Automatic Detection, Classification and Restoration of Defects in Historical Images

Historical photos are significant attestations of the inheritance of the past. Since Photography is an art that is more than 150 years old, more and more diffuse are the photographic archives all over the world. Nevertheless, time and bad preservation corrupts physical supports, and many important historical documents risk to be ruined and their content lost. Therefore solutions must be implemented to preserve their state and to recover damaged information. This PhD thesis proposes a general methodology, and several applicative solutions, to handle these problems, by means of digitization and digital restoration. The purpose is to create a useful tool to support non-expert users in the restoration process of damaged historical images. The content of this thesis is outlined as follows: Chapter 1 gives an overview on the problems related to management and preservation of cultural repositories, and discusses about possible technological solutions that can help cultural institutions in their activities. Some examples of significant European projects are given. Chapter 2 presents the key problems related to the purpose of this work. It briefly describes the Italian scientific project, in the context of which my research work has been carrying out. A restoration model is proposed, and compared to the classical model. Then it discusses the methodology that has been proposed, which consists in a knowledge-based model for image restoration. Finally a software restoration tool, to guide users through the restoration process, is presented. Chapter 3 presents a taxonomy of typical defects by which damaged old photos are affected. A dual taxonomy is proposed, designed to catalogue defects of both old printed photos and their digitized copies. The next two chapters present two applications that have been implemented as solutions for specific damages. Chapter 4 presents a classification application for a particular damage of the digital defect taxonomy. Foxing spots are analyzed, and a set of low level descriptors, specific for this damage, is proposed. Then a classification tool, based on these descriptors, is presented. Chapter 5 discusses about detection and the removal of quasi-horizontal scratches in still images. The test dataset is composed by digitized aerial photos of the Sicilian territory, which has been damaged by manual inspection of the photo negatives with a mechanical device. In chapter 6 a new methodology is proposed to handle the problem of restoration of greyscale textured images. Two texture synthesis based approaches are presented: a conditional random generation process, which is designed for random textured images, and a best matching method, that works better with periodic textures. A final section summarizes obtained results