A set of image preprocessing approaches are developed for processing plankton images reconstructed from digital holograms. First, a threshold-based algorithm of image segmentation is proposed and applied to extract the regions of plankton from the original digital images. To improve the performance of image segmentation, an appropriate filter is adopted to reduce the background noise from the image and the image gray level is adjusted to enhance the image contrast. Second, we develop a novel and efficient edge detection method purposefully for the binary images. Third, we propose and use a simple chain-code-based algorithm to eliminate the single-pixel branches along the shape boundary, which will help boundary tracing work stably. Then, an algorithm is improved and applied to trace the boundaries of the plankton regions. This algorithm is optimized based on the relationship between two consecutive chain-codes such that it is fast on implementation. Finally, break points of the shape boundary are efficiently detected based on chain-codes and the boundary is represented compactly by a polygon comprised of those points. After images are preprocessed by these approaches, some redundant information of shape is reduced that will accelerate the running speeds of further image processing and aid identification and classification of plankton at species level. We analyze the accuracy and efficiency of our algorithms. The results show that our algorithm of image segmentation has a good performance in accuracy. Our edge detection method also outperforms the commonly used edge detection methods in terms of localization performance and the running time.

Measuring the distribution, characteristics and dynamics of marine microscale plankton and other particulate matter is essential to understand the vertical flux of elements in the marine environment. Digital holographic microscopy is a powerful approach for measuring these and studying their 3-D trajectories in a relatively large observation volume. This article demonstrates a compact, in-line digital holographic microscope that allows large-volume and high-resolution recording of marine particles through combining a continuous wave laser and a short exposure CMOS camera with efficient global shutters. A resolution of better than 10 μm is demonstrated in air and the minimum distinguishable size of targets recorded in water is approximately 20 μm. The maximum volumetric throughput of the setup is 1904 mL/s. The microscope can take motion blur free holograms of particles moving at up to 490 mm/s in theory, and has been tested in the ∼200-mm/s flowing water. The orientation of the measured volume improves the ability of digital holography in profiling sinking rates and active vertical migration. The system was tested onboard a research vessel to record a range of live plankton and other particles. The motion of some samples, including the sinking motion and swimming motion, was analyzed using custom developed image processing software. The experimental results show that the combination of high resolution and a large volume over which motion of sparse-distribution particles can be tracked, can improve the ability to differentiate between different types of marine particle and identify behaviors of live plankton.

It has been known that noise can enhance the temporal regularity of dynamical systems that exhibit a bursting behavior—the phenomenon of coherence resonance. But can the phenomenon be expected for nonbursting chaotic systems? We present a theoretical argument based on the idea of time-scale matching and provide experimental evidence with a chaotic electronic circuit for coherence resonance in nonbursting chaotic systems.

Digital holography is a useful tool to image microscopic particles. Reconstructed holograms give high-resolution shape information that can be used to identify the types of particles. However, the process of reconstructing holograms is computationally intensive and cannot easily keep up with the rate of data acquisition on low-power sensor platforms. In this work, we explore the possibility of performing object clustering on holograms that have not been reconstructed, i.e., images of raw interference patterns, using the latent representations of a deep-learning autoencoder and a self-organizing mapping network in a fully unsupervised manner. We demonstrate this concept on synthetically generated holograms of different shapes, where clustering of raw holograms achieves an accuracy of 94.4%. This is comparable to the 97.4% accuracy achieved using the reconstructed holograms of the same targets. Directly clustering raw holograms takes less than 0.1 s per image using a low-power CPU board. This represents a three-order of magnitude reduction in processing time compared to clustering of reconstructed holograms and makes it possible to interpret targets in real time on low-power sensor platforms. Experiments on real holograms demonstrate significant gains in clustering accuracy through the use of synthetic holograms to train models. Clustering accuracy increased from 47.1% when the models were trained only on the real raw holograms, to 64.1% when the models were entirely trained on the synthetic raw holograms, and further increased to 75.9% when models were trained on the both synthetic and real datasets using transfer learning. These results are broadly comparable to those achieved when reconstructed holograms are used, where the highest accuracy of 70% achieved when clustering raw holograms outperforms the highest accuracy achieved when clustering reconstructed holograms by a significant margin for our datasets.

We consider a common class of dielectric optical microlasing cavities with quadrupolar deformations and address the question of the maximally allowed amount of deformation for both high-Q operation and a high degree of directionality of light emission. Our approach is to compute the probability for light rays to be trapped in the cavity by examining chaotic scattering dynamics in the classical phase space. We develop a dynamical criterion for high-Q operation and introduce a measure to quantify the directionality of the light emission. Our results suggest that high-Q and directionality can be achieved simultaneously in a wide range of the deformation parameter.