Radiography is usually the method of choice for the detection of hidden design details and discontinuities in art objects. However, infrared reflectography (IRR) and other techniques, such as IRR coupled with multi/hyperspectral imaging, are now equally important for the detection of overpaint, changes, and general pigment distribution. To maximize information extraction from the images, as well as attempt to optimize the operators’ image interpretation, the noise of the system should be minimized. The development of effective image noise removal techniques within both the spatial and frequency domains is an important research area in industrial radiographic testing. In this study, the dual-domain image denoising technique was used to improve hidden design details and discontinuity visualization from art object radiographs. The technique relies on generating the low and high frequency components of the image through bilateral filtering and removing noise from the low contrast component using the wavelet shrinkage technique. The proposed algorithm was successfully applied to radiographic images of cultural heritage objects. Improvements in the design detail visualization and discontinuity region detections were achieved while preserving object edge and fine detail imaging information. Improvements in the visualization of detailed design and discontinuities were observed while preserving the overall quality of the image with respect to contours and fine details on the surface of the objects. Evaluation of the collected images demonstrated that design details obtained using the dual-domain reconstructed images were better visualized. Experts reviewed opinions gathered from Level II radiography certificate holders. These experts showed that design details from the dual-domain reconstructed images were better visualized than from the original images. Different discontinuities were also better detected by the application of the technique to the images. The evaluation of the image quality enhancement shows that the pixel intensity increases almost four times through the proposed technique.
Bradley, David and Dudley Creagh (editors), Physical Techniques in the Study of Art, Archaeology and Cultural Heritage, Vols. 1 and 2, Elsevier, Amsterdam, The Netherlands, 2007.
Chatterjee, Priyam and Peyman Milanfar, “Is Denoising Dead?” IEEE Transactions on Image Processing, Vol. 19, No. 4, 2010, pp. 895–911.
Dabov, Kostadin, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian, “Image Denoising by Sparse 3-D Trans-form-domain Collaborative Filtering,” IEEE Trans on Image Process, Vol. 16, No. 8, 2007, pp. 2080–2095.
Dong, W., G. Shi, and X. Li, “Nonlocal Image Restoration with Bilateral Variance Estimation: A Low-rank Approach,” IEEE Transactions on Image Processing, Vol. 22, No. 2, 2013, pp. 700–711.
Gates, Glenn Alan, “Discovering the Material Secrets of Art: Tools of Cultural Heritage Science,” American Ceramic Society Bulletin, Vol. 93, No. 7, 2014, pp. 20–27.
IAEA, Nuclear Techniques for Cultural Heritage Research, IAEA Radiation Technology Series No. 2, International Atomic Energy Agency, Vienna, Austria, 2011.
ISO, ISO 14096-1: Non-destructive testing - Qualification of radiographic film digitisation systems - Part I: Definitions, qualitative measurements of image quality parameters, standard reference film and qualitative control, International Organization for Standardization, Geneva, Switzerland, 2005a.
ISO, ISO 14096-2: Non-destructive testing - Qualification of radiographic film digitisation systems - Part 2: Minimum requirements, International Organization for Standardization, Geneva, Switzerland, 2005b.
ISO, ISO 17636-2, Non-destructive testing of welds - Radiographic testing - Part 2: X- and gamma-ray techniques with digital detectors, International Organization for Standardization, Geneva, Switzerland, 2013.
Knaus, C., and M. Zwicker, “Progressive Image Denoising,” IEEE Transactions on Image Processing, Vol. 23, No. 7, 2014, pp. 3114–3125.
Knaus, Claude, and Matthias Zwicker, “Dual-domain Image Denoising,” Proceedings of the IEEE International Confer-ence on Image Processing (ICIP), 2013, pp. 440–444.
Lang, Janet and Andrew Middleton, Radiography of Cultural Material, 2nd edition, Routledge, Abingdon, United Kingdom, 2005.
Levin, Anat, Boaz Nadler, Fredo Durand, and William T. Freeman, “Patch Complexity, Finite Pixel Correlations and Optimal Denoising,” Proceedings of the 12th European Conference on Computer Vision: Volume Part V, Springer-Verlag GmbH, Berlin, Germany, 2012, pp. 73–86.
Montagnari Kokelj, M., M. Budinich, and C. Tuniz, editors, “Chapter 8: New X-Ray Digital Radiography and Computed Tomography for Cultural Heritage,” Science for Cultural Heritage: Technological Innovation and Case Studies in Marine and Land Archaeology in the Adriatic Region and Inland, World Scientific Publishing Co. Pte. Ltd., Singapore, 2010.
Morigi, M.P., F. Casali, M. Bettuzzi, R. Brancaccio, and V. D’Errico, “Application of X-ray Computed Tomography to Cultural Heritage Diagnostics,” Applied Physics A, Vol. 100, No. 3, 2010, pp. 653–661, DOI: 10.1007/s00339-010-5648-6.
Movafeghi, Amir, Effat Yahaghi, and Noureddin Mohm-madzadeh, “Design Detection in Cultural Heritage Lorestan Plate using the Shape-from-shading Method,” Insight, Vol. 57, No 10, 2015, pp. 576–579.
Tomasi, C., and R. Manduchi, “Bilateral Filtering for Gray and Color Images,” Sixth International Conference on Computer Vision, Bombay, India, 1998. IEEE, DOI: 10.1109/ICCV.1998.710815.
Yahaghi, Effat, Amir Movafeghi, Shokoofeh Ahmadi, Sholeh Ansari, Mehran Taheri, and Naser Rastkhah, “Cultural Heritage Object Identification by Radiography Nondestruc-tive Method and Digital Image Processing,” Applied Mechanics and Materials, Vol. 83, 2011, pp. 35–40.
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