Using Infrared Photothermal Heterodyne Imaging to Characterize Micro- and Nanoplastics in Complex Environmental Matrices.

A key challenge for addressing micro- and nanoplastics (MNPs) in the environment is being able to characterize their chemical properties, morphologies, and quantities in complex matrices. Current techniques, such as Fourier transform infrared spectroscopy, provide these broad characterizations but are unsuitable for studying MNPs in spectrally congested or complex chemical environments. Here, we introduce a new, super-resolution infrared absorption technique to characterize MNPs, called infrared photothermal heterodyne imaging (IR-PHI). IR-PHI has a spatial resolution of ∼300 nm and can determine the chemical identity, morphology, and quantity of MNPs in a single analysis with high sensitivity. Specimens are supported on CaF2 coverslips under ambient conditions from where we (1) quantify MNPs from nylon tea bags after steeping in ultrapure water at 25 and 95 °C, (2) identify MNP chemical or morphological changes after steeping at 95 °C, and (3) chemically identify MNPs in sieved road dust. In all cases, no special sample preparation was required. MNPs released from nylon tea bags at 25 °C were fiber-like and had characteristic IR frequencies corresponding to thermally extruded nylon. At 95 °C, degradation of the nylon chemical structure was observed via the disappearance of amide group IR frequencies, indicating chain scission of the nylon backbone. This degradation was also observed through morphological changes, where MNPs altered shape from fiber-like to quasi-spherical. In road dust, IR-PHI analysis reveals the presence of numerous aggregate and single-particle (<3 µm) MNPs composed of rubber and nylon.

Deep image restoration for infrared photothermal heterodyne imaging.

Infrared photothermal heterodyne imaging (IR-PHI) is an all-optical table top approach that enables super-resolution mid-infrared microscopy and spectroscopy. The underlying principle behind IR-PHI is the detection of photothermal changes to specimens induced by their absorption of infrared radiation. Because detection of resulting refractive index and scattering cross section changes is done using a visible (probe) laser, IR-PHI exhibits a spatial resolution of ∼300 nm. This is significantly below the mid-infrared diffraction limit and is unlike conventional infrared absorption microscopy where spatial resolution is of order ∼5µm. Despite having achieved mid-infrared super-resolution, IR-PHI's spatial resolution is ultimately limited by the visible probe laser's diffraction limit. This hinders immediate application to studying samples residing in spatially congested environments. To circumvent this, we demonstrate further enhancements to IR-PHI's spatial resolution using a deep learning network that addresses the Abbe diffraction limit as well as background artifacts, introduced by experimental raster scanning. What results is a twofold improvement in feature resolution from 300 to ∼150 nm.

Adaptive whitening of ambient ocean noise with narrowband signal preservation.

Passive underwater listening devices are often deployed to listen for narrowband signals of interest in time-varying background ocean noise. Such tonals are generated mechanically by ships, submarines, and machines, or acoustically by aquatic wildlife. Quantization of the sensor data for storage or low bit-rate transmission adds white noise which can overwhelm weak narrowband signals if the background noise is sufficiently colored. Whitening the background noise prior to quantization can reduce the detrimental effects, but the whitening process must preserve any tonals in the signal for maximum effectiveness. Existing adaptive whitening techniques make no effort to avoid suppressing tonals in the whitening process, while existing spectral separation methods fail to whiten background noise. The proposed methods perform adaptive whitening of background ambient noise while preserving narrowband tones at their original signal-to-noise ratios. The proposed methods are shown to outperform combinations of existing partial solutions both subjectively and by evaluating the objective criteria introduced. The stability and convergence properties of the proposed algorithms match or surpass those of existing well-known adaptive algorithms.

Multimodal Image Registration With Line Segments by Selective Search.

This paper proposes a line segment-based image registration method. Edges are detected from images by a modified Canny operator, and line segments are then extracted from these edges. At registration, triplets (quaternions) of line segment correspondences are tentatively formed by applying the distance and orientation constraints, which determine an intermediate transformation. Those triplets (quaternions) of lines resulting in higher similarity metrics are preserved, and their intersections are refined by an iterative process or random sample consensus. The proposed method is tested on indoor and outdoor EO/IR image pairs, and the average registration error is calculated to be compared with existing algorithms. Experimental results show that the proposed registration method can robustly align EO/IR images containing line segments, providing more reliable and accurate registration results on multimodal images.

Studentized dynamical system for robust object tracking.

This paper describes a studentized dynamical system (SDS) for robust target tracking using a subspace representation. Dynamical systems (DS) provide a powerful framework for the probabilistic modeling of temporal sequences. Visual tracking problems are often cast as a sequential inference problem within the DS framework and a compact way to model the observation distributions (i.e., object appearances) is through probabilistic principal component analysis (PPCA). PPCA is a classic Gaussian based subspace representation method and a popular tool for appearance modeling. Although Gaussian density has theoretically nice properties, resulting in models that are always tractable, they are also severely limited in practical settings. One of the central issues in the use of PPCA for target appearance modeling is that it is very sensitive to outliers. The Gaussian density has a very light tail, while real world data with outliers exhibit heavy tails. Recently, more heavy-tailed distributions (e.g., Student's t-distribution) have been introduced to increase the robustness of the original PPCA. We propose to augment the traditional target tracking DS by adding a set of auxiliary latent variables to adjust the shape of the observation distribution. We show that by carefully choosing the probability density of these auxiliary latent variables, a more robust observation distribution can be obtained with tails heavier than Gaussian. Numerical experiments verify that the proposed SDS has a better capability to handle considerable amount of outlier noise and an improved tracking performance over DS with a Gaussian based observation model.