Nonlinear model identification and see-through cancelation from recto-verso data

The problem of see-through cancelation in digital images of double-sided documents is addressed. We show that a nonlinear convolutional data model proposed elsewhere for moderate show-through can also be effective on strong back-to-front interferences, provided that the recto and verso pure patterns are estimated jointly. To this end, we propose a restoration algorithm that does not need any classification of the pixels. The see-through PSFs are estimated off-line, and an iterative procedure is then employed for a joint estimation of the pure patterns. This simple and fast algorithm can be used on both grayscale and color images and has proved to be very effective in real-world cases. The experimental results we report in this paper demonstrate that our algorithm outperforms the ones based on linear models with no need to tune free parameters and remains computationally inexpensive despite the nonlinear model and the iterative solution adopted. Strategies to overcome some of the residual difficulties are also envisaged.