GRID COMPUTING FOR STOCHASTIC SUPER-RESOLUTION IMAGING: FUNDAMENTALS AND ALGORITHMS

The super-resolution (SR) imaging refers to the image processing algorithms for overcoming the inherent limitations of the image acquisition systems to produce high-resolution images from their low-resolution counterparts. In our recent work, a stochastic SR imaging framework has been successfully developed by applying the Markov chain Monte Carlo (MCMC) technique and shown as a promising approach for addressing the SR problem. To further overcome the intensive computation requirement of the stochastic SR imaging, Grid computing is resorted in this paper to break down the computationally-intensive MCMC SR, task into a set of independent and small sub-tasks for parallel computing in the Grid computing environment. Experiments are conducted to show that Grid computing can effectively accelerating the execution time of the stochastic SR algorithm.