Identify Hidden Designs in Antique Paintings Using Industrial Radiography - Journal of Research on Archaeometry
year 6, Issue 2 (2020)                   JRA 2020, 6(2): 127-140 | Back to browse issues page

XML Persian Abstract Print

1- Bu-Ali Sina University ,
2- Imam Khomeini International University
3- Valencia Polytechnic University
4- Nuclear Science and Technology Research Institute
Abstract:   (1217 Views)

In past centuries, to prevent exquisite paintings, new works of art were painted on them. To identify the hidden paintings on the board, using digital radiography as a non-destructive testing method is recommended. Some phenomenon such as photon scattering, different types of noises, etc. causes on the quality of output radiographs. In this paper, we use two pyramid-based methods, i.e., the Gaussian pyramid method and the Laplacian pyramid method, to improve the quality of radiographs. The experimental results show the effectiveness of applied methods for identifying the hidden paintings.
Full-Text [PDF 1889 kb]   (374 Downloads)    
Technical Note: Original Research | Subject: Archaeometry
Received: 2020/09/24 | Accepted: 2020/12/15 | Published: 2020/12/24 | ePublished: 2020/12/24

1. Hadadi M, Mohammdi M, Study the repair documents of Golestan, Saheb-gheranieh and Farah Abad Palace Tableaus, Ganjine-ye Asnad, Spring 2013; 23(1):86-99. [in Persian] [حدادي محمد.، محمدي آچاچلويي محسن.، بررسي اسناد تعميرات تابلوهاي کاخ گلستان، صاحبقرانيه و فرح آباد، فصلنامۀ گنجينۀ اسناد، سال بيست و سوم، دفتر اول، بهار 1392.]
2. Maev RG, Gavrilov D, Maeva A, Vodyanoy I. Modern non-destructive physical methods for paintings testing and evaluation. In Proceedings of the 9th International Conference on NDT of Art 2008 May 25 (pp. 25-30).
3. Gavrilov D, Maev RG, Almond DP. A review of imaging methods in analysis of works of art: Thermographic imaging method in art analysis. Canadian Journal of Physics. 2014; 92(4):341-64. [DOI:10.1139/cjp-2013-0128]
4. Ghanooni M. Report on the maintenance and restoration of the painting of the camp by the river by Kamal Ol-Molk. Library, Museum and Document Center Islamic Consultative Assembly, 2019. [قانوني محسن.، گزارش نگاهداشت و مرمت تابلو نقاشي منظره اردوگاه در کنار رودخانه اثر کمال الملک، کارگاه مرمت موزه مجلس، كتابخانه موزه و مركز اسناد مجلس شوراي اسلامي؛ 1398.]
5. Afzali N, Vatan-Parast R. Pathology and erosion analysis of oil paintings on Kamal-ol-Molk canvas in Golestan Palace. Athar Journal. Spring 2017, 38(76): 3-76. [افضلي نرگس.، وطن‏پرست رسول، آسيب‏نگاري، آسيب‏شناسي و تحليل فرسودگي‏هاي نقاشي‏هاي رنگ‏روغن روي بوم كمال الملك در كاخ گلستان، اثر فصلنامه علمي فني هنري؛ بهار 1396.]
6. Baronti S, Casini A, Lotti F, Porcinai S. Multispectral imaging system for the mapping of pigments in works of art by use of principal-component analysis. Applied optics. 1998 Mar 10; 37(8):1299-309. [DOI:10.1364/AO.37.001299]
7. Casini A, Lotti F, Picollo M, Stefani L, Buzzegoli E. Image spectroscopy mapping technique for noninvasive analysis of paintings. Studies in conservation. 1999 Jan 1; 44(1):39-48. [DOI:10.1179/sic.1999.44.1.39]
8. Balas C, Papadakis V, Papadakis N, Papadakis A, Vazgiouraki E, Themelis G. A novel hyper-spectral imaging apparatus for the non-destructive analysis of objects of artistic and historic value. Journal of Cultural Heritage. 2003 Jan 1; 4: 330-7. [DOI:10.1016/S1296-2074(02)01216-5]
9. Fischer C, Kakoulli I. Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications. Studies in Conservation. 2006 Jun 1; 51(sup1):3-16. [DOI:10.1179/sic.2006.51.Supplement-1.3]
10. Vilaseca M, Pujol J, Arjona M, de Lasarte M. Multispectral system for reflectance reconstruction in the near-infrared region. Applied optics. 2006 Jun 20; 45(18):4241-53. [DOI:10.1364/AO.45.004241]
11. Cristoforetti G, Legnaioli S, Palleschi V, Salvetti A, Tognoni E. Optical chemical sensors for cultural heritage. InOptical Chemical Sensors 2006 (pp. 515-526). Springer, Dordrecht. [DOI:10.1007/1-4020-4611-1_25]
12. Bonifazzi C, Carcagnì P, Fontana R, Greco M, Mastroianni M, Materazzi M, Pampaloni E, Pezzati L, Bencini D. A scanning device for VIS-NIR multispectral imaging of paintings. Journal of Optics A: Pure and Applied Optics. 2008 May 1; 10 (6):064011. [DOI:10.1088/1464-4258/10/6/064011]
13. Arbabi F. Restoration of several pins excavated from Salmabad village in Khosf (South Khorasan). Bi-annual Journal of Restoration Science and Culture Heritage; Spring 2018, 5(9):1-11. [in Persian] [اربابي فائزه، مرمت چند نمونه سنجاق مکشوفه از سلم آبادِ خوسف (خراسان جنوبي). دوفصلنامه تخصصي دانش مرمت و ميراث فرهنگي؛ 1397.]
14. Taft WS, Mayer JW. The science of paintings. Measurement Science and Technology, 2001. [DOI:10.1007/b97567]
15. Berezhnoy IE, Postma EO, van den Herik HJ. Authentic: computerized brushstroke analysis. In2005 IEEE International Conference on Multimedia and Expo 2005 Jul 6 (pp. 1586-1588).
16. B. Raj, "Practical N.D.T.", Alpha Science, 3th edition, 2007.
17. Cortz L. Non-Destructive Testing. ASM International; 1995.
18. Toyserkani H. Nondestructive evaluations, Jahad Daneshgahi, Isfahan, 2015. [in Persian] [تويسرکاني ح.، بررسي¬هاي غيرمخرب. اصفهان: جهاد دانشگاهي؛ 1394.]
19. Bridgman CF. The amazing patent on the radiography of paintings. Studies in Conservation. 1964 Nov 1; 9(4):135-9. [DOI:10.1179/sic.1964.023]
20. Nacereddine N, Drai R, Benchaala A. Weld defect extraction and identification in radiograms based neural networks. InProc. IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, Crete, Greece 2002 Jun 25 (pp. 38-43).
21. Movafeghi A, Kargarnovin MH, Soltanian-Zadeh H, Taheri M, Ghasemi F, Rokrok B, Edalati K, Rastkhah N. Flaw detection improvement of digitised radiographs by morphological transformations. Insight-Non-Destructive Testing and Condition Monitoring. 2005 Oct 1; 47(10):625-30. [DOI:10.1784/insi.2005.47.10.625]
22. Felisberto MK, Lopes HS, Centeno TM, De Arruda LV. An object detection and recognition system for weld bead extraction from digital radiographs. Computer Vision and Image Understanding. 2006 Jun 1; 102(3):238-49. [DOI:10.1016/j.cviu.2006.02.004]
23. Daubechies I, Defrise M, De Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences. 2004 Nov; 57(11):1413-57. [DOI:10.1002/cpa.20042]
24. Carrasco MA, Mery D. Segmentation of welding defects using a robust algorithm. Materials Evaluation. 2004; 62(11):1142-7.
25. Carvalho AA, Suita RC, Silva RR, Rebello JM. Evaluation of the relevant features of welding defects in radiographic inspection. Materials Research. 2003 Jun; 6(3):427-32. [DOI:10.1590/S1516-14392003000300019]
26. Lampert CH, Blaschko MB, Hofmann T. Efficient subwindow search: A branch and bound framework for object localization. IEEE transactions on pattern analysis and machine intelligence. 2009 Jul 17; 31(12):2129-42. [DOI:10.1109/TPAMI.2009.144]
27. Mery D, Riffo V, Zuccar I, Pieringer C. Automated X-ray object recognition using an efficient search algorithm in multiple views. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops 2013 (pp. 368-374). [DOI:10.1109/CVPRW.2013.62]
28. Liao X, Yuan Z, Zheng Q, Yin Q, Zhang D, Zhao J. Multi-scale and shape constrained localized region-based active contour segmentation of uterine fibroid ultrasound images in HIFU therapy. PloS one. 2014 Jul 25; 9(7):e103334. [DOI:10.1371/journal.pone.0103334]
29. Leavline EJ, Sutha S. Design of FIR Filters for Fast Multiscale Directional Filter Banks. International Journal of u-and e-Service, Science and Technology. 2014; 7(5):221-34. [DOI:10.14257/ijunesst.2014.7.5.20]

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.