Evaluating automated reconstruction methods for digital inline holographic images of plankton
Holographic reconstruction algorithms based on wave propagation require the object's Z-plane location. The location is determined manually by selecting an image from a set of reconstructed images over a range of Z-planes. We evaluate five autofocus metrics; the standard deviation of Laplacian and Sobel edge detectors, sum of darkest 2% of pixels, sum of the difference of adjacent reconstructed images (DAMP method), and product of the variance of two orthogonal Gabor filters. The metrics were tested on ten classes of plankton collected from field deployments of a submersible digital holographic imaging system (HOLOCAM). Our results indicate that Gabor filters provide the best focus metric performance, correctly predicting focus distance with +/-100 um for 78% of the images (n=687). The performance of each metric is significantly dependent on the plankton class, from 46% for the round Coscinodiscus class to 100% for the Thalassionema nitzschoid class using the Gabor focus metric. Focus metric waveform analysis provides a prediction confidence to eliminate images likely to produce erroneous Z predictions. Applying focus metrics to reconstructed image segments substantially containing the object greatly improves the performance of the DAMP method. While Gabor filters are the most computationally intensive focus metric evaluated, the Gabor focus metric curves are relatively smooth and unimodal, enabling iterative search methods to reduce the number of reconstructions required to determine focus.