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.

Environmental control in galleries and museums is a necessity and is informed by the knowledge of ongoing processes of deterioration which can threaten the integrity and stability of artworks. Invisible dimensional changes in many works of art occur following environmental fluctuations as materials respond to the changes in humidity and temperature. The constant influence of dimensional changes usually remains invisible until displacement generates visible deterioration and irreversible damage. This paper exploits fully non contact coherent interferometry in a sequential masking procedure for visualising and studying surface deformation which is the direct effect of dimensional alterations induced by humidity changes. Surface deformation during dimensional displacements of constituent materials may occur on any artwork within an unstable environment. In this context, the presented research study explores the diagnostic potential of fully non contact sensors for the direct structural assessment of environmental effects as they occur in real time on works of art. The method is employed to characterise material responses, complementing and improving understanding of material behaviour in unstable environments.

Due to climate change a slowly increasing annual temperature may be experienced by structures. Relative humidity (RH) fluctuations affect the equiibrium moisture content of materials. Repeated RH cycling leads to mechanical failure and may endanger an object's structural integrity. Preventive conservation is based on adopting measures that will prevent fracture. Real-time interferometry allows the acquisition of sequential high-resolution full-field surface images from hygroscopic materials used in cultural heritage by recording during cycles of changing RH. The differential images allow the development of a preventive methodology directly through surface responses. Indications of the natural onset of degradation can be followed and traced before visible damage occurs, allowing preventive measures to be taken in advance. An ongoing study (Climate for Culture European project (FP7-ENV-2008-1 CfC no. 226973)) aims to experimentally classify structural deterioration as a function of acclimatization and confirm the hypothesis that surface responses before deformation can indicate deformation threshold values as reference points for the onset of RH-induced deterioration.