Overcoming registration uncertainty in image super-resolution: maximize or marginalize?
EURASIP Journal on Advances in Signal Processing
Image magnification based on a blockwise adaptive Markov random field model
Image and Vision Computing
Super-resolution using sub-band constrained total variation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Zoom based super-resolution: a fast approach using particle swarm optimization
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
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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.