Restoration of Archival Documents Using a Wavelet Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Separating a Real-Life Nonlinear Image Mixture
The Journal of Machine Learning Research
International Journal on Document Analysis and Recognition
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
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors
IEEE Transactions on Information Theory
Show-through cancellation in scans of duplex printed documents
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
An information fidelity criterion for image quality assessment using natural scene statistics
IEEE Transactions on Image Processing
Using non-negative matrix factorization for removing show-through
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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This work addresses the separation of the nonlinear real-life mixture of images that occurs when a page of a document is scanned or photographed and the back page shows through. This effect can be due to partial paper transparency (show-through) and/or to bleeding of the ink through the paper (bleed-through). These two causes usually lead to mixtures with different characteristics. We propose a separation method based on the fact that the high-frequency components of the images are sparse and are stronger on one side of the paper than on the other one. The same properties were already used in nonlinear denoising source separation (DSS). However, we developed significant improvements that allow us to achieve a competitive separation quality by means of a one-shot processing, with no iteration. The method does not require the sources to be independent or the mixture to be invariant, and is suitable for separating mixtures such as those produced by bleed-through, for which we do not have an adequate physical model.