JISE

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Journal of Information Science and Engineering, Vol. 26 No. 6, pp. 2213-2227

High Quality Inverse Halftoning Using Variance Gain-, Texture- and Decision Tree-Based Learning Approach

KUO-LIANG CHUNG, YONG-HUAI HUANG⁺ AND KANG-CHIEH WU
Department of Computer Science and Information Engineering
National Taiwan University of Science and Technology
Taipei, 106 Taiwan
⁺Institute of Computer and Communication Engineering
Jinwen University of Science and Technology
Taipei, 231 Taiwan

Inverse halftoning (IH) is used to reconstruct the gray image from an input halftone image. This paper presents a machine learning-based IH algorithm to reconstruct the high quality gray images. We first propose a novel variance gain-based tree construction approach to build up an approximate decision tree (DT). Based on the constructed DT, a texture- based training process is presented to construct a lookup tree-table which will be used in the reconstructing process. In our implementation, thirty training images are used to build up the lookup tree-table and five popular testing images are used to justify the quality performance of our proposed IH algorithm. Experimental results demonstrate that although our IH algorithm needs longest execution-time, it has the highest image quality when compared to the published three IH algorithms.

Keywords: decision tree, discrete cosine transform, inverse halftoning, lookup tree-table, machine learning, texture, vector quantization

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