Browsing by Author "Li, Yun"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Imaging and Visual Classification by Knowledge-Enhanced Compressive Imaging
    (2015-09-10) Li, Yun; Kelly, Kevin F.; Baraniuk , Richard G.; Landes , Christy F.
    Compressive imaging is a technology that uses multiplexed measurements and the sparsity of many natural images to efficiently capture and reconstruct images. The compressive single pixel camera is one embodiment of such an imaging system and has proven capable of imaging static images, dynamic scenes, and entire hyperspectral datacubes using fewer measurements than the current schemes. However, for many imaging tasks prior information or models exists and when incorporated in the compressive measurement can greatly improve reconstructed result. In this thesis, we illustrate and quantify through simulation and experiment the effectiveness of knowledge-enhanced patterns over unbiased compressive measurements in a variety of applications including motion tracking, anomaly detection, and object recognition. In the case of motion tracking, one might interest in moving foreground. Given prior information about the moving foreground in the scene, we propose the design of patterns for foreground imaging. Then one can recover the dynamic scene through combining moving foreground from designed patterns and static background. We also implemented anomaly detection from compressive measurements. A set of detection criteria is implemented and proven to be effective. On top of that, we also introduced patterns selected from partial-complete set according to the geometric information of the anomaly point, which later shows improved effectiveness comparing with random patterns. For image classification, we implemented two methods to generate secant projections, which are optimized to preserve the difference between image classes. Lastly we illustrate the new design of single pixel based hyperspectral design. To reach that, the control of DMD chip and optics of SPC have been improved. Also we show results about implementation of compressive endmembers unmixing scheme for compressive sum frequency generation hyperspectral imaging system.
  • Thumbnail Image
    Item
    Model-Based Acquisition for Compressive Sensing & Imaging
    (2013-09-16) Li, Yun; Kelly, Kevin F.; Baraniuk, Richard G.; Yin, Wotao
    Compressive sensing (CS) is a novel imaging technology based on the inherent redundancy of natural scenes. The minimum number of required measurements which defines the maximum image compression rate is lower-bounded by the sparsity of the image but is dependent on the type of acquisition patterns employed. Increased measurements by the Rice single pixel camera (SPC) slows down the acquisition process, which may cause the image recovery to be more susceptible to background noise and thus limit CS's application in imaging, detection, or classifying moving targets. In this study, two methods (hybrid-subspace sparse sampling (HSS) for imaging and secant projection on a manifold for classification are applied to solving this problem. For the HSS method, new image pattern are designed via robust principle component analysis (rPCA) on prior knowledge from a library of images to sense a common structure. After measuring coarse scale commonalities, the residual image becomes sparser, and then fewer measurements are needed. For the secant projection case, patterns that can preserve the pairwise distance between data points based on manifold learning are designed via semi-definite programming. These secant patterns turn out to be better in object classification over those learned from PCA. Both methods considerably decrease the number of required measurements for each task when compared with the purely random patterns of a more universal CS imaging system.
  • Thumbnail Image
    Item
    Video Compressive Sensing for Spatial Multiplexing Cameras Using Motion-Flow Models
    (SIAM, 2015) Sankaranarayanan, Aswin C.; Xu, Lina; Studer, Christoph; Li, Yun; Kelly, Kevin F.; Baraniuk, Richard G.
    Spatial multiplexing cameras (SMCs) acquire a (typically static) scene through a series of coded projections using a spatial light modulator (e.g., a digital micromirror device) and a few optical sensors. This approach finds use in imaging applications where full-frame sensors are either too expensive (e.g., for short-wave infrared wavelengths) or unavailable. Existing SMC systems reconstruct static scenes using techniques from compressive sensing (CS). For videos, however, existing acquisition and recovery methods deliver poor quality. In this paper, we propose the CS multiscale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs. Our framework features novel sensing matrices that enable the efficient computation of a low-resolution video preview, while enabling high-resolution video recovery using convex optimization. To further improve the quality of the reconstructed videos, we extract optical-flow estimates from the low-resolution previews and impose them as constraints in the recovery procedure. We demonstrate the efficacy of our CS-MUVI framework for a host of synthetic and real measured SMC video data, and we show that high-quality videos can be recovered at roughly $60\times$ compression.