Training-based algorithm for Moiré suppression in scanned halftone images

Hasib Siddiqui; Charles A. Bouman

doi:10.1117/12.724451

23 February 2007 Training-based algorithm for Moire suppression in scanned halftone images

Hasib Siddiqui, Charles A. Bouman

Proceedings Volume 6498, Computational Imaging V; 64981D (2007) https://doi.org/10.1117/12.724451
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

Conventional electrophotographic printers tend to produce Moire artifacts when used for printing images scanned from printed material such as books and magazines. We propose a novel descreening algorithm that removes a wide range of Moire-causing screen frequencies in a scanned document while preserving image sharpness and edge detail. We develop two non-linear noise removal algorithms, resolution synthesis denoising (RSD) and modified SUSAN filtering, and use the combination of the two to achieve a robust descreening performance. The RSD predictor is based on a stochastic image model whose parameters are optimized in an offline training algorithm using pairs of spatially registered original and scanned images obtained from real scanners and printers. The RSD algorithm works by classifying the local window around the current pixel in the scanned image and then applying linear filters optimized for the selected classes. The modified SUSAN filter uses the output of RSD for performing an edge-preserving smoothing on the raw scanned data and produces the final output of the descreening algorithm. The performance of the descreening algorithm was evaluated on a variety of test documents obtained from different printing sources. The experimental results demonstrate that the algorithm suppresses halftone noise without deteriorating text and image quality.

Citation Download Citation

Hasib Siddiqui and Charles A. Bouman "Training-based algorithm for Moire suppression in scanned halftone images", Proc. SPIE 6498, Computational Imaging V, 64981D (23 February 2007); https://doi.org/10.1117/12.724451

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