Browsing by Author "Xu, Yibo"
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Item A hyperspectral projector for simultaneous 3D spatial and hyperspectral imaging via structured illumination(Optical Society of America, 2020) Xu, Yibo; Giljum, Anthony; Kelly, Kevin F.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.Item Compressive Hyperspectral Imaging and Machine Vision(2019-11-21) Xu, Yibo; Kelly, Kevin F.Hyperspectral imaging is a challenging task given the high dimensionality of data and the limitations of conventional sensing scheme and detector design. Yet, it has great potential in studying optical phenomena in both science and engineering, and in both microscopic and macroscopic systems. Simultaneously, machine vision is an important field with a wide range of real-world applications. There has been constant effort to improve the accuracy and efficiency of machine vision implementations. The field of compressive sensing and its ability to exploit the inherent sparsity of a majority of natural images have the potential to make a tremendous impact on both of these fields. As such, the first part of this thesis describes the design and implementation of a compressive hyperspectral microscope that can capture and analyze different properties of metallic nanoparticles, fluorescent microspheres and two-dimensional materials. In relation to macroscale imaging, a hyperspectral projector system is developed and implemented as discussed in the middle portion of this thesis. It enhances conventional structured illumination methods by incorporating hyperspectral compressive measurements. Lastly, a general and efficient dynamic-rate training scheme for neural networks is developed and implemented that specifically exploits compressive measurements. The approach is capable of performing classification over a range of measurement rates directly on compressive measurements acquired by a single-pixel camera architecture bypassing image reconstruction. Since the input layer of the network is designed to couple with a single sensor, this approach is also compatible with a compressive hyperspectral imager. Overall, the results in this thesis presents many novel ways in which compressive sensing can greatly benefit both hyperspectral imaging and machine vision tasks.Item Compressive Hyperspectral Microscopy of Scattering and Fluorescence of Nanoparticles(American Chemical Society, 2022) Xu, Yibo; Lu, Liyang; Giljum, Anthony; Payne, Courtney M.; Hafner, Jason H.; Ringe, Emilie; Kelly, Kevin F.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.Item Compressive Hyperspectral Structured Illumination and Classification via Neural Networks(2016-04-21) Xu, Yibo; Kelly, Kevin F.We demonstrate two complementary applications based on compressive imaging: hyperspectral compressive structured illumination for three-dimensional imaging and compressive classification of objects using neural networks. The structured light method usually uses structured patterns generated from a commercial digital projector which contain very limited spectral content, using white light or RGB-based giving very little material content and not exploiting possible wavelength-dependent scattering. Therefore we designed and implemented a hyperspectral projector system that is able to generate structured patterns consisting of arbitrarily defined spectrum instead. We used the system to recover the unique spectrum-dependent 3-D volume density of the colored targets of participating media. For the image classification problem, it is known that a set of images of a fixed scene under varying articulation parameters forms a low-dimensional, nonlinear manifold that random projections can stably embed using far fewer measurements. Thus random projections in compressive sampling can be regarded as a dimension-reducing process. We demonstrate a method using compressive measurements of images to train a neural network that has a relatively simple architecture for object classification. As a proof of concept, simulations were performed on infrared vehicle images that demonstrated the utility of this approach over previous compressive matched filtering. The success of both these projects bodes well for their overall integration into a single infrared compressive hyperspectral machine-vision instrument.