Motion deblurring : algorithms and systems / edited by A.N. Rajagopalan, Indian Institute of Technology, Madras, Rama Chellappa, University of Maryland, College Park.

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 an...

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Bibliographic Details
Other Authors: Rajagopalan, A. N., (Editor), Chellappa, Rama, (Editor)
Format: Book
Language:English
Published: Cambridge : Cambridge University Press, 2014.
Subjects:
Online Access:CONNECT
Table of Contents:
  • Machine generated contents note: 1. Mathematical models and practical solvers for uniform motion deblurring / Jiaya Jia
  • 1.1. Non-blind deconvolution
  • 1.2. Blind deconvolution
  • 2. Spatially-varying image deblurring / Richard Szeliski
  • 2.1. Review of image deblurring methods
  • 2.2.A unified camera-shake blur model
  • 2.3. Single image deblurring using motion density functions
  • 2.4. Image deblurring using inertial measurement sensors
  • 2.5. Generating sharp panoramas from motion-blurred videos
  • 2.6. Discussion
  • 3. Hybrid-imaging for motion deblurring / Shree K. Nayar
  • 3.1. Introduction
  • 3.2. Fundamental resolution tradeoff
  • 3.3. Hybrid-imaging systems
  • 3.4. Shift-invariant PSF image deblurring
  • 3.5. Spatially-varying PSF image deblurring
  • 3.6. Moving object deblurring
  • 3.7. Discussion and summary
  • 4. Efficient, blind, spatially-variant deblurring for shaken images / Jean Ponce
  • 4.1. Introduction
  • 4.2. Modelling spatially-variant camera-shake blur.
  • Contents note continued: 13.8. Summary and discussion.
  • Contents note continued: 11. Coded exposure motion deblurring for recognition / Scott McCloskey
  • 11.1. Motion sensitivity of iris recognition
  • 11.2. Coded exposure
  • 11.3. Coded exposure performance on iris recognition
  • 11.4. Barcodes
  • 11.5. More general subject motion
  • 11.6. Implications of computational imaging for recognition
  • 11.7. Conclusion
  • 12. Direct recognition of motion-blurred faces / Rama Chellappa
  • 12.1. Introduction
  • 12.2. The set of all motion-blurred images
  • 12.3. Bank of classifiers approach for recognizing motion-blurred faces
  • 12.4. Experimental evaluation
  • 12.5. Discussion
  • 13. Performance limits for motion deblurring cameras / Mohit Gupta
  • 13.1. Introduction
  • 13.2. Performance bounds for flutter shutter cameras
  • 13.3. Performance bound for motion-invariant cameras
  • 13.4. Simulations to verify performance bounds
  • 13.5. Role of image priors
  • 13.6. When to use computational imaging
  • 13.7. Relationship to other computational imaging systems.
  • Contents note continued: 7.7. Optimized codes for PSF estimation
  • 7.8. Implementation
  • 7.9. Analysis
  • 7.10. Summary
  • 8. Richardson-Lucy deblurring for scenes under a projective motion path / Michael S. Brown
  • 8.1. Introduction
  • 8.2. Related work
  • 8.3. The projective motion blur model
  • 8.4. Projective motion Richardson
  • Lucy
  • 8.5. Motion estimation
  • 8.6. Experiment results
  • 8.7. Discussion and conclusion
  • 9. HDR imaging in the presence of motion blur / A.N. Rajagopalan
  • 9.1. Introduction
  • 9.2. Existing approaches to HDRJ
  • 9.3. CRF, irradiance estimation, and tone-mapping
  • 9.4. HDR imaging under uniform blurring
  • 9.5. HDRI for non-uniform blurring
  • 9.6. Experimental results
  • 9.7. Conclusions and discussions
  • 10.Compressive video sensing to tackle motion blur / Dikpal Reddy
  • 10.1. Introduction
  • 10.2. Related work
  • 10.3. Imaging architecture
  • 10.4. High-speed video recovery
  • 10.5. Experimental results
  • 10.6. Conclusions.
  • Contents note continued: 4.3. The computational model
  • 4.4. Blind estimation of blur from a single image
  • 4.5. Efficient computation of the spatially-variant model
  • 4.6. Single-image deblurring results
  • 4.7. Implementation
  • 4.8. Conclusion
  • 5. Removing camera shake in smartphones without hardware stabilization / Jan Flusser
  • 5.1. Introduction
  • 5.2. Image acquisition model
  • 5.3. Inverse problem
  • 5.4. Pinhole camera model
  • 5.5. Smartphone application
  • 5.6. Evaluation
  • 5.7. Conclusions
  • 6. Multi-sensor fusion for motion deblurring / Jlngyi Yu
  • 6.1. Introduction
  • 6.2. Hybrid-speed sensor
  • 6.3. Motion deblurring
  • 6.4. Depth map super-resolution
  • 6.5. Extensions to low-light imaging
  • 6.6. Discussion and summary
  • 7. Motion deblurring using fluttered shutter / Amit Agrawal
  • 7.1. Related work
  • 7.2. Coded exposure photography
  • 7.3. Image deconvolution
  • 7.4. Code selection
  • 7.5. Linear solution for deblurring
  • 7.6. Resolution enhancement.