Iterative inverse halftoning based on texture-enhancing deconvolution and error-compensating feedback

Chang-Hwan Son and Hyunseung Choo

Signal Processing, vol. 93, no. 5, pp. 1126-1140, May 2013 (SCI, IF: 2.238)


The quality of the reconstructed image with 255 discrete levels from its halftoned version of homogenously distributed dot patterns depends on how well the fine textures can be represented and how the noisy dot patterns can be simultaneously removed on the flat regions. To satisfy these criteria, an iterative inverse halftoning method based on the texture-enhancing deconvolution with error-compensating feedback is presented. In this study, the input halftoned image is initially low-pass filtered with the Gaussian filtering to generate a blurred image on which the textures or details are sharply restored and the noisy halftoned dots on the flat regions are forced to be suppressed through the joint of the texture-enhancing deconvolution with spatially varying image priors and the structure-preserving image denoising. Moreover, the initially blurred image is iteratively updated with the addition of the created error image defined as the difference-image between the low-pass-filtered input halftoned image and the low-pass filtered halftoned image of the reconstructed continuous image, thereby compensating the missing textures. This error compensation is conducted until the stop criterion is satisfied. The experiment results showed that the proposed method not only reproduced the fine textures or details but also suppressed the noisy dots on the flat regions, more than the conventional state-of-the-art methods.



Halftoning, Deconvolution, Posterior, Prior, Texture map


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