Hybrid LMS-MMSE inverse halftoning technique

Pao Chi Chang, Che Sheng Yu, Tien Hsu Lee

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

The objective of this work is to reconstruct high quality gray-level images from bilevel halftone images. We develop optimal inverse halftoning methods for several commonly used halftone techniques, which include dispersed-dot ordered dither, clustered-dot ordered dither, and error diffusion. At first, the !east-mean-square (LMS) adaptive filtering algorithm is applied in the training of inverse halftone filters. The resultant optimal mask shapes are significantly different for various halftone techniques, and these mask shapes are also quite different from the square shape that was frequently used in the literature. In the next step, we further reduce the computational complexity by using lookup tables designed by the minimum mean square error (MMSE) method. The optimal masks obtained from the LMS method are used as the default filter masks. Finally, we propose the hybrid LMS-MMSE inverse halftone algorithm. It normally uses the MMSE table lookup method for its fast speed. When an empty cell is referred, the LMS method is used to reconstruct the gray-level value. Consequently, the hybrid method has the advantages of both excellent reconstructed quality and fast speed. In the experiments, the error diffusion yields the best reconstruction quality among all three halftone techniques.

Original languageEnglish
Pages (from-to)95-103
Number of pages9
JournalIEEE Transactions on Image Processing
Volume10
Issue number1
DOIs
StatePublished - Jan 2001

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