English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  Finding Optimal Smoothnessnoperators for Inpainting with Bi-level Optimization

Tomasson, J. A. (2017). Finding Optimal Smoothnessnoperators for Inpainting with Bi-level Optimization. Master Thesis, Universität des Saarlandes, Saarbrücken.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/21.11116/0000-0000-7641-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0000-7642-1
Genre: Thesis

Files

show Files
hide Files
:
2017_Tomasson_ MSc thesis.pdf (Any fulltext), 828KB
 
File Permalink:
-
Name:
2017_Tomasson_ MSc thesis.pdf
Description:
-
OA-Status:
Visibility:
Restricted (Max Planck Institute for Informatics, MSIN; )
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Tomasson, Jon Arnar1, Author
Weickert, Joachim2, Advisor
Ochs, Peter2, Referee
Affiliations:
1International Max Planck Research School, MPI for Informatics, Max Planck Society, Campus E1 4, 66123 Saarbrücken, DE, ou_1116551              
2Externe Organisation (UdS), ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: Inpainting and image denoising are two problems in image processing that can be formulated as rather similar partial differential equations or PDE. In this work the effects of higher order smoothness constraints on the results of denoising and inpaintingvwere looked at. Methods from bi-level optimization were used in order to learn optimal smoothness constraints. The differences between the optimal smoothness constraints for the two problems were looked at both for a linear model and a more complex non-linear model. The results for the linear model were that inpainting favoured first order smoothness. For denoising on the other hand all of the different orders of smoothness made up a comparable part of the optimal smoothness constraint. Even with this difference the overall effect on the quality of the results was similar for both problems. For the non-linear model it was much more difficult to find a good smoothness constraint for the inpainting problem than for the denoising problem and the learned smoothness constraints looked very different.

Details

show
hide
Language(s): eng - English
 Dates: 2017-03-222017
 Publication Status: Issued
 Pages: 52 p.
 Publishing info: Saarbrücken : Universität des Saarlandes
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Tomassonmaster2017
 Degree: Master

Event

show

Legal Case

show

Project information

show

Source

show