  We propose a speed-up method for the in-focus plane detection in digital
holographic microscopy that can be applied to a broad class of autofocusing
algorithms that involve repetitive propagation of an object wave to various
axial locations to decide the in-focus position. The classical autofocusing
algorithms apply a uniform search strategy, i.e., they probe multiple,
uniformly distributed axial locations, which leads to heavy computational
overhead. Our method substantially reduces the computational load, without
sacrificing the accuracy, by skillfully selecting the next location to
investigate, which results in a decreased total number of probed propagation
distances. This is achieved by applying the golden selection search with
parabolic interpolation, which is the gold standard for tackling
single-variable optimization problems. The proposed approach is successfully
applied to three diverse autofocusing cases, providing up to 136-fold speed-up.
