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.

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A very challenging issue for optimizing compilers is the phase ordering problem: In what order should a collection of compiler optimizations be performed? We address this problem in the context of optimizing a sequence of tensor contractions. The pertinent loop transformations are loop permutation, tiling, and fusion; in addition, the placement of disk I/O statements crucially affects performance. The space of possible combinations is exponentially large. We develop novel pruning strategies whereby a search problem in a larger space is replaced by a large number of searches in a much smaller space, to determine the optimal permutation, fusion, tiling and placement of disk I/O statements. Experimental results show that we obtain an improvement in I/O cost by a factor of up to 2.6 over an equi-tile-size approach. Proceedings of the 2005 ACM/IEEE SC|05 Conference (SC'05) Proceedings of the 2005 ACM/IEEE SC|05 Conference (SC'05) Proceedings of the 2005 ACM/IEEE SC|05 Conference (SC'05)

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