Joint blind separation and restoration of mixed degraded images for document analysis
Anna Tonazzini, Ivan Gerace, Francesco Cricco · 2005
We consider the problem of extracting clean images from noisy mixtures of images degraded by blur operators. This special case of source separation arises, for instance, when analyzing document images showing bleed-through or show-through. We propose to jointly perform demixing and deblurring by augmenting blind source separation with a step of image restoration. Within the independent component analysis (ICA) approach, i.e. assuming the statistical independence of the sources, we adopt a Bayesian formulation where the priors on the ideal images are given in the form of Markov random field (MRF), and a MAP estimation is employed for the joint recovery of the mixing matrix and the images. We show that taking into account the blur model and a proper image model improves the separation process and makes it more robust against noise. Preliminary results on synthetic examples of documents exhibiting bleed-through are provided, considering edge-preserving priors that are suitable to describe text images.