Suspended particles play a significant role in aquatic systems. However, existing methods to probe suspended particles have several limitations. In this paper, we present a portable prototype to in situ probe individual particles in aquatic suspensions by simultaneously measuring polarized light scattering and fluorescence, aiming to obtain an effective classification of microplastics and microalgae. Results show that the obtained classification accuracy is significantly higher than that for either of these two methods. The setup also successfully measures submicron particles and discriminates two species of Synechococcus. Our study demonstrates the feasibility of simultaneously measuring polarized light scattering and fluorescence, and the promising capability of our method for further aquatic environmental monitoring.

Cyanobacterial bloom is one of the most urgent global environmental issues, which eventually could threaten human health and safety. Sonication treatment (ST) is a potential effective method to control cyanobacteria blooms in the field. Currently, the bottleneck of extensive application of ST is the difficulty to estimate the ST effect on the cyanobacterial cells and then determine suitable ST times in the field. In this study, cyanobacterial Microcystis samples sonicated at different times were first measured by a spectrophotometer to calculate the removal efficiency of Microcystis cells. Additionally, they were observed by TEM to reveal the intracellular structure changes of the cells. Then the samples were measured by an experimental setup based on polarized light scattering to measure the polarization parameters. Experimental results indicated that the polarization parameters can effectively characterize the intracellular structural changes of Microcystis cells with different ST times, which is quite consistent with the results for removal efficiency and TEM images. Further, the optimal ST time can be inferred by the polarization parameters. These results demonstrate that polarized light scattering can be a potentially powerful tool to explore suitable times for sonication treatment of cyanobacteria blooms.

Microalgae play a vital role in aquatic ecological research, but the fine classification of these tiny and various microalgae cells is still challenging for the community. In this paper, we propose a multimodality technique to simultaneously acquire the polarized light scattering, fluorescence and laser-induced acoustic wave signals originated from individual microalgae cells in water. Experiments of different species of Spirulina and different states of Microcystis have been conducted to test our experiment setup, and the results demonstrate that this method can well discriminate microalgae cells with pigment or microstructural differences. Moreover, with these modalities, the consumption of absorbed energy is evaluated quantitively, and a possible way to assess photosynthesis on a single-cell level is presented. This work is expected to be a powerful technique to probe the biophysical states of microalgae in the aquatic ecosystem.

Suspended particles play a vital role in aquatic environments. We propose a method to rapidly measure the scattered polarization parameters of individual suspended particles with continuously large angular range (PCLAR), from 60° to 120° in one shot. A conceptual setup is built to measure PCLAR with 20 kHz; to verify the setup, 10 μm-diameter silica microspheres suspended in water, whose PCLAR are consistent with those simulated by Mie theory, are measured. PCLAR of 6 categories of particles are measured, which enables high-accuracy classification with the help of a convolutional neural network algorithm. PCLAR of different mixtures of Cyclotella stelligera and silica microspheres are measured to successfully identify particulate components. Furthermore, classification ability comparisons of different angular-selection strategies show that PCLAR enables the best classification beyond the single angle, discrete angles and small-ranged angles. Simulated PCLAR of particles with different size, refractive index, and structure show explicit discriminations between them. Inversely, the measured PCLAR are able to estimate the effective size and refractive index of individual Cyclotella cells. Results demonstrate the method’s power, which intrinsically takes the advantage of the optical polarization and the angular coverage. Future prototypes based on this concept would be a promising biosensor for particles in environmental monitoring.

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the SpatialTemporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https: //vlis2022.github.io/cvpr23/egvsr.