Abstract
Inverse problems in digital image analysis are the main theme of this book. Typically, we have image-type data (an image, a pair of images, an image sequence, a set of projection images, etc.) from which we wish to extract information that is of interest to the user. Here are a few examples:
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1.
Our perception of the image is disturbed by an illumination fault. This fault must be removed to provide a more readable image (Figure 1.1).
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2.
For a pair of similar images, we wish to estimate the deformation field that transforms one image into the other (Figure 1.2).
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3.
From a set of images of a family of objects, we wish to estimate a model representing the variations of these objects (Figure 1.3).
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4.
On a very noisy image, we wish to detect specific entities such as surface rupture lines (Figure 1.4).
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5.
On a sequence of images, we wish to estimate the path of objects that are moving and being deformed throughout the image sequence (Figure 1.5).
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6.
From several radiographic projections of an object, we wish to create a three-dimensional reconstruction of the contents of the object (Figure 1.6).
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© 2003 Springer-Verlag New York, Inc.
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Chalmond, B. (2003). Introduction. In: Modeling and Inverse Problems in Imaging Analysis. Applied Mathematical Sciences, vol 155. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21662-1_1
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DOI: https://doi.org/10.1007/978-0-387-21662-1_1
Publisher Name: Springer, New York, NY
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