臺灣博碩士論文加值系統

本論文提出以線性估測法將半色調影像還原為灰階影像。所提出的方法步驟為：（1）輸入一張灰階訓練影像與其半色調影像，將半色調影像各像素分類，共分成18類；（2）以線性估測法（linear estimation）估測各類像素的最佳權重遮罩濾波器；（3）輸入測試半色調影像，將各像素分類；（4）各類像素運行該類的最佳權重遮罩濾波器，得到還原的逆半色調影像；（5） 結合WInHD方法，最後得到還原影像。分類的方法是將半色調影像使用高斯平滑濾波器處理得到一灰階影像，再對影像運行Canny邊緣（edge）法處理，得到二值的邊緣影像，考慮像素的鄰近25點邊緣情形分成18類。
實驗結果證明，所提出方法其重建影像的PSNR品質平均比WInHD及ELUT高約0.55dB，以Lena影像為例，所提出方法之逆半色調影像的PSNR為32.80dB。

This paper proposes the use of linear estimation to restore a halftone image into a grayscale image. The proposed method consists of the following steps: （1） Input a grayscale training image and its halftone image then classify the pixels of the halftone image into one of 18 categories. （2） Use the linear estimation method to estimate the best weight mask filter for each pixel. （3） Input the test halftone image and classify each pixel. （4） Process each pixel through the best weighting mask filter for their type to acquire the restored inverse halftone image. （5） Use the Wavelet-based Inverse Halftoning via Deconvolution （WInHD） method to acquire the final restored image. Classification is performed by applying the Gaussian smoothing filter to the halftone image to derive the grayscale image. Canny edge detection is then applied to the image to derive the binary edge image. Finally, the edges from the pixel’s 25 neighboring points are taken into account to classify it into one of 18 categories.
The experimental results showed that the PSNR quality of the image restored using the proposed method was on average 0.55dB higher than WInHD and ELUT. With the Lena image for example, the proposed method generated an inverse halftone image with a PSNR or 32.80dB.

封面內頁
簽名頁
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
圖目錄 ix
表目錄 xi

第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 2
第三節 文獻探討 2
第四節 例證 5
第五節 論文架構 6
第二章 相關研究 8
第一節 誤差擴散演算法（error-diffusion algorithm） 8
第二節 高斯濾波器（gaussian smoothing filter） 10
第三節 溫納濾波器（weiner filter） 11
第四節 小波濾波強化處理（wavelet-based inverse halftoning via deconvolution） 13
第五節 數位餘弦轉換強化處理（inverse halftoning by shape-adaptive DCT filtering） 17
第六節 查表法（LUT inverse halftoning） 20
第七節 改良查表法（ELUT inverse halftoning） 22
第三章 提出的方法 25
第一節 像素分類（pixel classification） 26
第二節 遮罩估測（weight mask estimation） 28
第三節 逆半色調影像（inverse halftoning image via weight mask） 31
第四節 線性估測+小波濾波強化處理（linear estimation + WInHD） 31
第四章 實驗及分析 32
第五章 結論與未來工作 39
第一節 結論 39
第二節 未來工作 40
參考文獻 41

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