Adaptive Sharpening with Overshoot Control A.Buemi, G.Santoro, A. Bruna, M.Guarnera ST Microelectronics, AST Catania Lab, Imaging Group Stradale Primosole, 50 - 95121 Catania, Italy {antonio.buemi, gaetano.santoro, arcangelo.bruna, mirko.guarnera}@st.com Abstract. This paper presents the Adaptive Sharpening with Overshoot Control (ASOC), an algorithm for digital image contrast enhancement. The ASOC exploits a properly defined band-pass filter in order to better discriminate the "uniform/not uniform" zones of the input (blurred) image. A more aggressive sharpening is then performed on the textured zones, whilst the homogeneous regions are preserved. Keywords: sharpening, gradients, band pass filters, overshoot. 1 Introduction The principal target of the sharpening in digital images [1] is to highlight fine details in an image or to enhance blurred images. Since not only edges or details but any discontinuity in the image could be enhanced by a trivial approach, one of the most important objectives in a sharpening algorithm design is providing sharpening without introducing noise amplification and the so-called ringing effects. Considering the typical horizontal profile of an edge (Fig.1), the target is making steeper such intensity profile. Several approaches have been presented in literature. The simplest algorithms (both conceptually and computationally) to the problem are the Unsharp Masking (UM) techniques [2],[3],[4]. Such methods are based on the idea of adding a high passed version Z of the original image I to the input image, yielding the enhanced image Y: Y(m,n)=I(m,n)+Z(m,n)• λ (1) where λ is an "enhance factor", used in order to modulate the strength of the filter. Different methods of UM for image enhancement are compared in the same condition in [2]. The results of such analysis show that the majority of the UM algorithms are very sensitive to the enhancement factor λ thus, for a good sharpening algorithm, this parameter must be adapted pixel by pixel with a recursive estimation, taking into account the statistics of neighboring pixel values. It increases the computational cost of the algorithm. An appropriate filter definition is a basic issue for the sharpening approach that uses sharpening masks in order to perform spatial filtering able to enhance the high contrast areas much more than the low contrast areas of the image. Using a filter properly defined, a good estimation of the local dynamics of the input image may be obtained performing an adaptive sharpening and avoiding noise