Super-resolution reconstruction of hyperspectral images

Akgun, Toygar; Altunbasak, Y.; Mersereau, R.M.

doi:10.1109/tip.2005.854479

Cited by 221 publications

(151 citation statements)

References 27 publications

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“…an ill-posed problem. To make the image recovery process less ill-posed (Akgun, 2005), (Dong, 2011), Equation (1), can be rewritten as the least squares formulation…”

Section: Bidimensional Image Super-resolutionmentioning

confidence: 99%

“…Hence, researchers have investigated the use of resolution enhancement techniques by post processing as a better alter native to improve the image quality (Akgun, 2005), (Mianji, 2008), (Zhao, 2011). To improve the spatial resolution of hyperspectral images, traditional super-resolution (SR) techniques may be used.…”

Section: Introductionmentioning

confidence: 99%

“…Super-resolution methods are currently a very active area of research, as it allows the implementation of low-cost imaging sensors in hyperspectral spectrometers. Many methodologies have been applied to the super-resolution problem, such as SR from single image methods (Zhang, 2012), (Takeda, 2007), (Li, 2001), multi-image-based SR methods (Protter, 2009), (Akgun, 2005), (Chan, 2010), and learning-based SR methods (Freeman, 2002). However, these methods ignore the spectral information that is a crucial parameter for an optimal reconstruction of high resolution (HR) images.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods ignore the spectral information that is a crucial parameter for an optimal reconstruction of high resolution (HR) images. A comprehensive background on super-resolution methods can be found in (Akgun, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Compact spatio-spectral algorithm for single image super-resolution in hyperspectral imaging

2016

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Hyperspectral imaging (HSI) is used in a wide range of applications such as remote sensing, space imagery, mineral detection, and exploration. Unfortunately, it is difficult to acquire hyperspectral images with high spatial and spectral resolution due to instrument limitations. The super-resolution techniques are used to reconstruct low-resolution hyperspectral images. However, traditional superresolution (SR) approaches do not allow direct use of both spatial and spectral information, which is a decisive for an optimal reconstruction. This paper proposes a single image SR algorithm for HSI. The algorithm uses the fact that the spatial and spectral information can be integrated to make an accurate estimate of the high-resolution HSI. To achieve this, two types of spatio-spectral downsampling, and a three-dimensional interpolation are proposed in order to increase coherence between the spatial and spectral information. The resulting reconstructions using the proposed method are up to 2 dB better than traditional SR approaches. Keywords:Hyperspectral imaging, spatio-spectral dimension, three-dimensional interpolation, hyperspectral downsampling. RESUMENLas imágenes hiperespectrales (HSI) son de vital importancia en una amplia gama de aplicaciones, tales como la teledetección, imágenes espaciales, la detección y la exploración de minerales. Desafortunadamente, es difícil adquirir HSI de alta resolución espacio-espectral debido a las limitaciones de los equipos de sensado. Para obtener versiones de HSI de alta calidad se usan técnicas tradicionales de superresolución. Éstas técnicas no permiten el uso directo de la información espacial y espectral que son un factor decisivo para una óptima reconstrucción. En este trabajo se propone la implementación de un novedoso algoritmo de superresolución de una sola imagen hiperespectral. El algoritmo integra la información espacial y espectral en las HSI para realizar una estimación precisa de alta resolución. Esta integración se obtiene mediante el uso de dos tipos de muestreo espacio-espectral y un interpolador tridimensional, que permite aumentar la coherencia de la información inherente en la imagen. Las imágenes resultantes son superiores hasta 2 dB comparas con reconstrucciones obtenidas por enfoques tradicionales.Palabras clave: Imágenes hiperespectrales, dimensión especial-espectral, interpolación tridimensional, sub-muestreo hiperespectral.

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“…an ill-posed problem. To make the image recovery process less ill-posed (Akgun, 2005), (Dong, 2011), Equation (1), can be rewritten as the least squares formulation…”

Section: Bidimensional Image Super-resolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compact spatio-spectral algorithm for single image super-resolution in hyperspectral imaging

2016

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“…Previous works have used this image to illustrate and compare unmixing strategies for hyperspectral images [17]. The region of interest is a 50 × 50 image represented in Fig.…”

Section: Real Hyperspectral Imagesmentioning

confidence: 99%

Variational methods for spectral unmixing of hyperspectral images

Eches¹,

Dobigeon²,

Tourneret³

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper studies a variational Bayesian unmixing algorithm for hyperspectral images based on the standard linear mixing model. Each pixel of the image is modeled as a linear combination of endmembers whose corresponding fractions or abundances are estimated by a Bayesian algorithm. This approach requires to define prior distributions for the parameters of interest and the related hyperparameters. After defining appropriate priors for the abundances (uniform priors on the interval (0, 1)), the joint posterior distribution of the model parameters and hyperparameters is derived. The complexity of this distribution is handled by using variational methods that allow the joint distribution of the unknown parameters and hyperparameter to be approximated. Simulation results conducted on synthetic and real data show similar performances than those obtained with a previously published unmixing algorithm based on Markov chain Monte Carlo methods, with a significantly reduced computational cost.Index Terms-Bayesian inference, variational methods, spectral unmixing, hyperspectral images.

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