Both 3D imaging and hyperspectral imaging provide important information of the scene and combining them is beneficial in helping us perceive and understand real-world structures. Previous hyperspectral 3D imaging systems typically require a hyperspectral imaging system as the detector suffers from complicated hardware design, high cost, and high acquisition and reconstruction time. Here, we report a low-cost, high-frame rate, simple-design, and compact hyperspectral stripe projector (HSP) system based on a single digital micro-mirror device, capable of producing hyperspectral patterns where each row of pixels has an independently programmable spectrum. We demonstrate two example applications using the HSP via hyperspectral structured illumination: hyperspectral 3D surface imaging and spectrum-dependent hyperspectral compressive imaging of volume density of participating medium. The hyperspectral patterns simultaneously encode the 3D spatial and spectral information of the target, requiring only a grayscale sensor as the detector. The reported HSP and its applications provide a solution for combining structured illumination techniques with hyperspectral imaging in a simple, efficient, and low-cost manner. The work presented here represents a novel structured illumination technique that provides the basis and inspiration of future variations of hardware systems and software encoding schemes.

Hyperspectral imaging in optical microscopy is of importance in the study of various submicron physical and chemical phenomena. However, its practical application is still challenging because the additional spectral dimension increases the number of sampling points to be independently measured compared to two-dimensional (2D) imaging. Here, we present a hyperspectral microscopy system through passive illumination approach based on compressive sensing (CS) using a spectrometer with a one-dimensional (1D) detector array and a digital micromirror device (DMD). The illumination is patterned after the sample rather than on it, making this technique compatible with both dark-field and bright-field imaging. The DMD diffraction issue resulting from this approach has been overcome by a novel striped DMD pattern modulation method. In addition, a split pattern method is developed for increasing the spatial resolution when employing the DMD pattern modulation. The efficacy of the system is demonstrated on nanoparticles using two model systems: extended plasmonic metal nanostructures and fluorescent microspheres. The compressive hyperspectral microscopic system provides a fast, high dynamic range, and enhanced signal-to-noise ratio (SNR) platform that yields a powerful and low-cost spectral analytical system to probe the optical properties of a myriad of nanomaterial systems. The system can also be extended to wavelengths beyond the visible spectrum with greatly reduced expense compared to other approaches that use 2D array detectors.

Computer vision with a single-pixel camera is currently limited by a trade-off between reconstruction capability and image classification accuracy. If random projections are used to sample the scene, then reconstruction is possible but classification accuracy suffers, especially in cases with significant background signal. If data-driven projections are used, then classification accuracy improves and the effect of the background is diminished, but image recovery is not possible. Here, we employ a shallow neural network to nonlinearly convert from measurements acquired with random patterns to measurements acquired with data-driven patterns. The results demonstrate that this improves classification accuracy while still allowing for full reconstruction.

Metallic nanoparticles (NPs) capable of sustaining localized surface plasmon resonances (LSPRs) are the key component for many applications ranging from photocatalysis to biomedical treatment [1]. Single-particle scattering spectroscopy techniques like dark-field (DF) hyperspectral imaging have become a key tool for studying the optical properties of these NPs because of the wealth of information that can be obtained from the resulting spectral-spatial datacubes [2]. Unfortunately, the data acquisition process for this technique is notoriously slow, since long integration times are needed at each position to ensure sufficient signal to noise ratio (SNR); a range of parallel acquisition schemes have been suggested to overcome this limitation such as the "push-broom" technique which has been implemented with both point-scan and faster line-scan methodologies [3]. However, recent work demonstrates further speed improvements through the use of a compressive sensing (CS) imaging system [4]. In this work we explore some of the factors limiting performance for a spectrally-modulating hyperspectral CS microscope. Our results show that for applications to single-particle scattering experiments, the hyperspectral CS microscope requires particularly careful selection of exposure and gain settings to balance intensity resolution against SNR.

Hyperspectral microscopy is an optical characterization technique vital to the understanding of nanomaterials. Because hyperspectral datacubes are so large, however, their acquisition is often cumbersome and highly timeconsuming. Although acquiring hyperspectral datacubes via spectral scanning is highly efficient, it is difficult to extend this technique to large numbers of spectral bands due to the low light levels caused by narrowband filters and various mechanical difficulties involved in the use of large filter wheels. Both problems can be circumvented, however, by using a DMD to perform spectral multiplexing, as the multiplexing advantage gives high light levels and a single DMD can be used for arbitrary spectral programming, thereby removing the need for large filter wheels. In this work, a DMD-based, brightfield, spectrally multiplexing microscope for investigations of two-dimensional materials is demonstrated. We show the effectiveness of this microscope by rapidly measuring the contrast spectra of few-layer graphene and MoS 2 to determine their thickness. The measured contrast spectra are then compared to their theoretical curves for validation.