143 research outputs found
A convex formulation for hyperspectral image superresolution via subspace-based regularization
Hyperspectral remote sensing images (HSIs) usually have high spectral
resolution and low spatial resolution. Conversely, multispectral images (MSIs)
usually have low spectral and high spatial resolutions. The problem of
inferring images which combine the high spectral and high spatial resolutions
of HSIs and MSIs, respectively, is a data fusion problem that has been the
focus of recent active research due to the increasing availability of HSIs and
MSIs retrieved from the same geographical area.
We formulate this problem as the minimization of a convex objective function
containing two quadratic data-fitting terms and an edge-preserving regularizer.
The data-fitting terms account for blur, different resolutions, and additive
noise. The regularizer, a form of vector Total Variation, promotes
piecewise-smooth solutions with discontinuities aligned across the
hyperspectral bands.
The downsampling operator accounting for the different spatial resolutions,
the non-quadratic and non-smooth nature of the regularizer, and the very large
size of the HSI to be estimated lead to a hard optimization problem. We deal
with these difficulties by exploiting the fact that HSIs generally "live" in a
low-dimensional subspace and by tailoring the Split Augmented Lagrangian
Shrinkage Algorithm (SALSA), which is an instance of the Alternating Direction
Method of Multipliers (ADMM), to this optimization problem, by means of a
convenient variable splitting. The spatial blur and the spectral linear
operators linked, respectively, with the HSI and MSI acquisition processes are
also estimated, and we obtain an effective algorithm that outperforms the
state-of-the-art, as illustrated in a series of experiments with simulated and
real-life data.Comment: IEEE Trans. Geosci. Remote Sens., to be publishe
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
Non-Local Total Variation (NLTV) has emerged as a useful tool in variational
methods for image recovery problems. In this paper, we extend the NLTV-based
regularization to multicomponent images by taking advantage of the Structure
Tensor (ST) resulting from the gradient of a multicomponent image. The proposed
approach allows us to penalize the non-local variations, jointly for the
different components, through various matrix norms with .
To facilitate the choice of the hyper-parameters, we adopt a constrained convex
optimization approach in which we minimize the data fidelity term subject to a
constraint involving the ST-NLTV regularization. The resulting convex
optimization problem is solved with a novel epigraphical projection method.
This formulation can be efficiently implemented thanks to the flexibility
offered by recent primal-dual proximal algorithms. Experiments are carried out
for multispectral and hyperspectral images. The results demonstrate the
interest of introducing a non-local structure tensor regularization and show
that the proposed approach leads to significant improvements in terms of
convergence speed over current state-of-the-art methods
Image Fusion in Remote Sensing and Quality Evaluation of Fused Images
In remote sensing, acquired optical images of high spectral resolution have usually a lower spatial resolution than images of lower spectral resolution. This is due to physical, cost and complexity constraints. To make the most of the available imagery, many image fusion techniques have been developed to address this problem. Image fusion is an ill-posed inverse problem where an image of low spatial resolution and high spectral resolution is enhanced in spatial-resolution by using an auxiliary image of high spatial resolution and low spectral resolution. It is assumed that both images display the same scene and are properly co-registered. Thus, the problem is essentially to transfer details from the higher spatial resolution auxiliary image to the upscaled lower resolution image in a manner that minimizes the spatial and spectral distortion of the fused image. The most common image fusion problem is pansharpening, where a multispectral (MS) image is enhanced using wide-band panchromatic (PAN) image. A similar problem is the enhancement of a hyperspectral (HS) image by either a PAN image or an MS image. As there is no reference image available, the reliable quantitative evaluation of the quality of the fused image is a difficult problem. This thesis addresses the image fusion problem in three different ways and also addresses the problem of quantitative quality evaluation.Í fjarkönnun hafa myndir með háa rófsupplausn lægri rúmupplausn en myndir með lægri rófsupplausn vegna eðlisfræðilegra og kostnaðarlegra takmarkana. Til að auka upplýsingamagn
slĂkra mynda hafa veriĂ° ĂľrĂłaĂ°ar fjölmargar sambræðsluaĂ°ferĂ°ir á sĂĂ°ustu
tveimur áratugum. Myndsambræðsla er illa framsett andhverft vandmál (e. inverse
problem) þar sem rúmupplausn myndar af hárri rófsupplausn er aukin með þvà að
nota upplýsingar frá mynd af hárri rúmupplausn og lægri rófsupplausn. Það er gert
ráð fyrir aĂ° báðar myndir sĂ˝ni nákvæmlega sama landsvæði. Ăžannig er vandamáliĂ° Ă
eĂ°li sĂnu aĂ° flytja fĂngerĂ°a eiginleika myndar af hærri rĂşmupplausn yfir á mynd af lægri
rúmupplausn sem hefur verið brúuð upp à stærð hinnar myndarinnar, án þess að skerða
gæði rĂłfsupplĂ˝singa upphaflegu myndarinnar. Algengasta myndbræðsluvandamáliĂ° Ă
fjarkönnun er svokölluð panskerpun (e. pansharpening) þar sem fjölrásamynd (e. multispectral
image) er endurbætt Ă rĂşmi meĂ° svokallaĂ°ri vĂĂ°bandsmynd (e. panchromatic
image) sem hefur aðeins eina rás af hárri upplausn. Annað svipað vandamál er sambræðsla
háfjölrásamyndar (e. hyperspectral image) og annaðhvort fjölrásamyndar eða
vĂĂ°bandsmyndar. Ăžar sem myndsambræðsla er andhverft vandmál er engin háupplausnar
samanburðarmynd tiltæk, sem gerir mat á gæðum sambræddu myndarinnar
að erfiðu vandamáli. Í þessari ritgerð eru kynntar þrjár aðferðir sem taka á myndsambræðlsu
og einnig er fjallað um mat á gæðum sambræddra mynda, þá sérstaklega
panskerptra mynda
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)
Dimensionality reduction for hyperspectral data
This thesis is about dimensionality reduction for hyperspectral data. Special emphasis is given to dimensionality reduction techniques known as kernel eigenmap methods and manifold learning algorithms. Kernel eigenmap methods require a nearest neighbor or a radius parameter be set. A new algorithm that does not require these neighborhood parameters is given. Most kernel eigenmap methods use the eigenvectors of the kernel as coordinates for the data. An algorithm that uses the frame potential along with subspace frames to create nonorthogonal coordinates is given. The algorithms are demonstrated on hyperspectral data. The last two chapters include analysis of representation systems for LIDAR data and motion blur estimation, respectively
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