This paper presents a new processing method for denoising interferograms obtained by digital holographic speckle pattern interferometry (DHSPI) to serve in the structural diagnosis of artworks. DHSPI is a non-destructive and non-contact imaging method that has been successfully applied to the structural diagnosis of artworks by detecting hidden subsurface defects and quantifying the deformation directly from the surface illuminated by coherent light. The spatial information of structural defects is mostly delivered as local distortions interrupting the smooth distribution of intensity during the phase-shifted formation of fringe patterns. Distortions in fringe patterns are recorded and observed from the estimated wrapped phase map, but the inevitable electronic speckle noise directly affects the quality of the image and consequently the assessment of defects. An effective method for denoising DHSPI wrapped phase based on deep learning is presented in this paper. Although a related method applied to interferometry for reducing Gaussian noise has been introduced, it is not suitable for application in DHSPI to reduce speckle noise. Thus, the paper proposes a new method to remove speckle noise in the wrapped phase. Simulated data and experimental captured data from samples prove that the proposed method can effectively reduce the speckle noise of the DHSPI wrapped phase to extract the desired information. The proposed method is helpful for accurately detecting defects in complex defect topography maps and may help to accelerate defect detection and characterization procedures.

Relative humidity (RH) changes are a natural environmental effect that forces organic materials to a constant cycle of achieving equilibrium. The present work is part of an ongoing research based on the hypothesis that the inevitable deleterious effects of the RH natural cycle may be prevented or minimized if a deformation threshold is assigned to each monitored endangered object prior to exposure to structural damage. In this paper the characterization of the behavior of a softwood sample (1.0 cm thick) submitted to RH abrupt cycles has been performed, in terms of mass and rate of displacement of the surface. The exemplary study is based on the concept of recording the RH impact directly from the material surface, allowing us to identify diversity in reaction with time, which in turn could determine the onset of structural changes prior to irreversible damage. The RH impact is measured as surface deformation from interference fringes, using a custom-made real time holography system with interferometric precision termed digital holographic speckle-pattern interferometry (DHSPI). The main observations presented here are a hysteresis in the dynamic sorption isotherm and a greater rate of displacement during the drying. A long-term experiment was performed to identify signs of ageing of the sample. The evolution of the mass and the rate of displacement stayed similar, an offset with an interesting behavior was observed and highlights ageing of wood. In order to produce a future preventive model for distinct art objects it is necessary to determine a deformation threshold for each material. In this context the study was planned to continue with organic samples bearing variable density and thickness under longer-term RH cycles and monitoring until the samples show visible signs of irreversible damage.

This paper presents first laboratory results of a combined approach carried out by the use of three different portable non-invasive electromagnetic methods: Digital holographic speckle pattern interferometry (DHSPI), stimulated infrared thermography (SIRT) and holographic subsurface radar (HSR), proposed for the analysis of a custom-built wall mosaic model. The model reproduces a series of defects (e.g., cracks, voids, detachments), simulating common deteriorated, restored or reshuffled areas in wall mosaics. DHSPI and SIRT, already well known in the field of non-destructive (NDT) methods, are full-field contactless techniques, providing complementary information on the subsurface hidden discontinuities. The use of DHSPI, based on optical imaging and interferometry, provides remote control and visualization of surface micro-deformation after induced thermal stress, while the use of SIRT allows visualization of thermal energy diffusion in the surface upon the induced thermal stress. DHSPI and SIRT data are complemented by the use of HSR, a contact method that provides localized information about the distribution of contrasts in dielectric permittivity and related possible anomalies. The experimental results, made by the combined use of these methods to the identification of the known anomalies in the mosaic model, are presented and discussed here as a contribution in the development of an efficient non-invasive approach to the in-situ subsurface analysis of ancient wall mosaics.