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Focusing Evaluation for In situ Darkfield Imaging of Marine Plankton

The application of in situ imaging technology has greatly promoted our understanding of marine plankton ecology. However, the out-of-focus blur, which is the main cause of in situ plankton image degradation, leads not only to subsequent recognition difficulty for both human and machine, but also to inaccurate measurement of seawater volume and hence plankton abundance quantification. Therefore, it is necessary to develop methods to evaluate the blur, reserve those high-quality in-focus images for subsequent analysis, and remove blurred ones to cater for limited computation, storage and transmission bandwidth resources in a field deployed system. In this work, we report two algorithms exclusively developed for focusing evaluation of in situ darkfield plankton images that are based on handcrafted and automatic convolution neural network feature extraction, respectively. They complement each other in computational efficiency and evaluation performance, and are expected to be deployed in darkfield imaging systems under different field scenarios with different powering, computing, storage and networking conditions.