What's Wrong with the Murals at the Mogao Grottoes: A Near-Infrared Hyperspectral Imaging Method

Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method.

Images Enhancement of Ancient Mural Painting of Bey's Palace Constantine, Algeria and Lacuna Extraction Using Mahalanobis Distance Classification Approach

As a result of human activity and environmental changes, several types of damages may occur to ancient mural paintings; indeed, lacunae, which refer to the area of paint layer loss, are the most prevalent kind. The presence of lacuna is an essential sign of the progress of mural painting deterioration. Most studies have focused on detecting and removing cracks from old paintings. However, lacuna extraction has not received the necessary consideration and is not well-explored. Furthermore, most recent studies have focused on using deep learning for mural protection and restoration, but deep learning requires a large amount of data and computational resources which is not always available in heritage institutions. In this paper, we present an efficient method to automatically extract lacunae and map deterioration from RGB images of ancient mural paintings of Bey's Palace in Algeria. Firstly, a preprocessing was applied using Dark Channel Prior (DCP) to enhance the quality and improve visibility of the murals. Secondly, a determination of the training sample and pixel's grouping was assigned to their closest sample based on Mahalanobis Distance (MD) by calculating both the mean and variance of the classes in three bands (R, G, and B), in addition to the covariance matrix of all the classes to achieve lacuna extraction of the murals. Finally, the accuracy of extraction was calculated. The experimental results showed that the proposed method can achieve a conspicuously high accuracy of 94.33% in extracting lacunae from ancient mural paintings, thus supporting the work of a specialist in heritage institutions in terms of the time- and cost-consuming documentation process.

Acquisition of High Spectral Resolution Diffuse Reflectance Image Cubes (350-2500 nm) from Archaeological Wall Paintings and Other Immovable Heritage Using a Field-Deployable Spatial Scanning Reflectance Spectrometry Hyperspectral System

There is growing interest in bringing non-invasive laboratory-based analytical imaging tools to field sites to study wall paintings in order to collect molecular information on the macroscale. Analytical imaging tools, such as reflectance imaging spectrometry, have provided a wealth of information about artist materials and working methods, as well as painting conditions. Currently, scientific analyses of wall paintings have been limited to point-measurement techniques such as reflectance spectroscopy (near-ultraviolet, visible, near-infrared, and mid-infrared), X-ray fluorescence, and Raman spectroscopy. Macroscale data collection methods have been limited to multispectral imaging in reflectance and luminescence modes, which lacks sufficient spectral bands to allow for the mapping and identification of artist materials of interest. The development of laboratory-based reflectance and elemental imaging spectrometers and scanning systems has sparked interest in developing truly portable versions, which can be brought to field sites to study wall paintings where there is insufficient space or electrical power for laboratory instruments. This paper presents the design and testing of a simple hyperspectral system consisting of a 2D spatial spot scanning spectrometer, which provides high spectral resolution diffuse reflectance spectra from 350 to 2500 nm with high signal to noise and moderate spatial resolution (few mm). This spectral range at high spectral resolution was found to provide robust chemical specificity sufficient to identify and map many artists' materials, as well as the byproducts of weathering and conservation coatings across the surface of ancient and Byzantine Cypriot wall paintings. Here, we present a detailed description of the hyperspectral system, its performance, and examples of its use to study wall paintings from Roman tombs in Cyprus. The spectral/spatial image processing workflow to make maps of pigments and constituent painting materials is also discussed. This type of configurable hyperspectral system and the imaging processing workflow offer a new tool for the field study of wall paintings and other immovable heritage.

Technical Non-Invasive Study of an 18th Century Novo-Hispanic Panel Painting

Purísima Concepción, a large-format and unusual panel painting attributed to the 18th century, based on style and the common aspect of the visual tradition of the Virgin Mary found in the Viceroyalty of New Spain, is sheltered at the Museo Ex-convento San Agustín Acolman-INAH, México, an institution opened in late 1920, and one of the oldest museums in México. In this work, we present the material characterization of the surface layer of the painting by means of a non-invasive methodology, resulting from the combination of imaging and spectroscopic techniques. Analysis of hyperspectral images employing methods such as spectral angle mapper and principal component analysis allowed us to describe spatial distribution of the pigments and manufacturing methods, while XRF and FORS allowed us to record the complex and diverse color palette employed to achieve effects such as brightness, hue, saturation, and even the covering power of this painting.

Nondestructive Evaluation of Heritage Object Coatings with Four Hyperspectral Imaging Systems

Advanced imaging techniques can noninvasively characterise, monitor, and evaluate how conservation treatments affect cultural heritage objects. In this specific field, hyperspectral imaging allows nondestructive characterisation of materials by identifying and characterising colouring agents, binders, and protective coatings as components of an object’s original construction or later historic additions. Furthermore, hyperspectral imaging can be used to monitor deterioration or changes caused by environmental conditions. This paper examines the potential of hyperspectral imaging (HSI) for the evaluation of heritage objects. Four cameras operating in different spectral ranges were used to nondestructively scan a beehive panel painting that originated from the Slovene Ethnographic Museum collection. The specific objective of this research was to identify pigments and binders present in the samples and to spatially map the presence of these across the surface of the art piece. Merging the results with databases created in parallel using other reference methods allows for the identification of materials originally used by the artist on the panel. Later interventions to the original paintings can also be traced as part of past conservation campaigns.

OPTICS-based Unsupervised Method for Flaking Degree Evaluation on the Murals in Mogao Grottoes

In recent years, the preventive protection and restoration work of the murals in Mogao Grottoes has received extensive attention. Due to the fragility and detachment of the murals, it is necessary to study non-contact disease detection and prevention methods. In this paper, we propose an unsupervised method to accurately predict the degree of mural flaking diseases in Mogao Grottoes. The hyperspectral image (HSI) is captured by V10-PS hyperspectral camera. The proposed method includes three main steps: (1) extract the spectral features of the HSI by Principal Component Analysis (PCA) and Sparse Auto-Encoder (SAE) respectively; (2) cluster the extracted features by the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm based on the density; (3) calculate the distance between the cluster core point and the other points in the feature space and visualize the final classification result. Different from other existing hyperspectral classification works, the research proposed in this paper is the degree detection of flaking of murals. Since the degree of flaking is continuous and the work is conducted without any supervision information, the entire workflow is complex and challenging. The experimental results show the effectiveness of our method.

A four-dimensional snapshot hyperspectral video-endoscope for bio-imaging applications

Hyperspectral imaging has proven significance in bio-imaging applications and it has the ability to capture up to several hundred images of different wavelengths offering relevant spectral signatures. To use hyperspectral imaging for in vivo monitoring and diagnosis of the internal body cavities, a snapshot hyperspectral video-endoscope is required. However, such reported systems provide only about 50 wavelengths. We have developed a four-dimensional snapshot hyperspectral video-endoscope with a spectral range of 400–1000 nm, which can detect 756 wavelengths for imaging, significantly more than such systems. Capturing the three-dimensional datacube sequentially gives the fourth dimension. All these are achieved through a flexible two-dimensional to one-dimensional fiber bundle. The potential of this custom designed and fabricated compact biomedical probe is demonstrated by imaging phantom tissue samples in reflectance and fluorescence imaging modalities. It is envisaged that this novel concept and developed probe will contribute significantly towards diagnostic in vivo biomedical imaging in the near future.