نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه فیزیک، دانشکده علوم پایه، دانشگاه بین‌المللی امام خمینی (ره)، صندوق پستی: 96818-34148، قزوین- ایران

2 پژوهشکده رآکتور و ایمنی هسته‌ای، پژوهشگاه علوم و فنون هسته‌ای، سازمان انرژی اتمی ایران، صندوق پستی: 1339-14155، تهران-

3 مؤسسه دانشگاهی مرمت و بازسازی، دانشگاه پلی‌تکنیک والنسیا، والنسیا- اسپانیا

10.24200/nst.2021.1306

چکیده

آسیب‌های عمده در تابلوهای هنری شامل پارگی، خراشیدگی، تاب برداشتن و... است که به‌­دلیل تغییرات درجه حرارت، قرارگیری در محل مرطوب و ساییدگی به‌وجود می‌آیند. تشخیص محل دقیق آسیب از طریق پرتونگاری صنعتی، که از آزمون‌های غیرمخرب می‌باشد، امکان‌­پذیر است. تصاویر تهیه شده در آزمون پرتونگاری، به‌دلیل پراکندگی ذاتی پرتو ایکس، عوامل هندسی مانند اندازه چشمه پرتو، ضخامت قطعه و فاصله چشمه تا فیلم (SFD) ممکن است وضوح پایینی داشته باشند. تشخیص دقیق شکل و اندازه آسیب‌ها در برخی موارد با دشواری انجام می‌شود. روش‌های پردازش تصویر به­‌عنوان ابزار کمکی برای افزایش کیفیت تصویر و تفسیر سهل‌تر، می‌توانند به‌­کار گرفته شوند. در این پژوهش، برای شناسایی بهتر محل آسیب‌های تابلوهای هنری از صافی‌ گابور که مبتنی بر تجزیه‌ اطلاعات تصویر با کمک موجک گابور با سطح آستانه خودکار است، برای کاهش عدم وضوح و افزایش کنتراست استفاده شده است. نتایج نشان داد تصاویر بازسازی شده‌ حاصل از این الگوریتم دارای کنتراست بهتری بوده و آسیب‌ها و نشانه‌ها واضح‌تر از تصویر اوّلیه می‌باشند. این روش می‌تواند کمک شایانی به کارشناسان مرمت برای مرمت تابلوهای هنری به­‌شمار رود.

کلیدواژه‌ها

عنوان مقاله [English]

Defects identifying of valuable artistic paintings by industrial radiography

نویسندگان [English]

  • S. M. Ghyasi 1
  • E. Yahaghi 1
  • A. Movafeghi 2
  • J. A. Madrid Garcia 3

1 Department of Physics, Faculty of Basic Sciences, Imam Khomeini International University, P.O.BOX: 34148-96818, Qazvin, Iran

2 Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box: 14155-1339, Tehran- Iran

3 University Institute for the Restoration of the Patrimony, Universitat Politècnica de València, Valencia, Spain

چکیده [English]

The significant defects in the artistic paints are rupture, scratches, and twisting, which are caused by variations in temperature, exposure to moisture, and erosion. Industrial radiography can detect the defect locations, which is a non-destructive test, can be carried out. In radiography testing (RT), the produced radiographs may suffer from some degree of blurriness and low contrast due to the inherent scattering of X-ray, geometric factors such as the size of the beam source, the thickness of the part, and the source to film distance (SFD). The diagnosis of the shapes and sizes of the damages is difficult in some cases. Image processing methods can be used as additional tools for enhancing image quality and more accessible interpretation. In the present study, a Gabor filter technique, based on wavelet analysis with an automatic threshold level, was used to reduce the fogginess of radiographs. The reconstructed images of the proposed algorithm have better contrast, and the defects and signs are more precise than the original image. This algorithm can help the restoration experts for repairing the artistic paintings.

کلیدواژه‌ها [English]

  • Industrial radiography
  • Defect Identifying
  • Artistic Painting
  • Gabor filtering
  • Image processing
1.     J. Lang and A. Middleton, Radiography of Cultural Material, Second Edition, Elsevier Butterworth-Heinemann, )2005).

 

2.  J. A. Madrid Garcia, E. Yahaghi, A. Movafeghi‌. Improvement of the digital radiographic images of old paintings on wooden support through the anisotropic diffusion methodJournal of Cultural Heritage, 49, 115-122, (2021)(In Persian).

 

3.    Sh. K. Rutledge, and et al., Atomic Oxygen Treatment as a Method of Recovering Smoke-Damaged PaintingsJournal of the American Institute for Conservation, 39 (1), (2000).

 

4.   J.A. Madrid, Use of telemetry x-ray techniques in large-size pictorial worksin Ge-conservation, 5, 101−109, (2013).

 

5.  D. J. Yusà-Marco, B. D. Atienza and J. A. Madrid, Review of the work of Antonio Bisquert carried out in the city of Teruel through radiographic analysis and characterization of materials by SEM / EDXGe-Conservacion, 1(16):6-22, (2019).

 

6.    B. Appelbaum, Conservation treatment methodology, 1st edition. Softback with stiff wrappers, (2011).

 

7.    E. Negahdarzadeh, and et al., Diagnosis of design and defects in radiography of ceramic antique objects using the wavelet- domain hidden Markov models, Journal of Cultural Heritage, 35, 56-63, https://doi.org/10.1016/j.culher.2018.07.005 ‌(2019).

 

8.   N. Nacereddine, R. Drai and A. Benchaala, Weld defect extraction and identification in radiograms based neural networks, (IASTED International Conference on Signal Processing, Pattern Recognition, and Applications 2002), Crete, Greece, June, pp 38-43, (2002.)

 

9.   J. Rouhi, Development of the Theories of Cultural Heritage Conservation in Europe: A Survey of 19th and 20th Century Theories, In Proceedings of the 4th International Congress on Civil Engineering, Architecture & Urban Development, Tehran, Iran, 2729,  (2016)

 

10. Serrano, M. de Diego, C. Conde and E. Cabello, Analysis of variance of Gabor filter banks parameters for optimal face recognitionPattern Recognition Letters, 1998-2008, (2011).

 

11.   R. J. Ferrari, and et al., Analysis of Asymmetry in Mammograms via Directional Filtering With Gabor WaveletsIEEE Trans. on Medical  Imaging, 20 (9), 953-964, (2001).

 

12.  ‌J. Oh, S. Choi, Selective generation of Gabor features for fast face recognition on mobile devicesPattern Recognition Letters, 34, (2013).

 

13. J. R. Movellan, Tutorial on Gabor  Filterhttps://inc.ucsd.edu/mplab/tutorials (2008).

 

14. P. Kruizinga, N. Petkov and S. E. Grigorescu, Comparison of texture features based on Gabor filtersProceedings of the 10th International Conference on Image Analysis and processing, (1999).

 

15.  S. Sheykh Rabiee , B. Rokrok, E. Yahaghi, B. Arezabak, Improvement of Security Systems by Enhancing the Detection of Objects in Baggage X-Ray Radiography ImagesJournal of Nuclear Science and Technology (JonSat), 39 (2), 19-37, 10.24200/NST.2018.1044, (2018). (In Persian)

 

16. ‌ P. Kovesi, Phase Preserving Denoising of Images. The Australian Pattern Recognition Society Conference: DICTA'99. Perth WA. 212-217, (1999).

1.     J. Lang and A. Middleton, Radiography of Cultural Material, Second Edition, Elsevier Butterworth-Heinemann, )2005).
 
2.  J. A. Madrid Garcia, E. Yahaghi, A. Movafeghi‌. Improvement of the digital radiographic images of old paintings on wooden support through the anisotropic diffusion methodJournal of Cultural Heritage, 49, 115-122, (2021)(In Persian).
 
3.    Sh. K. Rutledge, and et al., Atomic Oxygen Treatment as a Method of Recovering Smoke-Damaged PaintingsJournal of the American Institute for Conservation, 39 (1), (2000).
 
4.   J.A. Madrid, Use of telemetry x-ray techniques in large-size pictorial worksin Ge-conservation, 5, 101−109, (2013).
 
5.  D. J. Yusà-Marco, B. D. Atienza and J. A. Madrid, Review of the work of Antonio Bisquert carried out in the city of Teruel through radiographic analysis and characterization of materials by SEM / EDXGe-Conservacion, 1(16):6-22, (2019).
 
6.    B. Appelbaum, Conservation treatment methodology, 1st edition. Softback with stiff wrappers, (2011).
 
7.    E. Negahdarzadeh, and et al., Diagnosis of design and defects in radiography of ceramic antique objects using the wavelet- domain hidden Markov models, Journal of Cultural Heritage, 35, 56-63, https://doi.org/10.1016/j.culher.2018.07.005 ‌(2019).
 
8.   N. Nacereddine, R. Drai and A. Benchaala, Weld defect extraction and identification in radiograms based neural networks(IASTED International Conference on Signal Processing, Pattern Recognition, and Applications 2002), Crete, Greece, June, pp 38-43, (2002.)
 
9.   J. Rouhi, Development of the Theories of Cultural Heritage Conservation in Europe: A Survey of 19th and 20th Century Theories, In Proceedings of the 4th International Congress on Civil Engineering, Architecture & Urban Development, Tehran, Iran, 2729,  (2016)
 
10. Serrano, M. de Diego, C. Conde and E. Cabello, Analysis of variance of Gabor filter banks parameters for optimal face recognitionPattern Recognition Letters, 1998-2008, (2011).
 
11.   R. J. Ferrari, and et al., Analysis of Asymmetry in Mammograms via Directional Filtering With Gabor WaveletsIEEE Trans. on Medical  Imaging, 20 (9), 953-964, (2001).
 
12.  ‌J. Oh, S. Choi, Selective generation of Gabor features for fast face recognition on mobile devicesPattern Recognition Letters, 34, (2013).
 
13. J. R. Movellan, Tutorial on Gabor  Filter,  https://inc.ucsd.edu/mplab/tutorials (2008).
 
14. P. Kruizinga, N. Petkov and S. E. Grigorescu, Comparison of texture features based on Gabor filtersProceedings of the 10th International Conference on Image Analysis and processing, (1999).
 
15.  S. Sheykh Rabiee , B. Rokrok, E. Yahaghi, B. Arezabak, Improvement of Security Systems by Enhancing the Detection of Objects in Baggage X-Ray Radiography ImagesJournal of Nuclear Science and Technology (JonSat), 39 (2), 19-37, 10.24200/NST.2018.1044, (2018). (In Persian)
 
16. ‌ P. Kovesi, Phase Preserving Denoising of Images. The Australian Pattern Recognition Society Conference: DICTA'99Perth WA. 212-217, (1999).