Single Image Plankton 3D Reconstruction from Extended Depth of Field Shadowgraph

Abstract

Marine plankton occurs in the ocean with strongly varying degrees of sparsity. For in-situ plankton measurements the shadowgraph has been established as the observation device of choice in recent years. In this paper a novel depth from defocus based approach to partially coherent 3D reconstruction of marine plankton volumes is presented. With a combination of recent advances in coherent image restoration and deep learning, we create a 3D view of the shadowgraph observation volume. For the selection of in-focus images we develop a novel training data generation technique. This kind of reconstruction was previously only possible with holographic imaging systems, which require laser illumination with high coherence, which often causes parasitic interferences on optical components and speckles. The new 3D visualization gives easily manageable data by resulting in a sharp view of each plankton together with its depth and position. Moreover, this approach allows the creation of all-in-focus images of larger observation volumes, which is otherwise impossible due to the physically limited depth of field. We show an effective increase in depth of field by a factor of 7, which allows marine researchers to use larger observation volumes and thus a much more effective observation of marine plankton.