An adaptive real-time descreening method based on SVM and improved SUSAN filter

Scanned halftone images are degraded for the presence of screen patterns. It's a challenge to automatically detect the halftone images and remove the noises on the fly. This paper proposes a novel adaptive real-time descreening method based on Support Vector Machine (SVM) and modified Smoothing over Univalue Segment Assimilating Nucleus (SUSAN) filter for imaging devices, including scanners and multifunction printers. The proposed algorithm contains two major steps: image classification and adaptive descreening. The image classification uses SVM methods to accurately classify the scanned images into three categories: continuous tone, amplitude modulation (AM) halftone or frequency modulation (FM) halftone. The halftone images are needed to descreen. The proposed descreening method is based on modified SUSAN filter. It considers screen cell size to choose the optimal filter parameters which can preserve more high frequency image detail. The experiment results show that the algorithm is effective and fully automatism, and maintains higher image quality.