In this paper, we present two novel speed-up techniques for deterministic inference on Markov random fields (MRF) via generalized belief propagation (GBP). Both methods require the MRF to have a grid-like graph structure, as it is generally encountered in 2D and 3D image processing applications, e.g. in image filtering, restoration or segmentation. First, we propose a caching method that significantly reduces the number of multiplications during GBP inference. And second, we introduce a speed-up for computing the MAP estimate of GBP cluster messages by presorting its factors and limiting the number of possible combinations. Experimental results suggest that the first technique improves the GBP complexity by roughly factor 10, whereas the acceleration for the second technique is linear in the number of possible labels. Both techniques can be used simultaneously.
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