Estimation of the parameters in regularized simultaneous super-resolution

Zibetti, Marcelo V. W.; Bazán, Fermín S. V.; Mayer, Joceli

doi:10.1016/j.patrec.2009.12.009

Cited by 19 publications

(17 citation statements)

References 33 publications

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“…For example, in [10] [113], [217], [224], [293], [295], [315], [328], [331], [429], [432], [468], [512], [529], [551] a MAP method has been used for simultaneous reconstruction of the super-resolved image and registration of the LR images. In these cases, the MAP formulation of Eq.…”

Section: Simultaneous Methodsmentioning

confidence: 99%

“…In [606] the TV terms are weighted with an adaptive spatial algorithm based on differences in the curvature. Table 8), which is used to approximate TV, is defined by: [123], [277], [328], [363], [365], [368], [388], [414], [500], [547], [551], [ [123], [133], [149], [253], [260], [272], [276], [282], [313], [314], [328], [375], [428], [452], [588] GMRF [123], [129], [163], [181], [209], [276], [282], [305], [496], [535], [555], [574], [575], [598] TV [73], [84], [124], [157], [245], …”

Section: Markov Random Fields (Mrf)mentioning

confidence: 99%

See 1 more Smart Citation

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

572

292

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Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new superresolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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Section: Simultaneous Methodsmentioning

confidence: 99%

Section: Markov Random Fields (Mrf)mentioning

confidence: 99%

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

572

292

View full text Add to dashboard Cite

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“…The borders of the lines that converge to the centre of the image present aliasing artefacts in all other methods but in current. Estimation of the Parameters in Regularized Simultaneous SR (Zibetti et al, 2010). A method for automatic determination of the regularization parameters for the class of simultaneous SR algorithms is based on the joint maximum a posteriori (JMAP) estimation technique.…”

Section: Modelling Simulation and Identiication 114mentioning

confidence: 99%

Super-Resplution Procedures in Images and Video Sequences Based on Wavelet Atomic Functions

Ponomaryov¹,

Gomeztagle²

2010

Modelling, Simulation and Identification

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“…The limited accuracy inherent to HR registration from LR images is a shortcoming of this first approach. The second approach is to alternate between HR image registration and HR image estimation (see [5,[16][17][18][19][20][21][22][23][24][25]). …”

Section: Introductionmentioning

confidence: 99%

“…Hyperparameter estimation is also a critical issue, which has been studied in previous SR papers, see for instance [5,6,10,21,25]. In this paper the entire SR problem, that is HR image reconstruction, registration, and hyperparameter estimation, is approached from a Bayesian perspective.…”

Section: Introductionmentioning

confidence: 99%

Bayesian combination of sparse and non-sparse priors in image super resolution

Villena¹,

Vega²,

Babacan³

et al. 2013

Digital Signal Processing

104

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In this paper the application of image prior combinations to the Bayesian Super Resolution (SR) image registration and reconstruction problem is studied. Two sparse image priors, a Total Variation (TV) prior and a prior based on the 1 norm of horizontal and vertical first order differences (f.o.d.), are combined with a non-sparse Simultaneous Auto Regressive (SAR) prior. Since, for a given observation model, each prior produces a different posterior distribution of the underlying High Resolution (HR) image, the use of variational approximation will produce as many posterior approximations as priors we want to combine. A unique approximation is obtained here by finding the distribution on the HR image given the observations that minimizes a linear convex combination of Kullback-Leibler (KL) divergences. We find this distribution in closed form. The estimated HR images are compared with the ones obtained by other SR reconstruction methods.

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Estimation of the parameters in regularized simultaneous super-resolution

Cited by 19 publications

References 33 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

Super-Resplution Procedures in Images and Video Sequences Based on Wavelet Atomic Functions

Bayesian combination of sparse and non-sparse priors in image super resolution

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