Multi-modal dictionary learning for image separation with application in art investigation

Abstract

In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component models features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data - taken from digital acquisition of the Ghent Altarpiece (1432) - confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.

submitted to IEEE Transactions on Images Processing

References in corpus (4)

• Modified-CS: Modifying Compressive Sensing for Problems with Partially Known Support
• Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information
• Dictionary Learning for Blind One Bit Compressed Sensing
• X-ray image separation via coupled dictionary learning

Cited by in corpus (6)

• Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries
• Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information
• Mixed X-Ray Image Separation for Artworks with Concealed Designs
• Two-by-two ordinal patterns in art paintings
• Multimodal Deep Unfolding for Guided Image Super-Resolution
• Multi-modal Image Processing based on Coupled Dictionary Learning