Browsing by Author "Sun, Ting"
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Item A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing(2011-01) Li, Chengbo; Sun, Ting; Kelly, Kevin; Zhang, YinHyperspectral data processing typically demands enormous computational resources in terms of storage, computation and I/O throughputs, especially when real-time processing is desired. In this paper, we investigate a low-complexity scheme for hyperspectral data compression and reconstruction. In this scheme, compressed hyperspectral data are acquired directly by a device similar to the single-pixel camera based on the principle of compressive sensing. To decode the compressed data, we propose a numerical procedure to directly compute the unmixed abundance fractions of given endmembers, completely bypassing high-complexity tasks involving the hyperspectral data cube itself. The reconstruction model is to minimize the total variational of the abundance fractions subject to a pre-processed fidelity equation with a significantly reduced size, and other side constraints. An augmented Lagrangian type algorithm is developed to solve this model. We conduct extensive numerical experiments to demonstrate the feasibility and efficiency of the proposed approach, using both synthetic data and hardware-measured data. Experimental and computational evidence obtained from this study indicates that the proposed scheme has a high potential in real-world applications.Item Apparatus and method for compressive imaging and sensing through multiplexed modulation(2015-09-01) Kelly, Kevin F.; Baraniuk, Richard G.; Woods, Gary; Sun, Ting; Turner, Matthew; Rice University; United States Patent and Trademark OfficeCompressive imaging apparatus employing multiple modulators in various optical schemes to generate the modulation patterns before the signal is recorded at a detector. The compressive imaging apparatus is equally valid when applying compressive imaging to structured light embodiments where the placement is shifted from the acquisition path between the subject and the detector into the illumination path between the source and the subject to be imaged.Item Apparatus and method for compressive imaging and sensing through multiplexed modulation via spinning disks(2016-12-13) Kelly, Kevin F.; Baraniuk, Richard G.; Woods, Gary; Sun, Ting; Turner, Matthew; Rice University; United States Patent and Trademark OfficeCompressive imaging apparatus employing multiple modulators in various optical schemes to generate the modulation patterns before the signal is recorded at a detector. The compressive imaging apparatus is equally valid when applying compressive imaging to structured light embodiments where the placement is shifted from the acquisition path between the subject and the detector into the illumination path between the source and the subject to be imaged.Item Compressive Sensing and Imaging Applications(2012) Sun, Ting; Kelly, Kevin F.Compressive sensing (CS) is a new sampling theory which allows reconstructing signals using sub-Nyquist measurements. It states that a signal can be recovered exactly from randomly undersampled data points if the signal exhibits sparsity in some transform domain (wavelet, Fourier, etc). Instead of measuring it uniformly in a local scheme, signal is correlated with a series of sensing waveforms. These waveforms are so called sensing matrix or measurement matrix. Every measurement is a linear combination of randomly picked signal components. By applying a nonlinear convex optimization algorithm, the original can be recovered. Therefore, signal acquisition and compression are realized simultaneously and the amount of information to be processed is considerably reduced. Due to its unique sensing and reconstruction mechanism, CS creates a new situation in signal acquisition hardware design as well as software development, to handle the increasing pressure on imaging sensors for sensing modalities beyond visible (ultraviolet, infrared, terahertz etc.) and algorithms to accommodate demands for higher-dimensional datasets (hyperspectral or video data cubes). The combination of CS with traditional optical imaging extends the capabilities and also improves the performance of existing equipments and systems. Our research work is focused on the direct application of compressive sensing for imaging in both 2D and 3D cases, such as infrared imaging, hyperspectral imaging and sum frequency generation microscopy. Data acquisition and compression are combined into one step. The computational complexity is passed to the receiving end, which always contains sufficient computer processing power. The sensing stage requirement is pushed to the simplest and cheapest level. In short, simple optical engine structure, robust measuring method and high speed acquisition make compressive sensing-based imaging system a strong competitor to the traditional one. These applications have and will benefit our lives in a deeper and wider way.Item Near-infrared imaging and optical microscopy by compressive sensing(2009) Sun, Ting; Kelly, Kevin F.Given its important role, factors such as sensitivity, resolution, dwell time, and bandwidth limit are critical parameters for detectors in modern optical imaging. A new method known as compressive sensing has emerged, which greatly improves the imaging resolution of these detectors. In our configuration, a digital micromirror device randomly but controllably modulates the light before it is collected at the detector. This process simultaneously compresses the signal because the measurement projects the signal onto a white-noise basis. Subsequently, the data from this incoherent basis is reconstructed into a complete real-space image. Given its compressive nature, far fewer measurements are required than the total number of pixels which greatly decreases the acquisition time of the signal. In addition, the intensity of the compressed signal at the detector is much greater than its raster scan counterpart and therefore results in greater signal sensitivity and improved image quality. These advantages make compressive sensing particularly attractive for use in single-detector near-infrared imaging and white light optical microscopy.Item Single-pixel imaging via compressive sampling(2008-03-01) Duarte, Marco F.; Davenport, Mark A.; Takhar, Dharmpal; Laska, Jason N.; Sun, Ting; Kelly, Kevin F.; Baraniuk, Richard G.