Whale Optimization Algorithm-based Parallel Computing for Accelerating Misalignment Estimation of Reflective Fourier Ptychography Microscopy

Reflective Fourier ptychographic microscopy has much potential for industrial surface inspection due to the ability to overcome the physical limits of the numerical aperture of the optical microscopy. However, the time cost for misalignment calibration and Fourier ptychography (FP) recovery has been a big issue for industrial applications which require fast output. Here, we introduce a misalignment estimation method which is accelerated through the whale optimization algorithm by running in parallel on Central Processing Units (CPUs), named pWOA, to reduce computing time. The proposed method shows more accurate and faster calibration compared to other population-based algorithms, including the parallel genetic algorithm and the parallel particle swarm optimization, and much faster than that of the exhaustive search both in simulations and in real experiments. In addition, this cost-effective technique can address global non-convex optimization problems with heavy loss functions including reflective FP.