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Evolutionary Models for Signal Enhancement and Approximation

Bergerhoff, Leif (2021) Evolutionary Models for Signal Enhancement and Approximation. Dissertation, Saarland University. doi: 10.22028/D291-34294.

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Abstract

This thesis deals with nature-inspired evolution processes for the purpose of signal enhancement and approximation. The focus lies on mathematical models which originate from the description of swarm behaviour. We extend existing approaches and show the potential of swarming processes as a modelling tool in image processing. In our work, we discuss the use cases of grey scale quantisation, contrast enhancement, line detection, and coherence enhancement. Furthermore, we propose a new and purely repulsive model of swarming that turns out to describe a specific type of backward diffusion process. It is remarkable that our model provides extensive stability guarantees which even support the utilisation of standard numerics. In experiments, we demonstrate its applicability to global and local contrast enhancement of digital images. In addition, we study the problem of one-dimensional signal approximation with limited resources using an adaptive sampling approach including tonal optimisation. We suggest a direct energy minimisation strategy and validate its efficacy in experiments. Moreover, we show that our approximation model can outperform a method recently proposed by Dar and Bruckstein.

Item URL in elib:https://elib.dlr.de/145558/
Document Type:Thesis (Dissertation)
Title:Evolutionary Models for Signal Enhancement and Approximation
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Bergerhoff, LeifLeif.Bergerhoff (at) dlr.dehttps://orcid.org/0000-0002-9422-8115
Date:13 July 2021
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.22028/D291-34294
Number of Pages:163
Status:Published
Keywords:dynamical systems, diffusion filtering, swarm dynamics, signal enhancement, signal approximation, convex optimisation, non-convex optimisation, inverse processes
Institution:Saarland University
Department:Faculty of Mathematics and Computer Science
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Neustrelitz
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Bergerhoff, Dr. Leif
Deposited On:23 Nov 2021 10:10
Last Modified:23 Nov 2021 10:10

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