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-tomography01458nas a2200121 4500008004100000020002200041245011700063210006900180260004600249300001400295520093900309856008801248 2011 eng d a978-3-642-23686-000aA Multi-Layer 'Gas of Circles' Markov Random Field Model for the Extraction of Overlapping Near-Circular Objects0 aMultiLayer Gas of Circles Markov Random Field Model for the Extr aGhent, BelgiumbSpringer-VerlagcAug 2011 a171 - 1823 a

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