Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented

Responsive Urban Models by Processing Sets of Heterogeneous Data

This paper presents some steps in experimentation aimed at describing urban spaces made following the series of earthquakes that affected a vast area of central Italy starting on 24 August 2016. More specifically, these spaces pertain to historical centres of limited size and case studies that can be called "problematic" (due to complex morphological and settlement conditions, because they are difficult to access, or because they have been affected by calamitous events, etc.). The main objectives were to verify the use of sets of heterogeneous data that are already largely available to define a workflow and develop procedures that would allow some of the steps to be automated as much as possible. The most general goal was to use the experimentation to define a methodology to approach the problem aimed at developing descriptive responsive models of the urban space, that is, morphological and computer-based models capable of being modified in relation to the constantly updated flow of input data

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

HBIM IMPLEMENTATION FOR AN OTTOMAN MOSQUE. CASE OF STUDY: SULTAN MEHMET FATIH II MOSQUE IN KOSOVO

Abstract. National Strategy For Cultural Heritage 2017–2027 is a Kosovo Government document that aims the enhancement of the system for the protection and preservation of Kosovan cultural heritage. Among the listed goals, one can find the promotion of an integrated data management approach towards cooperation platforms that involve advanced technologies and information systems applied to cultural heritage. In a country with a low technological progress, as Kosovo is, an innovative information management system like HBIM is a huge challenge. This research contributes in opening the debate about the use of HBIM even for historical architecture, illustrating a methodology of information management promoting the conservation and the valorization of a Kosovan ottoman mosque. The workflow pipeline started with the close range photogrammetric survey, obtaining first spherical panoramas and then the wire-frame processed in a 3D modelling environment, suitable to implement the HBIM project. Basing on the accuracy of the data acquisition, the availability of information about the building and the related level of knowledge, we proposed a semantic representation of the complex structure integrating in an HBIM collecting in an "ad hoc" database the geometrical building components, enriched with attributes as images, materials, decay, interventions, etc., linked to each features. Our approach is an example of how efficient semantic classification can be repeated for the analysis and the documentation of other similar ottoman mosque, simplifying the management of construction by a sort of unique and searchable archive. The advantage of the interoperability concept allows the data sharing is now stressed by HBIM.</p

HBIM IMPLEMENTATION for AN OTTOMAN MOSQUE. CASE of STUDY: SULTAN MEHMET FATIH II MOSQUE in KOSOVO

none5noNational Strategy For Cultural Heritage 2017-2027 is a Kosovo Government document that aims the enhancement of the system for the protection and preservation of Kosovan cultural heritage. Among the listed goals, one can find the promotion of an integrated data management approach towards cooperation platforms that involve advanced technologies and information systems applied to cultural heritage. In a country with a low technological progress, as Kosovo is, an innovative information management system like HBIM is a huge challenge. This research contributes in opening the debate about the use of HBIM even for historical architecture, illustrating a methodology of information management promoting the conservation and the valorization of a Kosovan ottoman mosque. The workflow pipeline started with the close range photogrammetric survey, obtaining first spherical panoramas and then the wire-frame processed in a 3D modelling environment, suitable to implement the HBIM project. Basing on the accuracy of the data acquisition, the availability of information about the building and the related level of knowledge, we proposed a semantic representation of the complex structure integrating in an HBIM collecting in an "ad hoc" database the geometrical building components, enriched with attributes as images, materials, decay, interventions, etc., linked to each features. Our approach is an example of how efficient semantic classification can be repeated for the analysis and the documentation of other similar ottoman mosque, simplifying the management of construction by a sort of unique and searchable archive. The advantage of the interoperability concept allows the data sharing is now stressed by HBIM.openDi Stefano F.; Malinverni E.S.; Pierdicca R.; Fangi G.; Ejupi S.Di Stefano, F.; Malinverni, E. S.; Pierdicca, R.; Fangi, G.; Ejupi, S

Picture processing for enhancement and recognition

Recent years have been characterized by an incredible growth in computing power and storage capabilities, communication speed and bandwidth availability, either for desktop platform or mobile device. The combination of these factors have led to a new era of multimedia applications: browsing of huge image archives, consultation of online video databases, location based services and many other. Multimedia is almost everywhere and requires high quality data, easy retrieval of multimedia contents, increase in network access capacity and bandwidth per user. To meet all the mentioned requirements many efforts have to be made in various research areas, ranging from signal processing, image and video analysis, communication protocols, etc. The research activity developed during these three years concerns the field of multimedia signal processing, with particular attention to image and video analysis and processing. Two main topics have been faced: the first is relating to image and video reconstruction/restoration (using super resolution techniques) in web based application for multimedia contents' fruition; the second is relating to image analysis for location based systems in indoor scenario. The first topic is relating to image and video processing, in particular the focus has been put on the development of algorithm for super resolution reconstruction of image and video sequences in order to make easier the fruition of multimedia data over the web. On one hand, latest years have been characterized by an incredible proliferation and surprising success of user generated multimedia contents, and also distributed and collaborative multimedia database over the web. This brought to serious issues related to their management and maintenance: bandwidth limitation and service costs are important factors when dealing with mobile multimedia contents’ fruition. On the other hand, the current multimedia consumer market has been characterized by the advent of cheap but rather high-quality high definition displays. However, this trend is only partially supported by the deployment of high-resolution multimedia services, thus the resulting disparity between content and display formats have to be addressed and older productions need to be either re-mastered or postprocessed in order to be broadcasted for HD exploitation. In the presented scenario, superresolution reconstruction represents a major solution. Image or video super resolution techniques allow restoring the original spatial resolution from low-resolution compressed data. In this way, both content and service providers, not to tell the final users, are relieved from the burden of providing and supporting large multimedia data transfer. The second topic addressed during my Phd research activity is related to the implementation of an image based positioning system for an indoor navigator. As modern mobile device become faster, classical signal processing is suggested to be used for new applications, such location based service. The exponential growth of wearable devices, such as smartphone and PDA in general, equipped with embedded motion (accelerometers) and rotation (gyroscopes) sensors, Internet connection and high-resolution cameras makes it ideal for INS (Inertial Navigation System) applications aiming to support the localization/navigation of objects and/or users in an indoor environment where common localization systems, such as GPS (Global Positioning System), fail. Thus the need to use alternative positioning techniques. A series of intensive tests have been carried out, showing how modern signal processing techniques can be successfully applied in different scenarios, from image and video enhancement up to image recognition for localization purpose, providing low costs solutions and ensuring real-time performance