Imaging via Compressive Sampling

By Justin K. Romberg. IEEE Signal Processing Magazine, 2008

1,025 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/95b6b465d0a7b3f72230fb0620409948821f7482

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization (2009, methodology): “We start by reviewing prior related work, following by the presentation of our proposed algorithm and the first results showing the computational and reconstruction advantages of this new approach.”
Compressive Sensing with structured random matrices (2012, background): “Although we will not cover specific applications in these notes, let us mention that compressive sensing may be applied in imaging [44, 109], A/D conversion [133], radar [69, 49] and wireless communication [126, 95], to name a few.”
SPARSE RECONSTRUCTION FOR SAR IMAGING BASED ON COMPRESSED SENSING (2010, neutral): “[9–11].”
Sampling Content from Online Social Networks (2015, background): “RELATED WORK There have been a number of studies on sampling This section brieﬂy reviews some of these studies. been extensively studied in signal processing and information theory literature [Candes and Wakin 2008; Kellogg 2006; Romberg 2008].”
A Novel Strategy for Radar Imaging Based on Compressive Sensing (2008, neutral): “a computational load of about 30 or 50 times that of solving a least-square problem of equivalent dimensions [20].”
High-resolution hyperspectral imaging via matrix factorization (2011, methodology): “The proposed method can efficiently obtain very accurate spectrum estimates with a significant resolution gap between the high-resolution RGB camera and low-resolution hyperspectral camera.”
Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing (2012, methodology): “Compressive sensing provides a novel framework for recovering sparse or compressible signals with far fewer noisy measurements than that needed by the Nyquist sampling theorem [16], [17], [18].”
Direction-of-Arrival Estimation Using a Mixed $\ell _{2,0}$ Norm Approximation (2010, background): “Digital Object Identifier 10.1109/TSP.2010.2050477 these issues in this paper by using a rather different framework [10], where we recover a joint-sparse signal from multiple measurement vectors (MMV) [11], [12].”