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Efficient Parallel Nonlinear Multigrid Algorithms for Low-Level Vision Applications

Abstract Multigrid techniques have been shown to significantly improve the convergence rate of the nonlinear relaxation algorithms used in computer vision for the extraction of low-level image features. It is also well known that the computations involved with relaxation algorithms are regular and local, and lead naturally to massive data parallelism. However, standard data parallelism does not exploit the large computing resources of the now available massively parallel 2D processor arrays when coarse image resolutions (i.e., small image grids) have to be processed, like in multigrid methods. In this research note, we present an algorithmic framework which enables us making a full use of the large potential of data parallelism for the implementation of nonlinear multigrid relaxation methods. The approach combines two different levels of parallelism: parallel updating of the image sites and concurrent explorations of the configuration space of the problem. The efficiency of the method is demonstrated on two different low-level vision applications: restoration of noisy images and optical flow computation.