A non-stationary density model to separate overlapped texts in degraded documents

We address the problem of the removal of a text superimposed to a more important one, in a document image, considering the two instances of canceling back-to-front interferences from recto and verso images of archival documents and of recovering the erased text in palimpsests from multispectral images. Both problems are approached through a model where the ideal images of the two texts are considered as individual source patterns, mixed through some parametric operator. To cope with occlusions, ink saturation, and space variability of the mixing operator, a data model for this problem should be nonlinear and space variant. Here, we show that if a pointwise non-stationarity is allowed, a linear model can compensate for the lack of a suitable nonlinearity and for other modeling errors.