8,740 research outputs found
Exploiting Structural Complexity for Robust and Rapid Hyperspectral Imaging
This paper presents several strategies for spectral de-noising of
hyperspectral images and hypercube reconstruction from a limited number of
tomographic measurements. In particular we show that the non-noisy spectral
data, when stacked across the spectral dimension, exhibits low-rank. On the
other hand, under the same representation, the spectral noise exhibits a banded
structure. Motivated by this we show that the de-noised spectral data and the
unknown spectral noise and the respective bands can be simultaneously estimated
through the use of a low-rank and simultaneous sparse minimization operation
without prior knowledge of the noisy bands. This result is novel for for
hyperspectral imaging applications. In addition, we show that imaging for the
Computed Tomography Imaging Systems (CTIS) can be improved under limited angle
tomography by using low-rank penalization. For both of these cases we exploit
the recent results in the theory of low-rank matrix completion using nuclear
norm minimization
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Hyperspectral images (HSIs) are often corrupted by a mixture of several types
of noise during the acquisition process, e.g., Gaussian noise, impulse noise,
dead lines, stripes, and many others. Such complex noise could degrade the
quality of the acquired HSIs, limiting the precision of the subsequent
processing. In this paper, we present a novel tensor-based HSI restoration
approach by fully identifying the intrinsic structures of the clean HSI part
and the mixed noise part respectively. Specifically, for the clean HSI part, we
use tensor Tucker decomposition to describe the global correlation among all
bands, and an anisotropic spatial-spectral total variation (SSTV)
regularization to characterize the piecewise smooth structure in both spatial
and spectral domains. For the mixed noise part, we adopt the norm
regularization to detect the sparse noise, including stripes, impulse noise,
and dead pixels. Despite that TV regulariztion has the ability of removing
Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian
noise for some real-world scenarios. Then, we develop an efficient algorithm
for solving the resulting optimization problem by using the augmented Lagrange
multiplier (ALM) method. Finally, extensive experiments on simulated and
real-world noise HSIs are carried out to demonstrate the superiority of the
proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure
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