Direct Hallucination: Direct Locality Preserving Projections (DLPP) for Face Super-Resolution

Ahmed, Sulayman; Rao, Nikitha; Ghafoor, Abdul; Sheri, Ahmad Muqeem

doi:10.1109/icacte.2008.198

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Beside the improvement factors reported in the table the following ones have been reported by at least one paper: 9 [572], 10 [373], [520], 12 [599], and 24 [329].…”

Section: K+1mentioning

confidence: 92%

“…Manifold Manifold based methods [151] (2004), [206], [207], [310], [311], [327], [329], [343], [382], [386], [403], [404], [469], [495], [521], [553], [561], [568], [569], [570] assume that the HR and LR images form manifolds with similar local geometries in two distinct feature spaces [343]. Similar to PCA, these methods are also usually used for dimensionality reduction.…”

Section: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

“…-Locality Preserving Projections (LPP) [310], [311], [327], [329], [382], [461], [495]. -Orthogonal Locality Preserving Projections (OLPP) [343].…”

Section: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

“…The main difference between LPP and LLE is that LPP can handle cases which have not been seen during training better than LLE [329]. OLPP can produce orthogonal basis functions and therefore can provide better locality preserving than LPP.…”

Section: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

588

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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“…Beside the improvement factors reported in the table the following ones have been reported by at least one paper: 9 [572], 10 [373], [520], 12 [599], and 24 [329].…”

Section: K+1mentioning

confidence: 92%