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We propose a speed-up method for the in-focus plane detection in digitalholographic microscopy that can be applied to a broad class of autofocusingalgorithms that involve repetitive propagation of an object wave to variousaxial locations to decide the in-focus position. The classical autofocusingalgorithms apply a uniform search strategy, i.e., they probe multiple,uniformly distributed axial locations, which leads to heavy computationaloverhead. Our method substantially reduces the computational load, withoutsacrificing the accuracy, by skillfully selecting the next location toinvestigate, which results in a decreased total number of probed propagationdistances. This is achieved by applying the golden selection search withparabolic interpolation, which is the gold standard for tacklingsingle-variable optimization problems. The proposed approach is successfullyapplied to three diverse autofocusing cases, providing up to 136-fold speed-up.

Versatile optimization-based speed-up method for autofocusing in digital holographic microscopy