An adaptive learning method for face hallucination using Locality Preserving Projections

Peng, Silong; Jiang, Jing

doi:10.1109/afgr.2008.4813394

Cited by 27 publications

(14 citation statements)

References 13 publications

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“…Different types of learning based SR algorithms are discussed in the following sub-sections. Table 9 Reported Hallucination works [4], [57], [58], [71], [82], [85], [99], [100], [127], [133], [142], [154], [165], [187], [208], [235], [241], [250], [281], [285], [327] [340], [341], [343], [344], [354], [360], [372], [373], [379], [382], [394], [396], [400], [402], [403], [404], [409], [425], [430], [434], [435], [442], [443], [455], [456], [457], [458], [462]…”

Section: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

“…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%

“…Among the most common databases reviewed (shown in Table 15), only CAS-PEAL facial database provides such a data. Beside the list shown in Table 14, the following facial databases have been used in at least one system: Cohn-Kanade face database [208], NUST [323], [349], BioID [394], [400], Terrascope [299], Asian Face Database PF01 [327], FG-NET Database [341], Korean Face Database [355], Max Planck Institute Face Database [355], IMM Face Database [382], PAL [396], [455], USC-SIPI [489], [503], [577], Georgia Tech [407], [523], ViD-TIMIT [400], FRI CVL [480], Face96 [480], FEI face database [571], MBGC face and iris database [585], SOFTPIA Japan Face Database [605].…”

Section: Databasesmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

586

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: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

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%

Section: Databasesmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

586

292

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“…6: Compute the weights w[k, l, i, j, t] using equation (13). 7: Solve the optimization problem in (14) to getα. 8: Generate the HR patch x = D hα ; and put the patch x + m into a HR image X 0 .…”

mentioning

confidence: 99%

Super Resolution Reconstruction via Multiple Frames Joint Learning

Wang

Xuan

et al. 2011

2011 International Conference on Multimedia and Signal Processing

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This paper presents a novel multi-frame joint learning approach for image super resolution via sparse representation. Based on the assumption that several low-resolution patches degraded from a same high-resolution patch under subpixel translation can preserve similar structures, we can use those similar low-resolution patches together to recover the sparse coefficients for the corresponding high-resolution patch, and the differences between them can help to supply more information. So, unlike the learning-based super resolution algorithm from single image which uses one patch in the learning process, we take into consideration some other well matched patches in 3D domain. Computer simulations demonstrate that, comparing with those single frame learning algorithms, our method will not only restore more details but also can effectively overcome the over learning and is more robust to noise.

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