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E-raamat: Motion Deblurring: Algorithms and Systems

Edited by (University of Maryland, College Park), Edited by (Indian Institute of Technology, Madras)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 08-May-2014
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139949415
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Motion Deblurring: Algorithms and SystemsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 08-May-2014
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139949415
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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.

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Comprehensive guide to the restoration of images degraded by motion blur, encompassing algorithms and architectures, with novel computational photography methods.
List of contributors ix
Preface xi
1 Mathematical models and practical solvers for uniform motion deblurring 1(30)
Jiaya Jia
1.1 Non-blind deconvolution
1(11)
1.2 Blind deconvolution
12(19)
2 Spatially-varying image deblurring 31(26)
Neel Joshi
Sing Bing Kang
Richard Szeliski
2.1 Review of image deblurring methods
31(1)
2.2 A unified camera-shake blur model
32(3)
2.3 Single image deblurring using motion density functions
35(1)
2.4 Image deblurring using inertial measurement sensors
36(10)
2.5 Generating sharp panoramas from motion-blurred videos
46(8)
2.6 Discussion
54(3)
3 Hybrid-imaging for motion deblurring 57(18)
Moshe Ben-Ezra
Yu-Wing Tai
Michael S. Brown
Shree K. Nayar
3.1 Introduction
57(1)
3.2 Fundamental resolution tradeoff
57(2)
3.3 Hybrid-imaging systems
59(2)
3.4 Shift-invariant PSF image deblurring
61(6)
3.5 Spatially-varying PSF image deblurring
67(3)
3.6 Moving object deblurring
70(2)
3.7 Discussion and summary
72(3)
4 Efficient, blind, spatially-variant deblurring for shaken images 75(25)
Oliver Whyte
Josef Sivic
Andrew Zisserman
Jean Ponce
4.1 Introduction
75(1)
4.2 Modelling spatially-variant camera-shake blur
76(6)
4.3 The computational model
82(1)
4.4 Blind estimation of blur from a single image
83(4)
4.5 Efficient computation of the spatially-variant model
87(7)
4.6 Single-image deblurring results
94(1)
4.7 Implementation
95(2)
4.8 Conclusion
97(3)
5 Removing camera shake in smartphones without hardware stabilization 100(23)
Filip Sroubek
Jan Flusser
5.1 Introduction
100(1)
5.2 Image acquisition model
100(2)
5.3 Inverse problem
102(7)
5.4 Pinhole camera model
109(3)
5.5 Smartphone application
112(5)
5.6 Evaluation
117(2)
5.7 Conclusions
119(4)
6 Multi-sensor fusion for motion deblurring 123(18)
Jingyi Yu
6.1 Introduction
123(1)
6.2 Hybrid-speed sensor
124(1)
6.3 Motion deblurring
125(3)
6.4 Depth map super-resolution
128(4)
6.5 Extensions to low-light imaging
132(5)
6.6 Discussion and summary
137(4)
7 Motion deblurring using fluttered shutter 141(20)
Amit Agrawal
7.1 Related work
141(1)
7.2 Coded exposure photography
142(1)
7.3 Image deconvolution
142(2)
7.4 Code selection
144(3)
7.5 Linear solution for deblurring
147(3)
7.6 Resolution enhancement
150(1)
7.7 Optimized codes for PSF estimation
151(5)
7.8 Implementation
156(1)
7.9 Analysis
156(3)
7.10 Summary
159(2)
8 Richardson-Lucy deblurring for scenes under a projective motion path 161(23)
Yu-Wing Tai
Michael S. Brown
8.1 Introduction
161(2)
8.2 Related work
163(1)
8.3 The projective motion blur model
164(1)
8.4 Projective motion Richardson-Lucy
165(5)
8.5 Motion estimation
170(1)
8.6 Experiment results
171(8)
8.7 Discussion and conclusion
179(5)
9 HDR imaging in the presence of motion blur 184(23)
C.S. Vijay
C. Paramanand
A.N. Rajagopalan
9.1 Introduction
184(2)
9.2 Existing approaches to HDRI
186(3)
9.3 CRF, irradiance estimation, and tone-mapping
189(2)
9.4 HDR imaging under uniform blurring
191(1)
9.5 HDRI for non-uniform blurring
192(7)
9.6 Experimental results
199(4)
9.7 Conclusions and discussions
203(4)
10 Compressive video sensing to tackle motion blur 207(15)
Ashok Veeraraghavan
Dikpal Reddy
10.1 Introduction
207(1)
10.2 Related work
208(3)
10.3 Imaging architecture
211(2)
10.4 High-speed video recovery
213(3)
10.5 Experimental results
216(3)
10.6 Conclusions
219(3)
11 Coded exposure motion deblurring for recognition 222(24)
Scott McCloskey
11.1 Motion sensitivity of iris recognition
223(2)
11.2 Coded exposure
225(14)
11.3 Coded exposure performance on iris recognition
239(1)
11.4 Barcodes
240(1)
11.5 More general subject motion
241(1)
11.6 Implications of computational imaging for recognition
242(2)
11.7 Conclusion
244(2)
12 Direct recognition of motion-blurred faces 246(12)
Kaushik Mitra
Priyanka Vageeswaran
Rama Chellappa
12.1 Introduction
246(3)
12.2 The set of all motion-blurred images
249(2)
12.3 Bank of classifiers approach for recognizing motion-blurred faces
251(1)
12.4 Experimental evaluation
252(3)
12.5 Discussion
255(3)
13 Performance limits for motion deblurring cameras 258(25)
Oliver Cossairt
Mohit Gupta
13.1 Introduction
258(3)
13.2 Performance bounds for flutter shutter cameras
261(4)
13.3 Performance bound for motion-invariant cameras
265(4)
13.4 Simulations to verify performance bounds
269(1)
13.5 Role of image priors
270(3)
13.6 When to use computational imaging
273(1)
13.7 Relationship to other computational imaging systems
274(4)
13.8 Summary and discussion
278(5)
Index 283
A. N. Rajagopalan is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology, Madras. He co-authored the book Depth from Defocus: A Real Aperture Imaging Approach in 1998. He was Alexander von Humboldt Fellow in the Technical University of Munich in 20078, is a Fellow of the Indian National Academy of Engineering and a Senior Member of the IEEE. He also received the Outstanding Investigator Award from the Department of Atomic Energy, India in 2012. Rama Chellappa is Minta Martin Professor of Engineering and an affiliate Professor of Computer Science at University of Maryland, College Park. He is also affiliated with the Center for Automation Research and UMIACS, and is serving as the Chair of the ECE department. He is a recipient of the K. S. Fu Prize from the IAPR and the Society, Technical Achievement and Meritorious Service Awards from the IEEE Signal Processing Society. He also received the Technical Achievement and Meritorious Service Awards from the IEEE Computer Society. In 2010, he was recognized as an Outstanding ECE by Purdue University. He is a Fellow of the IEEE, IAPR, OSA and AAAS, a Golden Core Member of the IEEE Computer Society, and has served as a Distinguished Lecturer of the IEEE Signal Processing Society as well as the President of the IEEE Biometrics Council.
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