Dr. Marina Ljubenovic Profile

Dr. Marina Ljubenovic

at Istituto Italiano di Tecnologia

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Author

Publications (2)

Proceedings Article | 8 July 2021 Presentation + Paper

Processing and analysis of THz time-domain spectroscopy imaging applied to cultural heritage

Alessia Artesani, Marina Ljubenovic, Stefano Bonetti, Arianna Traviglia

Proceedings Volume 11784, 117840E (2021) https://doi.org/10.1117/12.2593699

KEYWORDS: Terahertz radiation, Imaging spectroscopy, Cultural heritage, Terahertz spectroscopy, Data processing, Spectroscopy, Spatial resolution, Silver, Laser development, Image restoration

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This contribution discusses the improvement approaches of Terahertz Time-Domain Spectroscopy (THz-TDS) to cultural heritage objects. The technique is gathering attention in the conservation field for its ability to retrieve information on material composition and surface topography in the far-infrared region (3-300 cm⁻¹ ), but it can pose major challenges when applied on works of art. THz-TDS generates hyperspectral images that are corrupted by frequency-dependent blur and noise and are distorted by surface warping. These degradation effects can make the analysis of heterogeneous materials rather complicated because they substantially limit the frequency range where THz images have good spatial resolution and hinder the identification of materials. This work tackles these inherent limitations of the THz-TDS imaging and shows how to improve the analysis by computationally restoring the hyperspectral data. The experiments were undertaken using a bench-top THz-TDS instrument equipped with an advanced laser design and a precise mechanical delay stage that can achieve a peak dynamic range of 100 dB and a bandwidth of more than 6 THz. The exploitable region of frequencies is limited, however, between 0.2 and 3.5 THz because of the size of the THz beam waist and noise present at high frequencies. Such limitations were addressed by adopting a twofold computational strategy that involves (i) the removal of the surface warping and (ii) the application a fast deblurring-denoising algorithm for image restoration tailored to THz images. Experiments undertaken on an archaeological coin demonstrate how the approach effectively reduces degradation effects and how unwanted surface warping (whether it is a simple tilted plane or a more complex distortion) can be removed. The computational restoration of the images significantly improves their resolution, enabling the analysis above 4 THz. The application of such computational strategies makes possible the characterisation of artistic and archaeological materials up to 250 cm⁻¹ . These results concretely open new possibilities in the use of THz spectroscopy imaging on objects with complex geometries and push the boundaries of THz data and image processing.

Proceedings Article | 8 July 2021 Presentation + Paper

Improved detection of buried archaeological sites by fast hyperspectral image deblurring and denoising

Marina Ljubenović, Arianna Traviglia

Proceedings Volume 11784, 117840W (2021) https://doi.org/10.1117/12.2593713

KEYWORDS: Hyperspectral imaging, Denoising, Image processing, Image analysis, Chemical elements, Target detection, Spatial resolution, Visualization, Vegetation, Sensors

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This work introduces a novel fast hyperspectral image deblurring and denoising approach tailored to archaeological applications of remote sensing. Hyperspectral data recorded by means of airborne or satelliteborne sensors can be used to detect buried archaeological deposits as the latter have a localised impact on the physical and chemical properties of the soil and the vegetation located above them, contributing to make them structurally different from the surrounding elements. By processing and analysing hyperspectral images, archaeological photo-interpreters can detect subtle changes in the properties of ground elements that can be attributed to the presence of subterranean archaeological sites. Hyperspectral imagery, while rich in content as far as the spectral characteristics of ground elements, often lacks in spatial resolution and contains blurring degradation and noise, prominent especially in some spectral regions. The influence of blur and noise highly effects not only the quality of the visual appearance of the represented objects and compromises the interpretation process, but impacts also further processing of imagery, limiting consequently the detection of targets of interest. The methodology here presented is based on the low-rank properties of hyperspectral images and fully exploits a sparse hyperspectral data representation linked with the self-similarity characteristics of image patches (small image parts). The restoration procedure additionally includes a bend-dependent formulation of blurring degradation. The preliminary results show high performances and reduced computational complexity, and that the proposed approach is able to cope with Gaussian and Poisson noise and band-dependent blur. By removing severe noise and blur, the accurate detection and interpretation of buried structures in different shapes and sizes is thus improved. The proposed approach significantly increases the number of hyperspectral bands that can be used for further image processing and analysis, providing new avenues for features of interest discoveries in bands where they normally obscured by noise.

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