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Example-based Super Resolution.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Salvador, Jordi
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2017.
Temas:
Acceso en línea:Texto completo

MARC

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035 |a (OCoLC)959536947  |z (OCoLC)959593230  |z (OCoLC)960833431  |z (OCoLC)965413094  |z (OCoLC)1105177625  |z (OCoLC)1105562521 
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072 7 |a TEC  |x 009070  |2 bisacsh 
082 0 4 |a 621.367  |2 23 
100 1 |a Salvador, Jordi. 
245 1 0 |a Example-based Super Resolution. 
260 |a London :  |b Academic Press,  |c 2017. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references. 
505 0 |a Front Cover; Example-Based Super Resolution; Copyright; Dedication; Contents; List of Figures; Acknowledgment; Introduction; The Super-Resolution Problem; Super-Resolution Approaches; Interpolation; Multiframe Super Resolution; Example-Based Super Resolution; Outline; About the Book; Chapter 1: Classic Multiframe Super Resolution; 1.1 Problem Statement; 1.1.1 A Frequency-Domain Pipeline; 1.2 Bayesian Inference; 1.2.1 Maximum Likelihood; 1.2.2 Maximum A Posteriori; 1.3 Interpolation-Based Methods; 1.3.1 Registration; 1.3.2 Warping Projection; Forward Warping; Backward Warping. 
505 8 |a 1.3.3 RestorationInpainting; Deblurring; Denoising; Iterative Reconstruction; 1.4 Performance Limits; 1.5 Discussion; Chapter 2: A Taxonomy of Example-Based Super Resolution; 2.1 Example-Based Super Resolution; 2.1.1 Parametric Methods; 2.1.2 Nonparametric Methods; 2.2 Internal Learning; 2.2.1 High-Frequency Transfer; 2.2.2 Neighbor Embedding; 2.3 External Learning; 2.3.1 Sparse Coding; 2.3.2 Anchored Regression; 2.3.3 Regression Trees; 2.3.4 Deep Learning; 2.4 Discussion; Chapter 3: High-Frequency Transfer; 3.1 Adaptive Filter Selection; 3.1.1 Parametric Filter Design; Implementation Notes. 
505 8 |a 3.1.2 Performance3.2 Robustness to Aliasing; 3.2.1 Local Regularization; High-Contrast Edge Detection, Dilation and Scaling; Local Denoising; Locally Regularized HF Synthesis; 3.2.2 Performance; 3.3 Robustness to Noise; 3.3.1 In-Place Cross-Scale Self-Similarity; In-place Structure Similarity; Noisy In-place Self-Similarity; 3.3.2 Iterative Noise-Aware Super Resolution; Interpolation; Analysis; Learning; Reconstruction; Implementation Details; 3.3.3 Performance; Processing Time; 3.4 Discussion; Chapter 4: Neighbor Embedding; 4.1 Framework; 4.1.1 Problem Statement. 
505 8 |a 4.1.2 Internal vs. External LearningInternal Learning; External Learning; 4.2 Extensions; 4.2.1 Multiphase Neighbor Embeddings; Pipeline; Complexity; 4.2.2 Nonnegative Least Squares; Features; Method; 4.3 Performance; 4.3.1 Configuration; Internal vs. External Learning; 4.3.2 Benchmark; 4.4 Discussion; Chapter 5: Sparse Coding; 5.1 Super Resolution Model; 5.1.1 Sparse Reconstruction; 5.1.2 Dictionary Training; Joint Training; Single-Scale Training; 5.2 Adaptive Extension; 5.2.1 Training Region Selection; 5.2.2 Region Rejection; 5.3 Application; 5.3.1 Feature Space; 5.3.2 Performance. 
505 8 |a Training and Testing SetsComparison With Other Methods; 5.4 Discussion; Chapter 6: Anchored Regression; 6.1 Anchored Regression Framework; 6.1.1 Problem Statement; 6.1.2 Anchored Neighbors; 6.1.3 Inference by Linear Regression; Features; 6.2 Extensions; 6.2.1 Improved Accuracy; 6.2.2 Improved Runtime; 6.3 Performance; 6.3.1 Implementation Details; Coarse Approximation; Feature Vectors; Supervised Learning; Hashing; 6.3.2 Benchmarking; Quality; Computational Cost; 6.4 Discussion; Chapter 7: Trees and Forests; 7.1 Hierarchical Manifold Learning; Contrast Normalization; Unimodal Trees. 
650 0 |a High resolution imaging. 
650 6 |a Imagerie �a haute r�esolution.  |0 (CaQQLa)000276081 
650 7 |a TECHNOLOGY & ENGINEERING  |x Mechanical.  |2 bisacsh 
650 7 |a High resolution imaging  |2 fast  |0 (OCoLC)fst01763322 
776 0 8 |i Print version:  |z 9780128097038  |z 0128097035  |w (OCoLC)953707986 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128097038  |z Texto completo