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Leonidas Spinoulas

Bio: Leonidas Spinoulas is an academic researcher from University of Southern California. The author has contributed to research in topics: Compressed sensing & Iterative reconstruction. The author has an hindex of 13, co-authored 39 publications receiving 629 citations. Previous affiliations of Leonidas Spinoulas include Rambus & National Technical University of Athens.

Papers
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Journal ArticleDOI
TL;DR: In this article, a deep learning framework for video compressive sensing is presented, which enables recovery of video frames in a few seconds at significantly improved reconstruction quality compared to previous approaches.

147 citations

Journal ArticleDOI
TL;DR: A prototype compressive video camera is presented that encodes scene movement using a translated binary photomask in the optical path, and the use of a printed binary mask allows reconstruction at higher spatial resolutions than has been previously demonstrated.
Abstract: We present a prototype compressive video camera that encodes scene movement using a translated binary photomask in the optical path. The encoded recording can then be used to reconstruct multiple output frames from each captured image, effectively synthesizing high speed video. The use of a printed binary mask allows reconstruction at higher spatial resolutions than has been previously demonstrated. In addition, we improve upon previous work by investigating tradeoffs in mask design and reconstruction algorithm selection. We identify a mask design that consistently provides the best performance across multiple reconstruction strategies in simulation, and verify it with our prototype hardware. Finally, we compare reconstruction algorithms and identify the best choice in terms of balancing reconstruction quality and speed.

74 citations

Journal ArticleDOI
TL;DR: In this article, compressive sensing has been developed for single-pixel optical cameras to significantly reduce the imaging time and at the same time produce high-fidelity images by exploiting the sparsity of the data in some transform domain.
Abstract: Passive millimeter-wave (PMMW) imagers using a single radiometer, called single pixel imagers, employ raster scanning to produce images. A serious drawback of such a single pixel imaging system is the long acquisition time needed to produce a high-fidelity image, arising from two factors: (a) the time to scan the whole scene pixel by pixel and (b) the integration time for each pixel to achieve adequate signal to noise ratio. Recently, compressive sensing (CS) has been developed for single-pixel optical cameras to significantly reduce the imaging time and at the same time produce high-fidelity images by exploiting the sparsity of the data in some transform domain. While the efficacy of CS has been established for single-pixel optical systems, its application to PMMW imaging is not straightforward due to its (a) longer wavelength by three to four orders of magnitude that suffers high diffraction losses at finite size spatial waveform modulators and (b) weaker radiation intensity, for example, by eight orders of magnitude less than that of infrared. We present the development and implementation of a CS technique for PMMW imagers and shows a factor-of-ten increase in imaging speed.

69 citations

Proceedings ArticleDOI
29 Dec 2011
TL;DR: A novel passive millimeter-wave (PMMW) imaging system designed using compressive sensing principles is presented, and a Bayesian reconstruction algorithm is developed to estimate the original image from these measurements, where the sparsity inherent to typical PMMW images is efficiently exploited.
Abstract: In this paper, we present a novel passive millimeter-wave (PMMW) imaging system designed using compressive sensing principles. We employ randomly encoded masks at the focal plane of the PMMW imager to acquire incoherent measurements of the imaged scene. We develop a Bayesian reconstruction algorithm to estimate the original image from these measurements, where the sparsity inherent to typical PMMW images is efficiently exploited. Comparisons with other existing reconstruction methods show that the proposed reconstruction algorithm provides higher quality image estimates. Finally, we demonstrate with simulations using real PMMW images that the imaging duration can be dramatically reduced by acquiring only a few measurements compared to the size of the image.

61 citations

Journal ArticleDOI
TL;DR: A novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images that relies on a constrained optimization technique, and allows the incorporation of existing CS reconstruction algorithms in compressive BID problems.
Abstract: We propose a novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images. The proposed framework relies on a constrained optimization technique, which is solved by a sequence of unconstrained sub-problems, and allows the incorporation of existing CS reconstruction algorithms in compressive BID problems. As an example, a non-convex lp quasi-norm with 0 <; p <; 1 is employed as a regularization term for the image, while a simultaneous auto-regressive regularization term is selected for the blur. Nevertheless, the proposed approach is very general and it can be easily adapted to other state-of-the-art BID schemes that utilize different, application specific, image/blur regularization terms. Experimental results, obtained with simulations using blurred synthetic images and real passive millimeter-wave images, show the feasibility of the proposed method and its advantages over existing approaches.

58 citations


Cited by
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Journal ArticleDOI
TL;DR: This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data Fusion.
Abstract: The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

1,797 citations

Journal Article
TL;DR: Methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data are described and dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification.
Abstract: We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification when the learning includes a class separability criteria in the objective function. The benefits of dictionary learning clearly show that a proper understanding of causes underlying the sensed world is key to task-specific representation of relevant information in high-dimensional data sets.

705 citations

Journal ArticleDOI
TL;DR: Active metamaterials have been used to realize terahertz imaging with a single-pixel detector Compressive techniques permit high-fidelity images to be acquired at high frame rates as discussed by the authors.
Abstract: Active metamaterials have been used to realize terahertz imaging with a single-pixel detector Compressive techniques permit high-fidelity images to be acquired at high frame rates The technique involves no moving parts and yields improved signal-to-noise ratios over standard raster scanning techniques

695 citations

Journal ArticleDOI
TL;DR: The popular neural network architectures used for imaging tasks are reviewed, offering some insight as to how these deep-learning tools can solve the inverse problem.
Abstract: Traditionally, analytical methods have been used to solve imaging problems such as image restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and deep learning have gained a lot of momentum in solving such imaging problems, often surpassing the performance provided by analytical approaches. Unlike analytical methods for which the problem is explicitly defined and domain-knowledge carefully engineered into the solution, deep neural networks (DNNs) do not benefit from such prior knowledge and instead make use of large data sets to learn the unknown solution to the inverse problem. In this article, we review deep-learning techniques for solving such inverse problems in imaging. More specifically, we review the popular neural network architectures used for imaging tasks, offering some insight as to how these deep-learning tools can solve the inverse problem. Furthermore, we address some fundamental questions, such as how deeplearning and analytical methods can be combined to provide better solutions to the inverse problem in addition to providing a discussion on the current limitations and future directions of the use of deep learning for solving inverse problem in imaging.

496 citations

Journal ArticleDOI
TL;DR: Two versions of a novel deep learning architecture are proposed, dubbed as ADMM-CSNet, by combining the traditional model-based CS method and data-driven deep learning method for image reconstruction from sparsely sampled measurements, which achieved favorable reconstruction accuracy in fast computational speed compared with the traditional and the other deep learning methods.
Abstract: Compressive sensing (CS) is an effective technique for reconstructing image from a small amount of sampled data. It has been widely applied in medical imaging, remote sensing, image compression, etc. In this paper, we propose two versions of a novel deep learning architecture, dubbed as ADMM-CSNet, by combining the traditional model-based CS method and data-driven deep learning method for image reconstruction from sparsely sampled measurements. We first consider a generalized CS model for image reconstruction with undetermined regularizations in undetermined transform domains, and then two efficient solvers using Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing the model are proposed. We further unroll and generalize the ADMM algorithm to be two deep architectures, in which all parameters of the CS model and the ADMM algorithm are discriminatively learned by end-to-end training. For both applications of fast CS complex-valued MR imaging and CS imaging of real-valued natural images, the proposed ADMM-CSNet achieved favorable reconstruction accuracy in fast computational speed compared with the traditional and the other deep learning methods.

470 citations