@incollection {acker2008,
	title = {Inverse Problems and Parameter Identification in Image Processing},
	year = {2008},
	pages = {111--151},
	publisher = {Springer},
	abstract = {Many problems in imaging are actually inverse problems. One reason for this is that conditions and parameters of the physical processes underlying the actual image acquisition are usually not known. Examples for this are the inhomogeneities of the magnetic field in magnetic resonance imaging (MRI) leading to nonlinear deformations of the anatomic structures in the recorded images, material parameters in geological structures as unknown parameters for the simulation of seismic wave propagation with sparse measurement on the surface, or temporal changes in movie sequences given by intensity changes or moving image edges and resulting from deformation, growth and transport processes with unknown fluxes. The underlying physics is mathematically described in terms of variational problems or evolution processes. Hence, solutions of the forward problem are naturally described by partial differential equations. These forward models are reflected by the corresponding inverse problems as well. Beyond these concrete, direct modeling links to continuum mechanics abstract concepts from physical modeling are successfully picked up to solve general perceptual problems in imaging. Examples are visually intuitive methods to blend between images showing multiscale structures at different resolution or methods for the analysis of flow fields.},
	doi = {10.1007/978-3-540-75632-3_4},
	author = {Jens F. Acker and Benjamin Berkels and Kristian Bredies and Mamadou S. Diallo and Marc Droske and Christoph S. Garbe and Matthias Holschneider and Jaroslav Hron and Claudia Kondermann and Michail Kulesh and Peter Maass and Nadine Olischl{\"a}ger and Heinz-Otto Peitgen},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@article {preusser2007,
	title = {A phase field method for joint denoising, edge detection, and motion estimation in image sequence processing.},
	journal = {SIAM Journal of Applied Mathematics},
	volume = {68},
	year = {2007},
	pages = {599-618},
	abstract = {The estimation of optical flow fields from image sequences is incorporated in a Mumford/Shah approach for image denoising and edge detection. Possibly noisy image sequences are considered as input and a piecewise smooth image intensity, a piecewise smooth motion field, and a joint discontinuity set are obtained as minimizers of the functional. The method simultaneously detects image edges and motion field discontinuities in a rigorous and robust way. It is able to handle information on motion that is concentrated on edges. Inherent to it is a natural multiscale approximation that is closely related to the phase field approximation for edge detection by Ambrosio and Tortorelli. We present an implementation for two-dimensional image sequences with finite elements in space and time. This leads to three linear systems of equations, which have to be solved in a suitable iterative minimization procedure. Numerical results and different applications underline the robustness of the approach presented.},
	doi = {10.1137/060677409},
	author = {Preusser, T. and Marc Droske and Christoph S. Garbe and Martin Rumpf and Alexandru Telea}
}
@conference {telea2006,
	title = {A variational approach to joint denoising, edge detection and motion estimation},
	booktitle = {Proceedings of the 28th DAGM Symposium on Pattern Recognition},
	year = {2006},
	pages = {525--535},
	url = {http://numod.ins.uni-bonn.de/research/papers/public/PrDrGa06.pdf},
	author = {Alexandru Telea and Tobias Preu{\ss}er and Christoph S. Garbe and Marc Droske and Martin Rumpf}
}
