Single image example-based super-resolution using cross-scale patch matching and markov random field modelling

Authors:
Tijana Ružić;Hiêp Q. Luong;Aleksandra Pižurica;Wilfried Philips
Affiliations:
Ghent University, TELIN-IPI-IBBT, Ghent, Belgium;Ghent University, TELIN-IPI-IBBT, Ghent, Belgium;Ghent University, TELIN-IPI-IBBT, Ghent, Belgium;Ghent University, TELIN-IPI-IBBT, Ghent, Belgium
Venue:
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Year:
2011

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Abstract

Example-based super-resolution has become increasingly popular over the last few years for its ability to overcome the limitations of classical multi-frame approach. In this paper we present a new example-based method that uses the input low-resolution image itself as a search space for high-resolution patches by exploiting self-similarity across different resolution scales. Found examples are combined in a highresolution image by the means of Markov Random Field modelling that forces their global agreement. Additionally, we apply back-projection and steering kernel regression as post-processing techniques. In this way, we are able to produce sharp and artefact-free results that are comparable or better than standard interpolation and state-of-the-art super-resolution techniques.