Super-resolution Imaging Using Grid Computing

Abstract

The super-resolution (SR) imaging is to overcome the inherent limitations of the image acquisition systems to produce high-resolution images from their low-resolution counterparts. In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully developed and shown as a promising stochastic approach for addressing the SR problem. However, the MCMC SR approach requires substantial amounts of computational resources, for it not only needs to generate a huge number of samples, but also requires an exhaustive search for obtaining an optimal prior image model. To tackle the above computation challenge, Grid computing is introduced for tackling the SR problem in this paper. The computationally- intensive MCMC SR task is broke down into a set of independent and small sub-tasks, which are further distributed and implemented in the grid computing environment. Their respective results are finally assembled to produce a high-resolution image as the final result of the entire MCMC SR task. Experiments are conducted to show that grid computing can effectively accelerating the execution time of the MCMC SR algorithm.