A learning-based method for image super-resolution from zoomed observations

Joshi, M. V. ; Chaudhuri, S. ; Panuganti, R. (2005) A learning-based method for image super-resolution from zoomed observations IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 35 (3). pp. 527-537. ISSN 1083-4419

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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

Related URL: http://dx.doi.org/10.1109/TSMCB.2005.846647


We propose a technique for super-resolution imaging of a scene from observations at different camera zooms. Given a sequence of images with different zoom factors of a static scene, we obtain a picture of the entire scene at a resolution corresponding to the most zoomed observation. The high-resolution image is modeled through appropriate parameterization, and the parameters are learned from the most zoomed observation. Assuming a homogeneity of the high-resolution field, the learned model is used as a prior while super-resolving the scene. We suggest the use of either a Markov random field (MRF) or an simultaneous autoregressive (SAR) model to parameterize the field based on the computation one can afford. We substantiate the suitability of the proposed method through a large number of experimentations on both simulated and real data.

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