Nonlinear separation of show-through image mixtures using a physical model trained with ICA

Often, when we scan a document, the image from the back page shows through, due to partial transparency of the paper, giving rise to a mixture of two images. We address the problem of separating these images through the use of a physical model of the mixture process. The model is nonlinear but invertible, and we use the inverse model to perform the separation. The model is trained through the MISEP technique of nonlinear ICA. Bounded independent sources are proved to be separable through this method, apart from offset, scale and permutation indeterminacies.We compare our results with those obtained with other approaches and with different separation models that were trained with MISEP. For the latter case we test a bilinear model and MLP-based models, using both symmetry-based regularization and the more recently proposed minimal nonlinear distortion regularization. Quantitative quality measures show that the approach that we propose is superior to the other methodologies.