Iteratively-reweighted local model fitting method for adaptive and accurate single-shot surface profiling

摘要

The local model fitting (LMF) method is one of the useful single-shot surface profiling algorithms. The measurement principle of the LMF method relies on the assumption that the target surface is locally flat. Based on this assumption, the height of the surface at each pixel is estimated from pixel values in its vicinity. Therefore, we can estimate flat areas of the target surface precisely, whereas the measurement accuracy could be degraded in areas where the assumption is violated, because of a curved surface or sharp steps. In this paper, we propose to overcome this problem by weighting the contribution of the pixels according to the degree of satisfaction of the locally flat assumption. However, since we have no information on the surface profile beforehand, we iteratively estimate it and use this estimation result to determine the weights. This algorithm is named the iteratively-reweighted LMF (IRLMF) method. Experimental results show that the proposed algorithm works excellently.