Non-parametric image super-resolution using multiple images

Mithun Das Gupta, Shyamsundar Rajaram, Nemanja Petrovic, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a novel learning based framework for performing super-resolution using multiple images. We model the image as an undirected graphical model over image patches in which the compatibility functions are represented as non-parametric kernel densities which are learnt from training data. The observed images are translation rectified and stitched together onto a high resolution grid and the inference problem reduces to estimating unknown pixels in the grid. We solve the inference problem by using an extended version of the non-parametric belief propagation algorithm. We show experimental results on synthetic digit images and real face images from the ORL face dataset.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing 2005, ICIP 2005
Pages89-92
Number of pages4
DOIs
StatePublished - Dec 1 2005
EventIEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy
Duration: Sep 11 2005Sep 14 2005

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing 2005, ICIP 2005
Country/TerritoryItaly
CityGenova
Period9/11/059/14/05

ASJC Scopus subject areas

  • Engineering(all)

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