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Motion Deblurring : Algorithms and Systems.
Comprehensive guide to the restoration of images degraded by motion blur, encompassing algorithms and architectures, with novel computational photography methods.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge :
Cambridge University Press,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Half Title; Title Page; Copyright; Contents; List of contributors; Preface; 1 Mathematical models and practical solvers for uniform motion deblurring; 1.1 Non-blind deconvolution; 1.1.1 Regularized approaches; 1.1.2 Iterative approaches; 1.1.3 Recent advancements; 1.1.4 Variable splitting solver; 1.1.5 A few results; 1.2 Blind deconvolution; 1.2.1 Maximum marginal probability estimation; 1.2.2 Alternating energy minimization; 1.2.3 Implicit edge recovery; 1.2.4 Explicit edge prediction for very large PSF estimation; 1.2.5 Results and running time; 2 Spatially-varying image deblurring.
- 2.1 Review of image deblurring methods2.2 A unified camera-shake blur model; 2.2.1 Blur matrices; 2.2.2 Spatially-varying deconvolution; 2.3 Single image deblurring using motion density functions; 2.3.1 Optimization formulation; 2.3.2 Our system for MDF-based deblurring; 2.3.3 Experiments and results; 2.4 Image deblurring using inertial measurement sensors; 2.4.1 Deblurring using inertial sensors; 2.4.2 Deblurring system; 2.4.3 Results; 2.5 Generating sharp panoramas from motion-blurred videos; 2.5.1 Motion and duty cycle estimation; 2.5.2 Experiments; 2.5.3 Real videos; 2.6 Discussion.
- 3 Hybrid-imaging for motion deblurring3.1 Introduction; 3.2 Fundamental resolution tradeoff; 3.3 Hybrid-imaging systems; 3.4 Shift-invariant PSF image deblurring; 3.4.1 Parametric motion computation; 3.4.2 Shift-invariant PSF estimation; 3.4.3 Image deconvolution; 3.4.4 Examples
- shift-invariant PSF; 3.4.5 Shift-invariant PSF optimization; 3.4.6 Examples
- optimized shift-invariant PSF; 3.5 Spatially-varying PSF image deblurring; 3.5.1 Examples
- spatially-varying PSF; 3.6 Moving object deblurring; 3.7 Discussion and summary; 4 Efficient, blind, spatially-variant deblurring for shaken images.
- 4.1 Introduction4.2 Modelling spatially-variant camera-shake blur; 4.2.1 Components of camera motion; 4.2.2 Motion blur and homographies; 4.2.3 Camera calibration; 4.2.4 Uniform blur as a special case; 4.3 The computational model; 4.4 Blind estimation of blur from a single image; 4.4.1 Updating the blur kernel; 4.4.2 Updating the latent image; 4.5 Efficient computation of the spatially-variant model; 4.5.1 A locally-uniform approximation for camera shake; 4.5.2 Updating the blur kernel; 4.5.3 Updating the latent image: fast, non-iterative, non-blind deconvolution.
- 4.6 Single-image deblurring results4.6.1 Limitations and failures; 4.7 Implementation; 4.8 Conclusion; 5 Removing camera shake in smartphones without hardware stabilization; 5.1 Introduction; 5.2 Image acquisition model; 5.2.1 Space-invariant model; 5.2.2 Space-variant model; 5.3 Inverse problem; 5.3.1 MAP and beyond; 5.3.2 Getting more prior information; 5.3.3 Patch based; 5.4 Pinhole camera model; 5.5 Smartphone application; 5.5.1 Space-invariant implementation; 5.5.2 Space-variant implementation; 5.6 Evaluation; 5.7 Conclusions; 6 Multi-sensor fusion for motion deblurring; 6.1 Introduction.