Variational posterior distribution approximation in Bayesian super resolution reconstruction of multispectral images

Molina, Rafael; Vega, Miguel; Mateos, Javier; Katsaggelos, Aggelos K.

doi:10.1016/j.acha.2007.03.006

Cited by 56 publications

(61 citation statements)

References 19 publications

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“…Using these initial parameters, the iterative procedure in [12] is used with a flat hyperprior on the hyperparameters to obtain an estimate of the precision parameters of the low-resolution and panchromatic observation models, as well as, the global prior model. The method in [12] also provides a reconstructed multispectral image that will be used as initial estimate in our iterative procedure.…”

Section: Resultsmentioning

confidence: 99%

“…. , b B are first estimated using the variational approximation method described in [12] for the CAR global image model described in Equation (5). This method utilizes the following initial estimates of the hyperparameters g, b 1 , .…”

Section: Resultsmentioning

confidence: 99%

“…In this paper we present a global image model, which enforces the same similarity among all pixels in each image band, and a [12], that is, (we use M ¼ G to denote it)…”

Section: Global and Local Image Modelingmentioning

confidence: 99%

“…Equations (20) and (21) indicate that, for each hyperparameter of the global model, the inverse of the mean of its posterior distribution approximation is a convex combination of the inverse of the mean of its hyperprior distribution [see Equation (12)] and its maximum likelihood estimate. Furthermore, m b , b ¼ 1, .…”

Section: Bayesian Inference and Variational Approximation Of The Postmentioning

confidence: 99%

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Super-Resolution of Multispectral Images

Vega¹,

Mateos²,

Molina³

et al. 2008

The Computer Journal

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In this paper we propose and analyze a globally and locally adaptive super-resolution Bayesian methodology for pansharpening of multispectral images. The methodology incorporates prior knowledge on the expected characteristics of the multispectral images uses the sensor characteristics to model the observation process of both panchromatic and multispectral images and includes information on the unknown parameters in the model in the form of hyperprior distributions. Using real and synthetic data, the pansharpened multispectral images are compared with the images obtained by other pansharpening methods and their quality is assessed both qualitatively and quantitatively.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…In this paper we present a global image model, which enforces the same similarity among all pixels in each image band, and a [12], that is, (we use M ¼ G to denote it)…”

Section: Global and Local Image Modelingmentioning

confidence: 99%

Section: Bayesian Inference and Variational Approximation Of The Postmentioning

confidence: 99%

See 2 more Smart Citations

Super-Resolution of Multispectral Images

Vega¹,

Mateos²,

Molina³

et al. 2008

The Computer Journal

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“…• SAR (Simultaneously Autoregressive) based pansharpening: The method proposed in [13] assumes that each band of a MS image is a degraded (blurred and decimated) version of the original HR MS image, and the PAN image is a linear combination of HR MS image bands. It uses a classical Simultaneous Autoregressive (SAR) model [14] to impose smoothness on the HR MS image.…”

Section: Pansharpeningmentioning

confidence: 99%

Interactive Pansharpening and Active Classification in Remote Sensing

Ruiz

Mateos

Camps‐Valls

et al. 2013

Multimodal Interaction in Image and Video Applications

Self Cite

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This paper presents two multimodal prototypes for remote sensing image classification where user interaction is an important part of the system. The first one applies pansharpening techniques to fuse information from different satellite sensors to obtain a high resolution (HR) multispectral image. Once the HR image has been classified the user can interact with the system to select a class of interest. The pansharpening parameters are then modified to increase the system accuracy for the selected class without deteriorating the performance of the classifier on the other classes. The second prototype utilizes Bayesian modeling and inference to implement active learning and parameter estimation in binary kernel-based multispectral classification problems with possibly infinite dimensional feature spaces. In the prototype we developed three different strategies for selecting the more informative pixel to be included in the training set. In the experimental section, the prototypes are described and applied to two real multispectral image classification problems.

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A Bayesian approach to robust process identification with ARX models

Khatibisepehr

Huang

2012

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com).In the context of process industries, outlying observations mostly represent a large random error resulting from irregular process disturbances, instrument failures, or transmission problems. Statistical analysis of process data contaminated with outliers may lead to biased parameter estimation and plant-model mismatch. The problem of process identification in the presence of outliers has received great attention and a wide variety of outlier identification approaches have been proposed. However, there is a great need to seek for more general solutions and a robust framework to deal with different types of outliers. The main objective of this work is to formulate and solve the robust process identification problem under a Bayesian framework. The proposed solution strategy not only yields maximum a posteriori estimates of model parameters but also provides hyperparameters that determine data quality as well as prior distribution of model parameters. Identification of a simulated continuous fermentation reactor is considered to show the effectiveness and robustness of the proposed Bayesian framework. The advantages of the method are further illustrated through an experimental case study of a pilot-scale continuous stirred tank heater.

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Variational posterior distribution approximation in Bayesian super resolution reconstruction of multispectral images

Cited by 56 publications

References 19 publications

Super-Resolution of Multispectral Images

Super-Resolution of Multispectral Images

Interactive Pansharpening and Active Classification in Remote Sensing

A Bayesian approach to robust process identification with ARX models

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