X-ray image separation via coupled dictionary learning
This addresses the challenge of art investigation by enabling clearer analysis of double-sided paintings, though it is incremental as it builds on prior source separation methods.
The authors tackled the problem of separating mixed X-ray images from double-sided paintings by proposing a new source separation method that uses visual images from both sides to drive the process, achieving successful discrimination where state-of-the-art methods fail.
In support of art investigation, we propose a new source sepa- ration method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation meth- ods, which are based on statistical or structural incoherence of the sources, we use visual images taken from the front- and back-side of the panel to drive the separation process. The coupling of the two imaging modalities is achieved via a new multi-scale dictionary learning method. Experimental results demonstrate that our method succeeds in the discrimination of the sources, while state-of-the-art methods fail to do so.