Restoration of Archival Documents Using a Wavelet Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image denoising using self-organizing map-based nonlinear independent component analysis
Neural Networks - New developments in self-organizing maps
Independent component analysis for document restoration
International Journal on Document Analysis and Recognition
MISEP - Linear and nonlinear ICA based on mutual information
The Journal of Machine Learning Research
International Journal on Document Analysis and Recognition
Nonlinear independent component analysis with minimal nonlinear distortion
Proceedings of the 24th international conference on Machine learning
Enhancement of Old Manuscript Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Using Signal Invariants to Perform Nonlinear Blind Source Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields
IEEE Transactions on Image Processing
Separation of nonlinear image mixtures by denoising source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Show-through cancellation in scans of duplex printed documents
IEEE Transactions on Image Processing
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
Nonlinear blind source separation using kernels
IEEE Transactions on Neural Networks
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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.