Coverart for item
The Resource Digital heritage reconstruction using super-resolution and inpainting, Milind G. Padalkar, Manjunath V. Joshi, Nilay L. Khatri, (electronic book)

Digital heritage reconstruction using super-resolution and inpainting, Milind G. Padalkar, Manjunath V. Joshi, Nilay L. Khatri, (electronic book)

Label
Digital heritage reconstruction using super-resolution and inpainting
Title
Digital heritage reconstruction using super-resolution and inpainting
Statement of responsibility
Milind G. Padalkar, Manjunath V. Joshi, Nilay L. Khatri
Creator
Contributor
Author
Subject
Language
eng
Summary
Heritage sites across the world have witnessed a number of natural calamities, sabotage and damage from visitors, resulting in their present ruined condition. Many sites are now restricted to reduce the risk of further damage. Yet these masterpieces are significant cultural icons and critical markers of past civilizations that future generations need to see. A digitally reconstructed heritage site could diminish further harm by using immersive navigation or walkthrough systems for virtual environments. An exciting key element for the viewer is observing fine details of the historic work and viewing monuments in their undamaged form. This book presents image superresolution methods and techniques for automatically detecting and inpainting damaged regions in heritage monuments, in order to provide an enhanced visual experience. The book presents techniques to obtain higher resolution photographs of the digitally reconstructed monuments, and the resulting images can serve as input to immersive walkthrough systems. It begins with the discussion of two novel techniques for image super-resolution and an approach for inpainting a user-supplied region in the given image, followed by a technique to simultaneously perform super-resolution and inpainting of given missing regions. It then introduces a method for automatically detecting and repairing the damage to dominant facial regions in statues, followed by a few approaches for automatic crack repair in images of heritage scenes. This book is a giant step toward ensuring that the iconic sites of our past are always available, and will never be truly lost
Member of
Cataloging source
CaBNVSL
http://library.link/vocab/creatorName
Padalkar, Milind G
Dewey number
621.367
Illustrations
illustrations
Index
no index present
LC call number
TA1637
LC item number
.P233 2017
Literary form
non fiction
Nature of contents
  • dictionaries
  • abstracts summaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Joshi, Manjunath V.
  • Khatri, Nilay L.
Series statement
Synthesis lectures on visual computing,
Series volume
26
http://library.link/vocab/subjectName
  • Image reconstruction
  • Image processing
  • Historic sites
  • Statues
  • Inpainting
Target audience
  • adult
  • specialized
Label
Digital heritage reconstruction using super-resolution and inpainting, Milind G. Padalkar, Manjunath V. Joshi, Nilay L. Khatri, (electronic book)
Instantiates
Publication
Bibliography note
Includes bibliographical references (pages 135-147)
Carrier category
online resource
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type MARC source
rdacontent
Contents
  • 1. Introduction -- 1.1 What is super-resolution? -- 1.2 What is inpainting? -- 1.3 Applying super-resolution and inpainting in digital heritage images: challenges and solutions -- 1.4 A tour of the book --
  • 2. Image super-resolution: self-learning, sparsity and Gabor prior -- 2.1 Single-image SR: a unified framework -- 2.1.1 Classical (within-scale) super-resolution -- 2.1.2 Exampled-based (across-scale) super-resolution -- 2.1.3 Unifying classical and example-based SR -- 2.2 Self-learning and degradation estimation -- 2.3 Gabor prior and regularization -- 2.4 Performance evaluation -- 2.4.1 Qualitative evaluation -- 2.4.2 Quantitative evaluation -- 2.5 Conclusion --
  • 3. Self-learning: faster, smarter, simpler -- 3.1 Efficient self-learning -- 3.1.1 Improved self-learning for super-resolution -- 3.2 Performance evaluation -- 3.2.1 Perceptual and quantitative evaluation -- 3.2.2 Improvements and extensions -- 3.3 Conclusion --
  • 4. An exemplar-based inpainting using an autoregressive model -- 4.1 Limitation of existing approaches -- 4.2 Proposed approach -- 4.3 Experimental results -- 4.4 Conclusion --
  • 5. Attempts to improve inpainting -- 5.1 A modified exemplar-based multi-resolution approach -- 5.1.1 Refinement by matching a larger region -- 5.1.2 Refinement using the patch-neighborhood relationship -- 5.1.3 Refinement using compressive sensing framework -- 5.2 Curvature-based approach for inpainting -- 5.3 Observations and conclusion --
  • 6. Simultaneous inpainting and super-resolution -- 6.1 Need for patch comparison at finer resolution -- 6.2 Proposed approach -- 6.2.1 Constructing image-representative LR-HR dictionaries -- 6.2.2 Estimation of HR patches -- 6.2.3 Simultaneous inpainting and SR of missing pixels -- 6.3 Experimental results -- 6.4 Conclusion --
  • 7. Detecting and inpainting damaged regions in facial images of statues -- 7.1 Preprocessing -- 7.2 Extraction of eye, nose and lip regions -- 7.3 Classification -- 7.4 Inpainting -- 7.5 Experimental results -- 7.6 Conclusion --
  • 8. Auto-inpainting cracks in heritage scenes -- 8.1 A simple method for detecting and inpainting cracks -- 8.1.1 Order-statistics-based filtering -- 8.1.2 Scan-line peak difference detection -- 8.1.3 Density-based filtering -- 8.1.4 Refinement -- 8.1.5 Experimental results -- 8.2 Singular value decomposition-based crack detection and inpainting -- 8.2.1 SVD and patch analysis -- 8.2.2 Thresholding -- 8.2.3 Experimental results -- 8.3 Crack detection using tolerant edit distance and inpainting -- 8.3.1 Preprocessing -- 8.3.2 Patch comparison using tolerant edit distance -- 8.3.3 Edge strength calculation -- 8.3.4 Thresholding -- 8.3.5 Refinement -- 8.3.6 Experimental results -- 8.4 Extension to auto-inpaint cracks in videos -- 8.4.1 Homography estimation -- 8.4.2 Reference frame detection -- 8.4.3 Tracking and inpainting cracked regions across frames -- 8.4.4 Experimental results -- 8.5 Conclusion --
  • 9. Challenges and future directions -- Bibliography -- Authors' biographies
Control code
201611VCP026
Dimensions
unknown
Extent
1 PDF (xviii, 150 pages)
File format
multiple file formats
Form of item
online
Isbn
9781627059213
Media category
electronic
Media MARC source
isbdmedia
Other control number
10.2200/S00740ED1V01Y201611VCP026
Other physical details
illustrations.
Reformatting quality
access
Specific material designation
remote
System details
System requirements: Adobe Acrobat Reader
Label
Digital heritage reconstruction using super-resolution and inpainting, Milind G. Padalkar, Manjunath V. Joshi, Nilay L. Khatri, (electronic book)
Publication
Bibliography note
Includes bibliographical references (pages 135-147)
Carrier category
online resource
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type MARC source
rdacontent
Contents
  • 1. Introduction -- 1.1 What is super-resolution? -- 1.2 What is inpainting? -- 1.3 Applying super-resolution and inpainting in digital heritage images: challenges and solutions -- 1.4 A tour of the book --
  • 2. Image super-resolution: self-learning, sparsity and Gabor prior -- 2.1 Single-image SR: a unified framework -- 2.1.1 Classical (within-scale) super-resolution -- 2.1.2 Exampled-based (across-scale) super-resolution -- 2.1.3 Unifying classical and example-based SR -- 2.2 Self-learning and degradation estimation -- 2.3 Gabor prior and regularization -- 2.4 Performance evaluation -- 2.4.1 Qualitative evaluation -- 2.4.2 Quantitative evaluation -- 2.5 Conclusion --
  • 3. Self-learning: faster, smarter, simpler -- 3.1 Efficient self-learning -- 3.1.1 Improved self-learning for super-resolution -- 3.2 Performance evaluation -- 3.2.1 Perceptual and quantitative evaluation -- 3.2.2 Improvements and extensions -- 3.3 Conclusion --
  • 4. An exemplar-based inpainting using an autoregressive model -- 4.1 Limitation of existing approaches -- 4.2 Proposed approach -- 4.3 Experimental results -- 4.4 Conclusion --
  • 5. Attempts to improve inpainting -- 5.1 A modified exemplar-based multi-resolution approach -- 5.1.1 Refinement by matching a larger region -- 5.1.2 Refinement using the patch-neighborhood relationship -- 5.1.3 Refinement using compressive sensing framework -- 5.2 Curvature-based approach for inpainting -- 5.3 Observations and conclusion --
  • 6. Simultaneous inpainting and super-resolution -- 6.1 Need for patch comparison at finer resolution -- 6.2 Proposed approach -- 6.2.1 Constructing image-representative LR-HR dictionaries -- 6.2.2 Estimation of HR patches -- 6.2.3 Simultaneous inpainting and SR of missing pixels -- 6.3 Experimental results -- 6.4 Conclusion --
  • 7. Detecting and inpainting damaged regions in facial images of statues -- 7.1 Preprocessing -- 7.2 Extraction of eye, nose and lip regions -- 7.3 Classification -- 7.4 Inpainting -- 7.5 Experimental results -- 7.6 Conclusion --
  • 8. Auto-inpainting cracks in heritage scenes -- 8.1 A simple method for detecting and inpainting cracks -- 8.1.1 Order-statistics-based filtering -- 8.1.2 Scan-line peak difference detection -- 8.1.3 Density-based filtering -- 8.1.4 Refinement -- 8.1.5 Experimental results -- 8.2 Singular value decomposition-based crack detection and inpainting -- 8.2.1 SVD and patch analysis -- 8.2.2 Thresholding -- 8.2.3 Experimental results -- 8.3 Crack detection using tolerant edit distance and inpainting -- 8.3.1 Preprocessing -- 8.3.2 Patch comparison using tolerant edit distance -- 8.3.3 Edge strength calculation -- 8.3.4 Thresholding -- 8.3.5 Refinement -- 8.3.6 Experimental results -- 8.4 Extension to auto-inpaint cracks in videos -- 8.4.1 Homography estimation -- 8.4.2 Reference frame detection -- 8.4.3 Tracking and inpainting cracked regions across frames -- 8.4.4 Experimental results -- 8.5 Conclusion --
  • 9. Challenges and future directions -- Bibliography -- Authors' biographies
Control code
201611VCP026
Dimensions
unknown
Extent
1 PDF (xviii, 150 pages)
File format
multiple file formats
Form of item
online
Isbn
9781627059213
Media category
electronic
Media MARC source
isbdmedia
Other control number
10.2200/S00740ED1V01Y201611VCP026
Other physical details
illustrations.
Reformatting quality
access
Specific material designation
remote
System details
System requirements: Adobe Acrobat Reader

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