Image Restoration for Remote Sensing: Overview and Toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS

Material Analysis and Conservation Treatment of Louise Nevelson’s sculpture Dawn’s Image, Night

Louise Nevelson’s large-scale, matte black assemblage sculpture Dawn’s Image, Night, 1969, is owned by and currently on display at SUNY Buffalo State College. The paint layers exhibited significant amounts of dust, some damages, vandalism, and unsightly fingerprints. Extensive scientific analysis and archival research were utilized to design an appropriate treatment and long-term preservation plan. Methods of analysis include: Fourier transform infrared spectroscopy, pyrolysis-gas chromatography-mass spectrometry, microchemical testing, and xray fluorescence spectroscopy. The project also addresses the ethical considerations surrounding the prior removal of original components for safety considerations, repainting as a possible treatment, and the overall obstacles of treating a large uncoated, matte monochrome sculpture insitu. The treatment was informed by scientific analysis, materials testing and ethical considerations, resulting in minimal intervention

Estimation of stripping by static immersion test using image processing and machine learning

Hot Mixed Asphalt (HMA) is one of the most common types of pavement, which exists on the surface of the roads, inside and outside of cities. One of the main destresses in HMA is moisture-related damage, which mainly occurs in the form of stripping. The process of losing adhesion and cohesion of asphalt cement due to the presence of moisture and cyclic loads is called “stripping”. Several test procedures have been designed and conducted on different types of asphaltic mixtures to identify and measure moisture damages, especially stripping. Stripping evaluations could be divided into two classes: tests on compacted mixtures and tests on loose mixtures. Test procedures for loose mixture have been adopted by different highway agencies, such as the Ministry of Transportation Ontario (MTO), and pavement industries, because they are easy to perform, cost-effective, and do not require complex equipment. But since stripping estimation is based on visual assessment, the results could be inconsistent when they are estimated by inexperienced operators. One of the most common tests on loose mixtures is static immersion test, and a modified version of the static immersion test has been used by MTO, listed as LS-285 R29. To evaluate stripping in this test procedure, 104g of loose asphaltic mixture should be immersed inside water for 24 hours and then the retained coating areas should be measured by a skilled technician as a percentage of the total surface area. Image processing methods are proper examples of using smart agents in visual assessment problems, such as object detection and pattern recognition. In this research, a vision-based algorithm and a low-cost light improvement system were developed as an alternative for manual judgment. The system receives images of samples captured in a controlled lighting condition, which is called illumination box, and then it applies Contrast Limited Adaptive Histogram Equalization to enhance contrast intensity of the image. In addition, the system uses inpainting to reconstruct specular highlights in the image, and then classifies the regions on the image, i.e. coated and stripped areas, using combinations of K-means clustering and K-Nearest Neighbors and Support Vector Machines classifiers. The developed system is able to overcome most of the shortcomings of prior methods, such as evaluation of the stripping on mixtures with dark-colour aggregates and processing test images without alteration of the test samples. The differences of the results in the best configuration of classifiers from manual estimations had the mean of 4.8 % and the standard deviation of 5.2 %. Moreover, application of illumination box and contrast enhancement module proved to be effective to improve the performance of this system

6th International Meeting on Retouching of Cultural Heritage, RECH6

RECH Biennial Meeting is one of the largest educational and scientific events in Retouching field, an ideal venue for conservators and scientists to present their research results about retouching. The main focus will be to promote the exchange of ideas, concepts, terminology, methods, techniques and materials applied during the retouching process in different areas of conservation: mural painting, easel painting, sculpture, graphic documentation, architecture, plasterwork, photography, contemporary art, among others. This Meeting aims to address retouching by encouraging papers that contribute to a deeper understanding of this final task of the conservation and restoration intervention. The main theme embraces the concepts of retouching, the criteria and limits in the retouching process, the bad retouching impact on heritage and their technical and scientific developments.This Meeting will discuss real-life approaches on retouching, focusing on practical solutions and on sharing experiencesColomina Subiela, A.; Doménech García, B.; Bailão, A. (2023). 6th International Meeting on Retouching of Cultural Heritage, RECH6. Editorial Universitat Politècnica de València. https://doi.org/10.4995/RECH6.2021.1601

Deep Learning-Based Robotic Perception for Adaptive Facility Disinfection

Hospitals, schools, airports, and other environments built for mass gatherings can become hot spots for microbial pathogen colonization, transmission, and exposure, greatly accelerating the spread of infectious diseases across communities, cities, nations, and the world. Outbreaks of infectious diseases impose huge burdens on our society. Mitigating the spread of infectious pathogens within mass-gathering facilities requires routine cleaning and disinfection, which are primarily performed by cleaning staff under current practice. However, manual disinfection is limited in terms of both effectiveness and efficiency, as it is labor-intensive, time-consuming, and health-undermining. While existing studies have developed a variety of robotic systems for disinfecting contaminated surfaces, those systems are not adequate for intelligent, precise, and environmentally adaptive disinfection. They are also difficult to deploy in mass-gathering infrastructure facilities, given the high volume of occupants. Therefore, there is a critical need to develop an adaptive robot system capable of complete and efficient indoor disinfection. The overarching goal of this research is to develop an artificial intelligence (AI)-enabled robotic system that adapts to ambient environments and social contexts for precise and efficient disinfection. This would maintain environmental hygiene and health, reduce unnecessary labor costs for cleaning, and mitigate opportunity costs incurred from infections. To these ends, this dissertation first develops a multi-classifier decision fusion method, which integrates scene graph and visual information, in order to recognize patterns in human activity in infrastructure facilities. Next, a deep-learning-based method is proposed for detecting and classifying indoor objects, and a new mechanism is developed to map detected objects in 3D maps. A novel framework is then developed to detect and segment object affordance and to project them into a 3D semantic map for precise disinfection. Subsequently, a novel deep-learning network, which integrates multi-scale features and multi-level features, and an encoder network are developed to recognize the materials of surfaces requiring disinfection. Finally, a novel computational method is developed to link the recognition of object surface information to robot disinfection actions with optimal disinfection parameters

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field