scholarly journals Looking Through Paintings by Combining Hyper-Spectral Imaging and Pulse-Compression Thermography

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4335 ◽  
Author(s):  
Stefano Laureti ◽  
Hamed Malekmohammadi ◽  
Muhammad Khalid Rizwan ◽  
Pietro Burrascano ◽  
Stefano Sfarra ◽  
...  

The use of different spectral bands in the inspection of artworks is highly recommended to identify the maximum number of defects/anomalies (i.e., the targets), whose presence ought to be known before any possible restoration action. Although an artwork cannot be considered as a composite material in which the zero-defect theory is usually followed by scientists, it is possible to state that the preservation of a multi-layered structure fabricated by the artist’s hands is based on a methodological analysis, where the use of non-destructive testing methods is highly desirable. In this paper, the infrared thermography and hyperspectral imaging methods were applied to identify both fabricated and non-fabricated targets in a canvas painting mocking up the famous character “Venus” by Botticelli. The pulse-compression thermography technique was used to retrieve info about the inner structure of the sample and low power light-emitting diode (LED) chips, whose emission was modulated via a pseudo-noise sequence, were exploited as the heat source for minimizing the heat radiated on the sample surface. Hyper-spectral imaging was employed to detect surface and subsurface features such as pentimenti and facial contours. The results demonstrate how the application of statistical algorithms (i.e., principal component and independent component analyses) maximized the number of targets retrieved during the post-acquisition steps for both the employed techniques. Finally, the best results obtained by both techniques and post-processing methods were fused together, resulting in a clear targets map, in which both the surface, subsurface and deeper information are all shown at a glance.

Author(s):  
Bathula Namratha

Spectroscopy deals with how light behave in the target and recognize materials bases on their different spectral signatures. Spectrum describes the amount and range of radiation that is emitted, reflected or transmitted from the target. Hyper spectral data acquisition and exploitation by providing imaging sensors and software solutions covering hundreds of spectral bands from UV-VIS to SWIS is used to observe Earth, atmospheric science, space situation awareness etc. The work focuses primarily on hyper spectral imaging, data acquisition methods, Image resolution improvement strategies.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3738
Author(s):  
Muhammad Saad Shaikh ◽  
Keyvan Jaferzadeh ◽  
Benny Thörnberg ◽  
Johan Casselgren

In this paper, we present a hyper-spectral imaging system and practical calibration procedure using a low-cost calibration reference made of polytetrafluoroethylene. The imaging system includes a hyperspectral camera and an active source of illumination with a variable spectral distribution of intensity. The calibration reference is used to measure the relative reflectance of any material surface independent of the spectral distribution of light and camera sensitivity. Winter road conditions are taken as a test application, and several spectral images of snow, icy asphalt, dry asphalt, and wet asphalt were made at different exposure times using different illumination spectra. Graphs showing measured relative reflectance for different road conditions support the conclusion that measurements are independent of illumination. Principal component analysis of the acquired spectral data for road conditions shows well separated data clusters, demonstrating the system’s suitability for material classification.


2016 ◽  
Vol 22 (2) ◽  
pp. 267-277
Author(s):  
Baicheng Li ◽  
Baolu Hou ◽  
Yao Zhou ◽  
Mantong Zhao ◽  
Dawei Zhang ◽  
...  

2010 ◽  
Author(s):  
Weiming Xu ◽  
Liyin Yuan ◽  
Ying Lin ◽  
Zhiping He ◽  
Rong Shu ◽  
...  

2021 ◽  
Author(s):  
Eleni Aloupogianni ◽  
Masahiro Ishikawa ◽  
Takaya Ichimura ◽  
Atsushi Sasaki ◽  
Naoki Kobayashi ◽  
...  

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