Non-stationary approximate Bayesian super-resolution using a hierarchical prior model

Woods, N.A.; Galatsanos, N.P.

doi:10.1109/icip.2005.1529681

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…The above affine photometric model can only handle small photometric changes, therefore it has been extended to a non-linear model in [289], which also [113], [115], [116], [122], [128], [133], [138], [147], [149], [170], [174], [177], [181], [186], [187], [188], [189], [199], [204], [209], [215], [216], [217], [218], [223], [224], [226], [227], [228], [230], [231], [235], [242], [246], [248], [249], [250], [251], [252], [258], [260], [270], [272], [276], [277], [279], [280], …”

Section: Imaging Modelsmentioning

confidence: 99%

“…which can be rewritten aŝ [113], [118], [123], [124], [125], [129], [163], [184], [188], [189], [216], [251], [252], [253], [312], [279], [291], [406], [450] [104], [105], [107], [108], [109], [110], [112], [123], [127], [129], [133], [134], [135], [137], [138], [140], [149], [152], [157], [160], [161], [163], [165], [170], [181], [184], [187], [199], [209], [215], [217]...…”

Section: Maximum a Posteriorimentioning

confidence: 99%

“…-Non-stationary SAR [215] in which the variance of the SAR prediction can be different from one location in the image to another. -Soft edge smoothness a priori, which estimates the average length of all level lines in an intensity image [279], [406].…”

Section: Bilateral Total Variation (Btv) [125] (2003) (mentioning

confidence: 99%

“…- [189], [190], [213], [298], [299], [322], [323], [338], [339], [349], [433], [453], [469], [480], [510], [537], [133], [150], [151], [179], [160], [162], [174], [207], [209], [210], [215], [216], [217], [223], [231], [237], [241], [247], [251], [252], [273], [281], [285], [345], [308], [309], [317], [323], [326], [331], [344], [349], [353], [36...…”

Section: Assessment Of Sr Algorithmsunclassified

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

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: Imaging Modelsmentioning

confidence: 99%

Section: Maximum a Posteriorimentioning

confidence: 99%

Section: Bilateral Total Variation (Btv) [125] (2003) (mentioning

confidence: 99%

Section: Assessment Of Sr Algorithmsunclassified

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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“…This task that requires the filling-in of missing information at even and odd lines of subsequent video frames is referred to as deinterlacing [3,31,32]. It is closely related to image or video inpainting [2,10,11] where missing information at arbitrary locations has to be restored and video superresolution [13,21,31,36] that aims at improving both spatial and vertical resolution by combing information from several consecutive frames of an image sequence.…”

Section: Introductionmentioning

confidence: 99%

Deinterlacing with Motion-Compensated Anisotropic Diffusion

Ghodstinat

Bruhn

Weickert

2009

Lecture Notes in Computer Science

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Abstract. We present a novel deinterlacing scheme that makes consequent use of discontinuity-preserving partial differential equations (PDEs). It combines the accuracy of recent variational motion estimation techniques with the directional interpolation qualities of anisotropic diffusion filters. Our algorithm proceeds in three steps: First, we interpolate the interlaced images by means of a spatial edge enhancing diffusion process (EED). Then we apply the variational optic flow technique of Brox et al. (2004) in order to obtain a precise interframe registration. Finally we use a spatiotemporal generalisation of EED for motion-compensated inpainting of the missing data in the original sequence. Experiments demonstrate that the proposed method outperforms not only classical deinterlacing schemes, but also a recent PDE-based approach.

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