Hyperspectral Image Recovery via Hybrid Regularization

Arablouei, Reza; Hoog, Frank de

doi:10.1109/tip.2016.2614131

Cited by 16 publications

(7 citation statements)

References 48 publications

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“…We utilized the well-known forward observation model together with the linear mixture model to cast the fusion problem as a reduceddimension linear inverse problem. We used a vector totalvariation penalty as well as nonnegativity and sum-to-one constraints on the endmember abundances to regularize the 9 We used MATLAB with a 2.9GHz Core-i7 CPU and 24GB of DDR3 RAM and ran each of the proposed, HySure, and R-FUSE-TV algorithms for 200 iterations as they always converged sufficiently after this number of iterations. associated maximum-likelihood estimation problem.…”

Section: Discussionmentioning

confidence: 99%

Fusing Multiple Multiband Images

Arablouei

2018

J. Imaging

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We consider the problem of fusing an arbitrary number of multiband, i.e., panchromatic, multispectral, or hyperspectral, images belonging to the same scene. We use the well-known forward observation and linear mixture models with Gaussian perturbations to formulate the maximum-likelihood estimator of the endmember abundance matrix of the fused image. We calculate the Fisher information matrix for this estimator and examine the conditions for the uniqueness of the estimator. We use a vector total-variation penalty term together with nonnegativity and sum-to-one constraints on the endmember abundances to regularize the derived maximum-likelihood estimation problem. The regularization facilitates exploiting the prior knowledge that natural images are mostly composed of piecewise smooth regions with limited abrupt changes, i.e., edges, as well as coping with potential ill-posedness of the fusion problem. We solve the resultant convex optimization problem using the alternating direction method of multipliers. We utilize the circular convolution theorem in conjunction with the fast Fourier transform to alleviate the computational complexity of the proposed algorithm. Experiments with multiband images constructed from real hyperspectral images reveal the superior performance of the proposed algorithm in comparison with the state-of-the-art algorithms, which need to be used in tandem to fuse more than two multiband images.  Index Terms-multiband image fusion; alternating direction method of multipliers; total variation; Cramer-Rao lower bound; maximum likelihood; linear mixture model. 1

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Section: Discussionmentioning

confidence: 99%

Fusing Multiple Multiband Images

Arablouei

2018

J. Imaging

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“…For the considered regularization method, the quality of the reconstruction, as well as the convergence of the CGNR algorithm, are related to the condition number of the matrix to invert in eq. (5). It can be shown [29] that using orthonormal masks, with the additional requirement that each acquisition has the same number of open mirrors, ensures a smaller condition number for this matrix compared to random masks.…”

Section: A2 Orthonormalitymentioning

confidence: 99%

“…These hyperspectral cubes are useful for many applications, such as satellite imaging, remote sensing, medicine and food industry. Despite the large variety of scenes, hyperspectral cubes are often highly correlated and sparse in structure [1,2], and this sparsity can be exploited in post processing to compress or de-noise the datacube [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Optimized coded aperture for frugal hyperspectral image recovery using a dual-disperser system

et al. 2020

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We present a novel acquisition scheme based on a dual disperser architecture, which can reconstruct a hyperspectral data cube using many times fewer acquisitions than spectral bands. The reconstruction algorithm follows a quadratic regularization approach, based on the assumption that adjacent pixels in the scene share similar spectra, and if they do not, this corresponds to an edge which is detectable on the panchromatic image. A digital micro-mirror device (DMD) applies reconfigurable spectral-spatial filtering to the scene for each acquisition, and the filtering code is optimized considering the physical properties of the system. The algorithm is tested on simple multi-spectral scenes with 110 wavelength bands, and is able to accurately reconstruct the hyperspectral datacube using only 10 acquisitions.

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“…So, some prior knowledge of X is needed to regularize the super-resolution problem. In HSI processing, the spectral sparsity is a widespread regularizer applied to solve varieties of ill-posed problems [55][56][57][58]. In such regularization, spectral vectors are linearly combined by a small quantity of different spectral signatures.…”

Section: E Proposed Sttf-based Sr Algorithmmentioning

confidence: 99%

Key-Frame Detection and Super-Resolution of Hyperspectral Video via Sparse-Based Cumulative Tensor Factorization

Zhou

Wang

et al. 2020

Mathematical Problems in Engineering

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Thanks to the rapid development of hyperspectral sensors, hyperspectral videos (HSV) can now be collected with high temporal and spectral resolutions and utilized to handle invisible dynamic monitoring missions, such as chemical gas plume tracking. However, using such sequential large-scale data effectively is challenged, because the direct process of these data requires huge demands in terms of computational loads and memory. This paper presents a key-frame and target-detecting algorithm based on cumulative tensor CANDECOMP/PARAFAC (CP) factorization (CTCF) to select the frames where the target shows up, and a novel super-resolution (SR) method using sparse-based tensor Tucker factorization (STTF) is used to improve the spatial resolution. In the CTCF method, the HSV sequence is seen as cumulative tensors and the correlation of adjacent frames is exploited by applying CP tensor approximation. In the proposed STTF-based SR method, we consider the HSV frame as a third-order tensor; then, HSV frame super-resolution problem is transformed into estimations of the dictionaries along three dimensions and estimation of the core tensor. In order to promote sparse core tensors, a regularizer is incorporated to model the high spatial-spectral correlations. The estimations of the core tensor and the dictionaries along three dimensions are formulated as sparse-based Tucker factorizations of each HSV frame. Experimental results on real HSV data set demonstrate the superiority of the proposed CTCF and STTF algorithms over the comparative state-of-the-art target detection and SR approaches.

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Hyperspectral Image Recovery via Hybrid Regularization

Cited by 16 publications

References 48 publications

Fusing Multiple Multiband Images

Fusing Multiple Multiband Images

Optimized coded aperture for frugal hyperspectral image recovery using a dual-disperser system

Key-Frame Detection and Super-Resolution of Hyperspectral Video via Sparse-Based Cumulative Tensor Factorization

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