Compressive hyperspectral imaging using progressive total variation

Kuiteing, Simeon Kamdem; Coluccia, Giulio; Barducci, Alessandro; Barni, Mauro; Magli, Enrico

doi:10.1109/icassp.2014.6855117

Cited by 12 publications

(14 citation statements)

References 20 publications

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“…Compared to the existing RPCA model [23], [27], it can be clearly observed that the constraint of the rank of the clean image being no larger than the number of endmembers is added in model (18). The rank of the matrix X, which is an intrinsic feature of an HSI image, is very meaningful in the HSI restoration process.…”

Section: A Low-rank Matrix Factorization-based Hsi Restorationmentioning

confidence: 99%

“…In [17], a spatial spectral TV approach was used for HSI restoration. In [18], Kuiteing et al proposed an iterative TV architecture for HSI reconstruction. Taking the spectral noise differences and the spatial information differences into consideration, Yuan et al [19] proposed an HSI restoration algorithm employing a spectral spatial adaptive TV (SSAHTV) model.…”

Section: Introductionmentioning

confidence: 99%

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Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration

Wei

Zhang

et al. 2016

IEEE Trans. Geosci. Remote Sensing

553

301

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In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In general, HSIs are not only assumed to lie in a low-rank subspace from the spectral perspective but also assumed to be piecewise smooth in the spatial dimension. The proposed method integrates the nuclear norm, TV regularization, and L 1 -norm together in a unified framework. The nuclear norm is used to exploit the spectral low-rank property, and the TV regularization is adopted to explore the spatial piecewise smooth structure of the HSI. At the same time, the sparse noise, which includes stripes, impulse noise, and dead pixels, is detected by the L 1 -norm regularization. To tradeoff the nuclear norm and TV regularization and to further remove the Gaussian noise of the HSI, we also restrict the rank of the clean image to be no larger than the number of endmembers. A number of experiments were conducted in both simulated and real data conditions to illustrate the performance of the proposed LRTV method for HSI restoration.Index Terms-Hyperspectral image (HSI), low-rank matrix factorization, rank constraint, restoration, total variation (TV).

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Section: A Low-rank Matrix Factorization-based Hsi Restorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration

Wei

Zhang

et al. 2016

IEEE Trans. Geosci. Remote Sensing

553

301

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show abstract

“…We compared our results to those achieved by the iterative total variation algorithm (ITV) introduced in Ref. 37 In order to test the performance of the algorithm, acquisition and reconstruction of hyperspectral images from the AVIRIS Yellowstone dataset proposed in Ref. 2 were simulated.…”

Section: Resultsmentioning

confidence: 98%

“…A method that relies on separate sensing of spectral rows and that is compatible with onboard systems with a pushbroom configuration was proposed in Ref. 37. Last, hyperspectral imagers based on a single pixel camera architecture were introduced in Refs.…”

Section: Introductionmentioning

confidence: 99%

Hybrid tenso-vectorial compressive sensing for hyperspectral imaging

Bernal

2016

J. Electron. Imaging

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Hyperspectral imaging has a wide range of applications relying on remote material identification, including astronomy, mineralogy, and agriculture; however, due to the large volume of data involved, the complexity and cost of hyperspectral imagers can be prohibitive. The exploitation of redundancies along the spatial and spectral dimensions of a hyperspectral image of a scene has created new paradigms that overcome the limitations of traditional imaging systems. While compressive sensing (CS) approaches have been proposed and simulated with success on already acquired hyperspectral imagery, most of the existing work relies on the capability to simultaneously measure the spatial and spectral dimensions of the hyperspectral cube. Most real-life devices, however, are limited to sampling one or two dimensions at a time, which renders a significant portion of the existing work unfeasible. We propose a new variant of the recently proposed serial hybrid vectorial and tensorial compressive sensing (HCS-S) algorithm that, like its predecessor, is compatible with reallife devices both in terms of the acquisition and reconstruction requirements. The newly introduced approach is parallelizable, and we abbreviate it as HCS-P. Together, HCS-S and HCS-P comprise a generalized framework for hybrid tenso-vectorial compressive sensing, or HCS for short. We perform a detailed analysis that demonstrates the uniqueness of the signal reconstructed by both the original HCS-S and the proposed HCS-P algorithms. Last, we analyze the behavior of the HCS reconstruction algorithms in the presence of measurement noise, both theoretically and experimentally. IntroductionHyperspectral imaging refers to the process of using specialized sensors to collect image information across the electromagnetic (EM) spectrum, often beyond the visible wavelength range. As commonly understood, hyperspectral sensors sample the portion of the EM spectrum that extends from the visible part of the spectrum to near-infrared and beyond, in multiple narrow and continuous bands. A hyperspectral image (see Fig. 1) can be represented as a third-order or three-dimensional data cube, the face of which is a function of the spatial coordinates and the depth of which is a function of spectral bands. 1 The hyperspectral cube from Fig. 1 was acquired by JPL's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) on August 20, 1992, when it was flown on a NASA ER-2 plane at an altitude of 20,000 m (65,000 feet) over Moffett Field, California. 2 Hyperspectral data provide a wealth of information about the scene being imaged; consequently, there is a wide range of remote sensing problems that rely on extracting the spectral signature of objects in the scene. These applications include environmental mapping, 3 global change research, 4 assessment of trafficability, 5 plant and material identification, 6 crop analysis, 7 astronomy and space surveillance, 8 mineralogy, 9 agriculture, 10 healthcare 11 and surveillance, 12 among others.The significant amount of data (three tensorial...

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“…The acquisition is iterated while changing the pattern of the spatial modulator, thus measuring several elements of the involved integral transform. Information regarding the signal estimation algorithms developed in the framework of the Project have been discussed in [14]. Some preliminary measurements for the experimental assessment of CS performance and the related reconstruction errors are also shown.…”

Section: Introductionmentioning

confidence: 99%