| 000 | 07261nam a2200745 i 4500 | ||
|---|---|---|---|
| 001 | 7791094 | ||
| 003 | IEEE | ||
| 005 | 20200413152923.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 170125s2017 caua foab 000 0 eng d | ||
| 020 |
_a9781627056168 _qebook |
||
| 020 |
_z9781627059213 _qprint |
||
| 024 | 7 |
_a10.2200/S00740ED1V01Y201611VCP026 _2doi |
|
| 035 | _a(CaBNVSL)swl00407065 | ||
| 035 | _a(OCoLC)970006484 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aTA1637 _b.P233 2017 |
|
| 082 | 0 | 4 |
_a621.367 _223 |
| 100 | 1 |
_aPadalkar, Milind G., _eauthor. |
|
| 245 | 1 | 0 |
_aDigital heritage reconstruction using super-resolution and inpainting / _cMilind G. Padalkar, Manjunath V. Joshi, Nilay L. Khatri. |
| 264 | 1 |
_a[San Rafael, California] : _bMorgan & Claypool, _c2017. |
|
| 300 |
_a1 PDF (xviii, 150 pages) : _billustrations. |
||
| 336 |
_atext _2rdacontent |
||
| 337 |
_aelectronic _2isbdmedia |
||
| 338 |
_aonline resource _2rdacarrier |
||
| 490 | 1 |
_aSynthesis lectures on visual computing, _x2469-4223 ; _v# 26 |
|
| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 135-147). | ||
| 505 | 0 | _a1. 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 -- | |
| 505 | 8 | _a2. 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 -- | |
| 505 | 8 | _a3. 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 -- | |
| 505 | 8 | _a4. 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 -- | |
| 505 | 8 | _a5. 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 -- | |
| 505 | 8 | _a6. 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 -- | |
| 505 | 8 | _a7. 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 -- | |
| 505 | 8 | _a8. 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 -- | |
| 505 | 8 | _a9. Challenges and future directions -- Bibliography -- Authors' biographies. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aHeritage 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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on January 24, 2017). | ||
| 650 | 0 | _aImage reconstruction. | |
| 650 | 0 |
_aImage processing _xDigital techniques. |
|
| 650 | 0 |
_aHistoric sites _xConservation and restoration. |
|
| 650 | 0 |
_aStatues _xConservation and restoration. |
|
| 650 | 0 | _aInpainting. | |
| 653 | _asuper-resolution | ||
| 653 | _ainpainting | ||
| 653 | _acultural heritage | ||
| 653 | _acrack detection | ||
| 653 | _adigital reconstruction | ||
| 700 | 1 |
_aJoshi, Manjunath V., _eauthor. |
|
| 700 | 1 |
_aKhatri, Nilay L., _eauthor. |
|
| 776 | 0 | 8 |
_iPrint version: _z9781627059213 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on visual computing ; _v# 26. _x2469-4223 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7791094 |
| 999 |
_c562239 _d562239 |
||