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Motion Deblurring

Book Description

A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research. It encompasses both algorithms and architectures, providing detailed coverage of practical techniques by leading researchers. From an algorithms perspective, blind and non-blind approaches are discussed, including the use of single or multiple images; projective motion blur model; image priors and parametric models; high dynamic range imaging in the irradiance domain; and image recognition in blur. Performance limits for motion deblurring cameras are also presented. From a systems perspective, hybrid frameworks combining low-resolution-high-speed and high-resolution-low-speed cameras are described, along with the use of inertial sensors and coded exposure cameras. Also covered is an architecture exploiting compressive sensing for video recovery. A valuable resource for researchers and practitioners in computer vision, image processing, and related fields.

Table of Contents

  1. Cover Page
  2. Half Title Page
  3. Title Page
  4. Copyright Page
  5. Contents
  6. List of contributors
  7. Preface
  8. 1 Mathematical models and practical solvers for uniform motion deblurring
    1. 1.1 Non-blind deconvolution
    2. 1.2 Blind deconvolution
  9. 2 Spatially-varying image deblurring
    1. 2.1 Review of image deblurring methods
    2. 2.2 A unified camera-shake blur model
    3. 2.3 Single image deblurring using motion density functions
    4. 2.4 Image deblurring using inertial measurement sensors
    5. 2.5 Generating sharp panoramas from motion-blurred videos
    6. 2.6 Discussion
  10. 3 Hybrid-imaging for motion deblurring
    1. 3.1 Introduction
    2. 3.2 Fundamental resolution tradeoff
    3. 3.3 Hybrid-imaging systems
    4. 3.4 Shift-invariant PSF image deblurring
    5. 3.5 Spatially-varying PSF image deblurring
    6. 3.6 Moving object deblurring
    7. 3.7 Discussion and summary
  11. 4 Efficient, blind, spatially-variant deblurring for shaken images
    1. 4.1 Introduction
    2. 4.2 Modelling spatially-variant camera-shake blur
    3. 4.3 The computational model
    4. 4.4 Blind estimation of blur from a single image
    5. 4.5 Efficient computation of the spatially-variant model
    6. 4.6 Single-image deblurring results
    7. 4.7 Implementation
    8. 4.8 Conclusion
  12. 5 Removing camera shake in smartphones without hardware stabilization
    1. 5.1 Introduction
    2. 5.2 Image acquisition model
    3. 5.3 Inverse problem
    4. 5.4 Pinhole camera model
    5. 5.5 Smartphone application
    6. 5.6 Evaluation
    7. 5.7 Conclusions
  13. 6 Multi-sensor fusion for motion deblurring
    1. 6.1 Introduction
    2. 6.2 Hybrid-speed sensor
    3. 6.3 Motion deblurring
    4. 6.4 Depth map super-resolution
    5. 6.5 Extensions to low-light imaging
    6. 6.6 Discussion and summary
  14. 7 Motion deblurring using fluttered shutter
    1. 7.1 Related work
    2. 7.2 Coded exposure photography
    3. 7.3 Image deconvolution
    4. 7.4 Code selection
    5. 7.5 Linear solution for deblurring
    6. 7.6 Resolution enhancement
    7. 7.7 Optimized codes for PSF estimation
    8. 7.8 Implementation
    9. 7.9 Analysis
    10. 7.10 Summary
  15. 8 Richardson–Lucy deblurring for scenes under a projective motion path
    1. 8.1 Introduction
    2. 8.2 Related work
    3. 8.3 The projective motion blur model
    4. 8.4 Projective motion Richardson–Lucy
    5. 8.5 Motion estimation
    6. 8.6 Experiment results
    7. 8.7 Discussion and conclusion
  16. 9 HDR imaging in the presence of motion blur
    1. 9.1 Introduction
    2. 9.2 Existing approaches to HDRI
    3. 9.3 CRF, irradiance estimation, and tone-mapping
    4. 9.4 HDR imaging under uniform blurring
    5. 9.5 HDRI for non-uniform blurring
    6. 9.6 Experimental results
    7. 9.7 Conclusions and discussions
  17. 10 Compressive video sensing to tackle motion blur
    1. 10.1 Introduction
    2. 10.2 Related work
    3. 10.3 Imaging architecture
    4. 10.4 High-speed video recovery
    5. 10.5 Experimental results
    6. 10.6 Conclusions
  18. 11 Coded exposure motion deblurring for recognition
    1. 11.1 Motion sensitivity of iris recognition
    2. 11.2 Coded exposure
    3. 11.3 Coded exposure performance on iris recognition
    4. 11.4 Barcodes
    5. 11.5 More general subject motion
    6. 11.6 Implications of computational imaging for recognition
    7. 11.7 Conclusion
  19. 12 Direct recognition of motion-blurred faces
    1. 12.1 Introduction
    2. 12.2 The set of all motion-blurred images
    3. 12.3 Bank of classifiers approach for recognizing motion-blurred faces
    4. 12.4 Experimental evaluation
    5. 12.5 Discussion
  20. 13 Performance limits for motion deblurring cameras
    1. 13.1 Introduction
    2. 13.2 Performance bounds for flutter shutter cameras
    3. 13.3 Performance bound for motion-invariant cameras
    4. 13.4 Simulations to verify performance bounds
    5. 13.5 Role of image priors
    6. 13.6 When to use computational imaging
    7. 13.7 Relationship to other computational imaging systems
    8. 13.8 Summary and discussion
  21. Index