# Beyond Human Vision: Unlocking the Potential of Augmented Reality for Spectral Imaging
## 1 Introduction  
In their own yet intertwined ways, spectral imaging and augmented reality may be considered as enhancers of human perception with a wide range of applications across scientific and industrial fields, particularly in domains such as mineral exploration, cultural heritage diagnostics, and artifact conservation. On the one hand, spectral imaging captures information from a scene beyond classical RGB imaging, mapping it onto a two-dimensional (2D) image. By leveraging the potential of the spectroscopy technique, it may provide information regarding the spatial distribution of chemical elements or sample composition, revealing hidden features that are invisible to the human eye. On the other hand, Augmented Reality (AR) is a technology that enhances the perception of reality by projecting digital information on the physical world via mixed-reality displays or projection solutions. Recent technological advancements have unlocked the potential for new immersive and interactive systems that hold promise as tools for harnessing the full rangeof human sensory perception to interact with digital data [1]. Thus, while spectral imaging and AR have different purposes, they are connected in the way they provide a user-centric extension of reality and enhance human capabilities [2; 3]. Within this context, it also makes sense to explore synergistic pathways for both technologies, in particular, exploring how AR can enhance the visualization and interpretation of spectral imaging datasets. Conceptually, the combination of spectral imaging with AR presents a unique opportunity to assist and enhance the capabilities of the individual tools in a bidirectional manner, i.e., (i) AR assisting existing spectral imaging and (ii) spectral imaging enhancing AR common applications. From the perspective of spectral imaging, spectroscopy techniques may offer in situ, sensitive, and rapid spatial maps of chemical analyses, with minimal sample preparation and utilizing micro and non-destructive analytical methods [4; 5]. Although spectral imaging results are typically presented as 2D images, a recent approach [6] suggested a methodology for integrating 2D surface maps into 3D models of the sample, which may be constructed using various scanning solutions, such as photogrammetry, LIDAR, or laser scanning [7; 8; 9; 10]. The results demonstrate the possibility of integrating both models in a seamless manner, allowing an external user with no access to the physical sample to locate the data better, look from different perspectives, and explore scientific visualization tools to interact with the spectral dataset in non-trivial manners. For instance, viewing spectral data in 3D allows to see how chemical elements are distributed across the surface texture of a sample, making it easier to correlate specific elements or signal variations with physical characteristics, such as surface roughness or material texture. This type of immersive visualization supports more intuitive pattern recognition and material interpretation compared to static 2D interfaces, where spectral maps are typically analyzed on flat screens without spatial context or physical scale. While such tools are well-established, they often fragment the user attention and require cognitive effort to mentally reconstruct the 3D geometry or correlate spectral data with real-world locations. In this sense, bridging these 3D digital models to real environments via AR offers a pathway for a new data analysis environment, providing contextual interpretation with unique interaction and rescaling capabilities as explored in other areas [3]. In addition, while most of the work on the topic of spectral imaging focuses on the acquisition of images at the surface of a sample, specific techniques or applications allow access to volumetric information up to a certain degree [11; 12; 13; 14].