Visible to the public Scanned Image Descreening With Image Redundancy and Adaptive Filtering

TitleScanned Image Descreening With Image Redundancy and Adaptive Filtering
Publication TypeJournal Article
Year of Publication2014
AuthorsBin Sun, Shutao Li, Jun Sun
JournalImage Processing, IEEE Transactions on
Date PublishedAug
Keywordsadaptive filtering, adaptive filters, anisotropic Gaussian kernel, continuous tone image print, denoising algorithm, descreening, edge-preserving filter, electrophotographic printers, electrophotography, Gaussian processes, halftone pattern, high quality contone image recovery, image denoising, Image edge detection, image redundancy, inverse halftoning, Kernel, local gradient features, Noise, noise reduction, printers, Printing, printing distortion, printing noise reduction, Redundancy, scanned halftone image, Scanned image, scanned image descreening, steerable filter

Currently, most electrophotographic printers use halftoning technique to print continuous tone images, so scanned images obtained from such hard copies are usually corrupted by screen like artifacts. In this paper, a new model of scanned halftone image is proposed to consider both printing distortions and halftone patterns. Based on this model, an adaptive filtering based descreening method is proposed to recover high quality contone images from the scanned images. Image redundancy based denoising algorithm is first adopted to reduce printing noise and attenuate distortions. Then, screen frequency of the scanned image and local gradient features are used for adaptive filtering. Basic contone estimate is obtained by filtering the denoised scanned image with an anisotropic Gaussian kernel, whose parameters are automatically adjusted with the screen frequency and local gradient information. Finally, an edge-preserving filter is used to further enhance the sharpness of edges to recover a high quality contone image. Experiments on real scanned images demonstrate that the proposed method can recover high quality contone images from the scanned images. Compared with the state-of-the-art methods, the proposed method produces very sharp edges and much cleaner smooth regions.

Citation Key6841640