Image inpainting with a wavelet domain Hidden Markov Tree model

Papandreou, G; Maragos, P; Kokaram, A

dc.contributor.author	Papandreou, G	en
dc.contributor.author	Maragos, P	en
dc.contributor.author	Kokaram, A	en
dc.date.accessioned	2014-03-01T02:45:30Z
dc.date.available	2014-03-01T02:45:30Z
dc.date.issued	2008	en
dc.identifier.issn	15206149	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32280
dc.subject	Direction Selectivity	en
dc.subject	Heavy Tail	en
dc.subject	image inpainting	en
dc.subject	Image Modeling	en
dc.subject	Image Representation	en
dc.subject	Image Restoration	en
dc.subject	Indexing Terms	en
dc.subject	Markov Chain Monte Carlo	en
dc.subject	Monte Carlo Method	en
dc.subject	Natural Images	en
dc.subject	Probabilistic Model	en
dc.subject	Hidden Markov Tree	en
dc.subject	Shift Invariant	en
dc.subject	Wavelet Transform	en
dc.subject.other	Acoustics	en
dc.subject.other	Discrete cosine transforms	en
dc.subject.other	Image enhancement	en
dc.subject.other	Image processing	en
dc.subject.other	Image segmentation	en
dc.subject.other	Painting	en
dc.subject.other	Signal processing	en
dc.subject.other	Speech	en
dc.subject.other	Image inpainting	en
dc.subject.other	International conferences	en
dc.subject.other	Wavelet transforms	en
dc.title	Image inpainting with a wavelet domain Hidden Markov Tree model	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICASSP.2008.4517724	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICASSP.2008.4517724	en
heal.identifier.secondary	4517724	en
heal.publicationDate	2008	en
heal.abstract	We present a novel technique for image inpainting, the problem of filling-in missing image parts. Image inpainting is ill-posed and we adopt a probabilistic model-based approach to regularize it. The main elements of our image model are, first, an over-complete complex-wavelet image representation, which ensures good shift invariance and directional selectivity and, second, a discrete-state/continuous-observation Hidden Markov Tree model for the wavelet coefficients, which captures key statistical properties of natural image wavelet responses, such as heavy-tailed histograms and persistence of large wavelet coefficients across scales. We show how these ideas can be integrated into a multi-scale generative process for natural images and present alternative deterministic and Markov chain Monte Carlo algorithms for image inpainting under this model. We demonstrate the effectiveness of the method in digitally restoring images of ancient wall-paintings. ©2008 IEEE.	en
heal.journalName	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en
dc.identifier.doi	10.1109/ICASSP.2008.4517724	en
dc.identifier.spage	773	en
dc.identifier.epage	776	en