| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Zegeling, Dr. Paul A. | |
| dc.contributor.advisor | Bisseling, Prof. dr. Rob H. | |
| dc.contributor.author | Oron, K.E. | |
| dc.date.accessioned | 2011-11-09T18:00:44Z | |
| dc.date.available | 2011-11-09 | |
| dc.date.available | 2011-11-09T18:00:44Z | |
| dc.date.issued | 2011 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/9414 | |
| dc.description.abstract | (Digital) Signal Processing plays a huge role in computer vision. We will use two
related Partial Differential Equations (PDEs), known for their smoothing feature,
to investigate the removal of noise in (digital) signals, namely: the Heat and Perona-
Malik equation. This report explains how we can do (digital) signal processing on
a bounded domain $_ Rn (n = 1; 2)$, via a PDE approach. Depending on the type
of noise present in the signal, the PDE approach gives desirable results. For faster
iteration with the Perona-Malik equation we first need an (un)conditionally stable
finite difference method or use a non uniform grid with $R$-refinement (adaptive
grids) for a possibly better edge detection. | |
| dc.description.sponsorship | Utrecht University | |
| dc.format.extent | 2839962 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | |
| dc.title | R-refinement in image-processing via the PDE-approach | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | Finite Difference methods, Heat equation, Perona-Malik1 equation, Digital
Signal Processing, Image Processing, noise reduction (denoising), (anisotropic) diffusion,
refinement | |
| dc.subject.courseuu | Scientific Computing | |
