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題名 | High Quality Inverse Halftoning Using Variance Gain-, Texture- and Decision Tree-Based Learning Approach |
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作者 | 鍾國亮; 黃詠淮; 吳亢捷; | 書刊名 | Journal of Information Science and Engineering |
卷期 | 26:6 2010.11[民99.11] |
頁次 | 頁2213-2227 |
分類號 | 312.2 |
關鍵詞 | Decision tree; Discrete cosine transform; Inverse halftoning; Lookup tree-table; Machine learning; Texture; Vector quantization; |
語文 | 英文(English) |
英文摘要 | 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. |
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