Geometric and Texture Inpainting by Gibbs Sampling
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
Geometric and Texture Inpainting by Gibbs Sampling. / Gustafsson, David Karl John; Pedersen, Kim Steenstrup; Nielsen, Mads.
Proceedings SSBA 2007: Symposium on inage analysis, Linköping, March 14-14, 2007. ed. / Magnus Borga; Anders Brun; Michael Felsberg. Linköpings Universitet, 2007. (Institutionen för medicinsk teknik, Universitetet i Linköping; No. LiU-IMT-R-0047).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Geometric and Texture Inpainting by Gibbs Sampling
AU - Gustafsson, David Karl John
AU - Pedersen, Kim Steenstrup
AU - Nielsen, Mads
PY - 2007
Y1 - 2007
N2 - This paper discuss a method suitable for inpaintingboth large scale geometric structures and more stochastic texture components. Image inpaintingconcerns the problem of reconstructing the intensitycontents inside regions of missing data. Commontechniques for solving this problem are methods based on variational calculus and based onstatistical methods. Variationalmethods are good atreconstructing large scale geometric structures buthave a tendency to smooth away texture. On the contrary statistical methods can reproduce texturefaithfully but fails to reconstruct large scalestructures. In this paper we use the well-knownFRAME (Filters, Random Fields and Maximum Entropy) for inpainting. We introduce a temperature term inthe learned FRAME Gibbs distribution. By samplingusing different temperature in the FRAME Gibbsdistribution, different contents of the image are reconstructed. We propose a two step method forinpainting using FRAME. First the geometricstructure of the image is reconstructed by samplingfrom a cooled Gibbs distribution, then the stochastic component is reconstructed by samplefroma heated Gibbs distribution. Both steps in thereconstruction process are necessary, and contributein two very different ways to the appearance of the reconstruction.
AB - This paper discuss a method suitable for inpaintingboth large scale geometric structures and more stochastic texture components. Image inpaintingconcerns the problem of reconstructing the intensitycontents inside regions of missing data. Commontechniques for solving this problem are methods based on variational calculus and based onstatistical methods. Variationalmethods are good atreconstructing large scale geometric structures buthave a tendency to smooth away texture. On the contrary statistical methods can reproduce texturefaithfully but fails to reconstruct large scalestructures. In this paper we use the well-knownFRAME (Filters, Random Fields and Maximum Entropy) for inpainting. We introduce a temperature term inthe learned FRAME Gibbs distribution. By samplingusing different temperature in the FRAME Gibbsdistribution, different contents of the image are reconstructed. We propose a two step method forinpainting using FRAME. First the geometricstructure of the image is reconstructed by samplingfrom a cooled Gibbs distribution, then the stochastic component is reconstructed by samplefroma heated Gibbs distribution. Both steps in thereconstruction process are necessary, and contributein two very different ways to the appearance of the reconstruction.
M3 - Article in proceedings
T3 - Institutionen för medicinsk teknik, Universitetet i Linköping
BT - Proceedings SSBA 2007
A2 - Borga, Magnus
A2 - Brun, Anders
A2 - Felsberg, Michael
PB - Linköpings Universitet
Y2 - 14 March 2007 through 15 March 2007
ER -
ID: 2030868