Junliang Dong

Terahertz frequency-wavelet domain deconvolution for stratigraphic and subsurface investigation of art painting

Terahertz frequency-wavelet deconvolution is utilized specifically for the stratigraphic and subsurface investigation of art paintings with terahertz reflective imaging. In order to resolve the optically thin paint layers, a deconvolution technique is enhanced by the combination of frequency-domain filtering and stationary wavelet shrinkage, and applied to investigate a mid-20th century Italian oil painting on paperboard, After Fishing, by Ausonio Tanda. Based on the deconvolved terahertz data, the stratigraphy of the painting including the paint layers is reconstructed and subsurface features are clearly revealed, demonstrating that terahertz frequency-wavelet deconvolution can be an effective tool to characterize stratified systems with optically thin layers.

Terahertz Quantitative Nondestructive Evaluation of Failure Modes in Polymer-Coated Steel

Terahertz reflective imaging is applied to characterize the failure modes in a polymer coating on a steel plate. The coating was initially scratched, then after accelerated aging, several types of failure have occurred. In order to resolve the thin coating (~50 μm), terahertz frequency-wavelet domain deconvolution is implemented. With the deconvolved signals, the temporally overlapping echoes of the incident, roughly single-cycle terahertz pulse are clearly resolved, and three important failure modes, viz. corrosion, delamination, and blistering, are characterized quantitatively. Terahertz images in three dimensions clearly exhibit the coating thickness distribution across the entire damaged coating, highlighting the terahertz features associated with different failure modes, thus demonstrating that terahertz imaging can be considered as an effective modality for characterizing damage mechanisms in polymer coatings on metals.

Terahertz Superresolution Stratigraphic Characterization of Multilayered Structures Using Sparse Deconvolution

Terahertz sparse deconvolution based on an iterative shrinkage algorithm is presented in this study to characterize multilayered structures. With an upsampling approach, sparse deconvolution with superresolution is developed to overcome the time resolution limited by the sampling period in the measurement and increase the precision of the estimation of echo arrival times. A simple but effective time-domain model for describing the temporal pulse spreading due to the frequency-dependent loss is also designed and introduced into the algorithm, which greatly improves the performance of sparse deconvolution in processing time-varying pulses during the propagation of terahertz waves in materials. Numerical simulations and experimental measurements verify the algorithms and show that sparse deconvolution can be considered as an effective tool for terahertz nondestructive characterization of multilayered structures.

Global mapping of stratigraphy of an old-master painting using sparsity-based terahertz reflectometry

The process by which art paintings are produced typically involves the successive applications of preparatory and paint layers to a canvas or other support; however, there is an absence of nondestructive modalities to provide a global mapping of the stratigraphy, information that is crucial for evaluation of its authenticity and attribution, for insights into historical or artist-specific techniques, as well as for conservation. We demonstrate sparsity-based terahertz reflectometry can be applied to extract a detailed 3D mapping of the layer structure of the 17th century easel painting Madonna in Preghiera by the workshop of Giovanni Battista Salvi da Sassoferrato, in which the structure of the canvas support, the ground, imprimatura, underpainting, pictorial, and varnish layers are identified quantitatively. In addition, a hitherto unidentified restoration of the varnish has been found. Our approach unlocks the full promise of terahertz reflectometry to provide a global and detailed account of an easel painting’s stratigraphy by exploiting the sparse deconvolution, without which terahertz reflectometry in the past has only provided a meager tool for the characterization of paintings with paint-layer thicknesses smaller than 50 μm. The proposed modality can also be employed across a broad range of applications in nondestructive testing and biomedical imaging.

Terahertz pulsed imaging reveals the stratigraphy of a seventeenth-century oil painting

Terahertz pulsed imaging has attracted considerable interest for revealing the stratigraphy and hidden features of art paintings. The reconstruction of the stratigraphy is based on the precise extraction of THz echo parameters from the reflected signals. Several historical panel paintings and wall paintings have been well studied by THz reflective imaging, in which the detailed stratigraphy has been successfully revealed. To our knowledge, however, the stratigraphy of oil paintings has not been clearly uncovered by THz imaging, since the paint layers in an oil painting on canvas, especially for the 16th and 17th century art works, are usually very thin (~10 μm) in the THz regime. Therefore, in order to improve the performance of THz imaging, advanced signal-processing techniques with higher depth-resolution are still needed. In this study, THz reflective imaging is employed to reveal for the first time the detailed stratigraphy of a 17th century Italian oil painting on canvas. The paint layers on the supporting canvas are very thin in the THz regime, as the THz echoes corresponding to the stratigraphy totally overlap in the first cycle of the reflected THz signal. THz sparse deconvolution based on an iterative shrinkage algorithm is utilized to resolve the overlapping echoes. Based on the deconvolved signals, the detailed stratigraphy of this oil painting on canvas, including the varnish, pictorial, underdrawing, and ground layers, is successfully revealed. The THz C- and B-scans based on the THz deconvolved signals also enable us to reveal the features of each layer. Our results thus enhance the capability of terahertz imaging to perform detailed analysis and diagnostics of historical oil paintings on canvas with foreseen applications for the study of the artist’s technique and for authentication.

Terahertz imaging for nondestructive testing of materials for aerospace, automotive, and energy (Conference Presentation)

In the last ten years terahertz techniques have become increasingly common laboratory and industrial tools. This progress has been made possible by over thirty years of concentrated effort. In this talk we discuss our recent work combining time-domain terahertz imaging with advanced signal-processing to obtain unprecedented depth information in a nondestructive fashion about subsurface damage in both glass and carbon fiber composites and in coatings on metals. In addition, we present an example characterizing the stratigraphy of an art painting to illustrate the technique to measure thicknesses in a multilayer coating. Other optically opaque materials, including polymers, glass, textiles, paper, and ceramics, are transparent to terahertz radiation, and thus terahertz imaging may access information in these materials below the surface. Signal processing techniques are needed to unleash the power of terahertz imaging to measure thin layers of thickness on the order of 10 microns. These approaches permit us to gain information about thin layers that are obscured in the raw signals. That is, when the time duration of the terahertz pulses is longer than the optical delay to traverse a given layer, the terahertz echoes associated with reflections off the various interfaces may temporally overlap. Specifically, we have successfully employed frequency-wavelet domain deconvolution, sparse deconvolution, and autoregressive deconvolution for a range of problems.

Terahertz imaging for subsurface investigation of art paintings

Terahertz (THz) reflective imaging is applied to the stratigraphic and subsurface investigation of oil paintings, with a focus on the mid-20th century Italian painting, ‘After Fishing’, by Ausonio Tanda. THz frequency-wavelet domain deconvolution, which is an enhanced deconvolution technique combining frequency-domain filtering and stationary wavelet shrinkage, is utilized to resolve the optically thin paint layers or brush strokes. Based on the deconvolved terahertz data, the stratigraphy of the painting including the paint layers is reconstructed and subsurface features are clearly revealed. Specifically, THz C-scans and B-scans are analyzed based on different types of deconvolved signals to investigate the subsurface features of the painting, including the identification of regions with more than one paint layer, the refractive-index difference between paint layers, and the distribution of the paint-layer thickness. In addition, THz images are compared with X-ray images. The THz image of the thickness distribution of the paint exhibits a high degree of correlation with the X-ray transmission image, but THz images also reveal defects in the paperboard that cannot be identified in the X-ray image. Therefore, our results demonstrate that THz imaging can be considered as an effective tool for the stratigraphic and subsurface investigation of art paintings. They also open up the way for the use of non-ionizing THz imaging as a potential substitute for ionizing X-ray analysis in nondestructive evaluation of art paintings.