A fast 2D entropic thresholding method by wavelet decomposition

Compare with 1D grayscale histogram analysis, 2D entropic thresholding makes use of local average as well as pixel gray level. However, it is time consuming to search the threshold vector in 2D histogram. In the paper, a fast algorithm using wavelet decomposition is proposed, with which a set of candidates of vector was first obtained in the decomposed histogram. The optimal threshold vector is then obtained without exhaustive searching. Experimental results have shown that our algorithm not only find the threshold vector as same as Brink's method but also save computation costs in a large degree, using up only 0.53% of processing time taken by the exhaustive searching.