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 super-resolution 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.

Autorentext

Milind G. Padalkar, Dhirubhai Ambani Institute of Information and Communication Technology Milind G. Padalkar received a B.E. degree in information technology from the University of Mumbai, India, in 2008 and an M.Tech. degree in computer engineering from Sardar Vallabhbhai National Institute of Technology, Surat, India, in 2010. Currently he is pursuing a Ph.D. degree in information and communication technology and worked as a Junior Research Fellow from 2011 to 2016 (in the Indian Digital Heritage project) at Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India. His research interests include image processing and computer vision.Manjunath V. Joshi received a B.E. degree from the University of Mysore, Mysore, India, and M.Tech. and Ph.D. degrees from the Indian Institute of Technology Bombay (IIT Bombay), Mumbai, India. Currently, he is serving as a Professor with the Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India. He has been involved in active research in the areas of signal processing, image processing and computer vision. He has coauthored a book entitled Motion-Free Super Resolution (Springer, New York). Dr. Joshi was a recipient of the Outstanding Researcher Award in Engineering Section by the Research Scholars Forum of IIT Bombay. He was also a recipient of the Best Ph.D. Thesis Award by Infineon India and the Dr. Vikram Sarabhai Award for the year 2006-2007 in the field of information technology constituted by the Government of Gujarat, India.Nilay L. Khatri received his B.Tech. degree in electronics and communication engineering from the Institute of Technology, Nirma University, Ahmedabad, India, in 2008 and an M.Tech. degree in information and communication technology from Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, India, in 2011. He has worked as a Junior Research Fellow in the Indian Digital Heritage project at DA-IICT, Gandhinagar, India, from 2011 to 2014. His research interests include image processing and 3D computer vision. He is particularly interested in 3D content generation from 2D data by incorporating human interactions in computer vision algorithms. Currently, he is working as an Image Processing Research Engineer at Jekson-Vision, India.

Inhalt
Preface.- Acknowledgments.- Introduction.- Image Super-resolution: Self-learning, Sparsity and Gabor Prior.- Self-learning: Faster, Smarter, Simpler.- An Exemplar-based Inpainting Using an Autoregressive Model.- Attempts to Improve Inpainting.- Simultaneous Inpainting and Super-resolution.- Detecting and Inpainting Damaged Regions in Facial Images of Statues.- Auto-inpainting Cracks in Heritage Scenes.- Challenges and Future Directions.- Bibliography.- Authors' Biographies.