Restoration of recto-verso colour documents using correlated component analysis

In this paper, we consider the problem of removing see-through interferences from pairs of recto-verso documents acquired either in grayscale or RGB modality. The see-through effect is a typical degradation of historical and archival documents or manuscripts, and is caused by transparency or seeping of ink from the reverse side of the page. We formulate the problem as one of separating two individual texts, overlapped in the recto and verso maps of the colour channels through a linear convolutional mixing operator, where the mixing coefficients are unknown, while the blur kernels are assumed known a priori or estimated off-line. We exploit statistical techniques of blind source separation to estimate both the unknown model parameters and the ideal, uncorrupted images of the two document sides. We show that recently proposed Correlated Component Analysis techniques overcome the already satisfactory performance of Independent Component Analysis techniques and colour decorrelation, when the two texts are even sensibly correlated.