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Title: A learning-based method for image super-resolution from zoomed observations
Keywords: Markov Processes
Image Quality
Learning Systems
Regression Analysis
Issue Date: 2005
Publisher: IEEE
Citation: IEEE Transactions on Systems, Man, and Cybernetics, Part B 35(3), 527-37
Abstract: 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.
ISSN: 1083-4419
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