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Adaptive wavelets and their applications to image fusion and compression
Piella, G.
Publication date
2003
Link to publication
Citation for published version (APA):
Piella, G. (2003). Adaptive wavelets and their applications to image fusion and compression.
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Abstract
Over the past few years, wavelets have become extremely popular in signal and image processing applications. The classical linear wavelet transform, however, performs a homogeneous smoothing of the signal contents which, in some cases, is not desirable. This has led to a growing interest in (nonlinear) wavelet, representations that can preserve discontinuities, such as transitions and edges.
In this thesis, we present the construction of adaptive wavelets by means of an extension of the lifting scheme. The basic idea is to choose the update filters according to some decision criterion which depends on the local characteristics of the input signal. In this way. only homogeneous regions are smoothed while discontinuities are preserved. An interesting aspect of our approach is that it is neither causal nor redundant, i.e., it does not require any bookkeeping to enable perfect reconstruction. We show that these adaptive schemes yield lower entropies than schemes with fixed update filters, a property that is highly relevant in the context of
compression.
Another main topic of this thesis is image fusion using wavelets and other inultiresolution representations. We propose an axiomatic setup for multiresolution-based fusion which encompasses most of the existing inul-tiresolution image fusion schemes, but also allows the construction of new ones, both pixel and region-based. The purpose of such fusion framework is to use it for the development of adaptive fusion techniques in which the source images determine the type of multiresolut ion transform being used as well as the parameters that are involved.
