Enhancement of degraded images of binary shapes is an important task in many image processing applications, *e.g.* to provide appropriate image quality for optical character recognition. Although many image restoration methods can be found in the literature, most of them are developed for grayscale images. In this paper we propose a novel binary image restoration algorithm. As a first step, it restores the projections of the shape using 1-dimensional deconvolution, then reconstructs the image from these projections using a discrete tomography technique. The method does not require any parameter setting or prior knowledge like an estimation of the signal-to-noise ratio. Numerical experiments on a synthetic dataset show that the proposed algorithm is robust to the level of the noise. The efficiency of the method has also been demonstrated on real out-of-focus alphanumeric images.

1 aNemeth, Jozsef1 aBalázs, Péter1 aBlanc-Talon, Jacques1 aKasinski, Andrzej1 aPhilips, Wilfried1 aPopescu, Dan1 aScheunders, Paul uhttps://www.inf.u-szeged.hu/en/publication/restoration-of-blurred-binary-images-using-discrete-tomography01225nas a2200205 4500008004100000245005500041210005500096260005200151300001400203490000900217520059400226100002000820700002300840700002500863700002200888700001700910700002100927700002000948856005100968 2012 eng d00a3D Parallel Thinning Algorithms Based on Isthmuses0 a3D Parallel Thinning Algorithms Based on Isthmuses aBrno, Czech RepublicbSpringer VerlagcSep 2012 a325 - 3350 v75173 a

Thinning is a widely used technique to obtain skeleton-like shape features (i.e., centerlines and medial surfaces) from digital binary objects. Conventional thinning algorithms preserve endpoints to provide important geometric information relative to the object to be represented. An alternative strategy is also proposed that preserves isthmuses (i.e., generalization of curve/surface interior points). In this paper we present ten 3D parallel isthmus-based thinning algorithm variants that are derived from some sufficient conditions for topology preserving reductions. ` `

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images.

uhttp://www.inf.u-szeged.hu/ipcg/publications/Year/2011.complete.xml#Nemeth-etal201100686nas a2200193 4500008004100000245006000041210006000101260005000161300001400211100002700225700002000252700001600272700002500288700001400313700002200327700001700349700002100366856010500387 2010 eng d00aProjection selection algorithms for discrete tomography0 aProjection selection algorithms for discrete tomography aSydney, Australia bSpringer VerlagcDec 2010 a390 - 4011 aVarga, László Gábor1 aBalázs, Péter1 aNagy, Antal1 aBlanc-Talon, Jacques1 aBone, Don1 aPhilips, Wilfried1 aPopescu, Dan1 aScheunders, Paul uhttps://www.inf.u-szeged.hu/en/publication/projection-selection-algorithms-for-discrete-tomography-0