy. Lossless compression using other kinds of methods such as run-length encoding may achieve higher compression rates.

The actual definition is a bit more complex. It essentially says that only those objects in two adjacent slices should overlap for which the shortest distance on the surface between any two points on the two slices should not intersect any other slices.

The Laplacian may also be computed as divergence of the gradient, denoted as ∇² = ∇·∇, hence the symbol ∇².

The reason for this is easily seen when the filter is transformed into the spatial domain in order to form the corresponding convolution kernel. The result is a sinc function which obviously causes the repetitions of edges after filtering, which is called ringing.

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Titel: Image Enhancement
verfasst von: Klaus D. Toennies
Verlag: Springer London
Buch: Guide to Medical Image Analysis
Print ISBN: 978-1-4471-7318-2

Electronic ISBN: 978-1-4471-7320-5

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-1-4471-7320-5_4

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