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系統識別號 U0002-1706201000553600
中文論文名稱 數位浮水印與影像修復之研究
英文論文名稱 The Research on Digital Watermarking and Image Recovery
校院名稱 淡江大學
系所名稱(中) 電機工程學系博士班
系所名稱(英) Department of Electrical Engineering
學年度 98
學期 2
出版年 99
研究生中文姓名 洪國銘
研究生英文姓名 Kuo-Ming Hung
學號 894350031
學位類別 博士
語文別 英文
口試日期 2010-05-26
論文頁數 92頁
口試委員 指導教授-謝景棠
委員-陳稔
委員-楊維楨
委員-呂俊賢
委員-林慧珍
中文關鍵字 浮水印  整數餘弦轉換  Zernike 動量  對稱矩陣  影像修補  半色調影像  逆半色調  影像修復 
英文關鍵字 Watermark  IntDCT  Zernike Moment  Symmetric Matrix  Inpainting  Halftone Image  Inverse Halftoning  Image Recovery. 
學科別分類
中文摘要 近年來由於通訊科技的方便與快速,使得網際網路成為極佳的數位媒體傳播系統,也因此使得數位媒體非常容易被盜用或竄改,數位數媒體的著作權的保護與與保存成為非常重要的研究課題。
數位浮水印的發展可用來保護數位媒體的著作權與完整性,其技術可分成強健型浮水印與易碎型浮水印。強健型浮水印可用來作為數位媒體智慧財產權的保護;易碎型浮水印則可用來作為媒體的認證或被竄改的偵測。在數位浮水印發展的同時,影像修補也是一項非常熱門之研究主題。影像修補的技術,其目的是在修補被破壞影像的區域。影像修補可以延伸到不同的領域,例如:影像編輯、文化遺產的修復、資料的壓縮與網路資料的傳輸等。
本篇論文的第一個主軸是介紹一個基於整數離散餘弦轉換的強健型浮水印系統。我們利用4X4的整數離散餘弦轉換,去改善8X8離散餘弦轉換所產生的區塊效應,由於整數離散餘弦轉換的計算只包含了加法與平移運算,且沒有傳統離散餘弦轉換的結尾誤差,因此降低了複雜的計算,並使硬體的實現更加容易。
第二個主軸,提出一個基於實對稱矩陣,可以對抗幾何攻擊的半易脆浮水印系統,利用Multi-rings Zernike轉換去偵測影像的旋轉角度,並以實對稱矩陣的主特徵向量所產生的簽章位元來偵測影像受攻擊的區域。
本篇論文的第三個主軸,整合了數位浮水印、半色調技術與逆半色調轉換的方法,提出一新的影像修補技術,此技術利用LSB浮水印的技術,嵌入原始影像的半色調影像至原始影像中,用以保護該影像。當影像受破壞時,我們啟動修補程序,以LSB的方法,萃取半色調影像的資訊,並利用LUT逆半色調轉換,得到一參考影像。最後利用此參考影像來完成影像修補的工作。實驗證明,我們的方法在影像修補的效果是非常顯著的。
本篇論文最後一個主軸提出一個自動影像認證與修復的方法,以實對稱矩陣的主特徵向量所產生的簽章位元來偵測影像受攻擊的區域後,啟動半色調影像修補機制。我們成功整合我們所提出的易脆浮水印與影像修補技術,建立一個自動的影像恢復系統。
英文摘要 In recent years, Internet is an excellent distribution system for digital media because it is cheap and fast. It is known that digital media can be easily replicated and modified. Thus, digital watermarking has been developed to protect the copyright and integrity of multimedia content. A watermarking scheme can be classified as either robust watermarking or fragile watermarking. Robust watermarking is designed for the copyright and ownership verification. Fragile watermarking is used for image authentication and tamper detection, which can be used to identify any alteration of the images.
In parallel to watermarking there was also another field of study, image inpainting. Image inpainting technique aims to fill in the damaged region of an image with new information so that it is hard to find that the image has once been damaged. Image inpainting is extensively used in various fields such as photo editing, cultural relic restoration, data compression and network data transmission.
The first issue of this dissertation is to introduce a robust watermarking system based on Integer Discrete Cosine Transform. The 4x4 IntDCT transform can reduce blocking artifacts resulted from 8x8 DCT transform. The integer transform is designed such that the transformation involves only additions and shift operations, and no mismatch exists between the forward and inverse transforms. This reduces the computational complexity and it is much easier for hardware implementation.
The second issue of this dissertation is to introduce a new invariant semi-fragile digital watermarking technique based on eigenvalues and eigenvectors of a real symmetric matrix generated by the four pixel-pair. And the multi-rings Zernike transform (MRZT) is proposed to achieve geometric invariance. A signature bit for detecting malicious tampering of an image is generated using the dominant eigenvector. The MRZT method is against the geometric distortions even when the image is under malicious attacks.
In the third issue of this dissertation, we combine the algorithms of watermarking, halftoning and inverse halftoning to propose a novel image inpainting technique. This technique use LSB method to embed halftone image into original image for protecting the image. In image repair process, we use LSB method to extract the halftone information, and the reference image is achieved from LUT inverse halftoning. Finally we use the reference image to finish the image inpainting work. Experiment shows the performance of our method is very excellent in image inpainting.
In the last issue of this dissertation, a novel scheme of Automatic image authentication and recovery has been proposed, in which the signature bits for detecting malicious tampering of an image are generated using the dominant eigenvector. To achieve high-quality image recovery, we also insert the binary halftone image converted from the original image into the watermarked image to obtain the protected watermarked image. If the image has been tampered, the altered region of the image can be detected and recovered in a fully automated fashion.
論文目次 Contents I
List of Figures IV
List of Tables VII
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related works 1
1.2.1 Watermarking 1
1.2.2 Image Inpainting and Recovery 4
1.3 Main Contributions of the Proposal 6
1.4 The Organization of this Dissertation 7
CHAPTER 2 Proposed Robust Watermarking Technique Using IntDCT Based AC Prediction 8
2.1 Introduction 9
2.2 IntDCT and AC Prediction 10
2.2.1 Integer Discrete Cosine Transform 10
2.2.2 Data Embedding Technique Based on the Prediction of AC Coefficients 12
2.2.2.1 AC Prediction 12
2.2.2.2 Yulin Wang and Alan Pearmain’s Watermark Embedding Technique 14
2.3 The Proposed Robust Watermarking Scheme Using IntDCT and AC Prediction 14
2.3.1 Embedding the Watermark 14
2.3.2 Extracting the Watermark 17
2.4. Experimental Results 20
2.4.1 Imperceptibility and Watermark Capacity 22
2.4.2 Image Processing and Geometrical Attacking 23
2.4.3 JPEG Compression Attacks 24
2.5 Summary 25
Chapter 3 Proposed Geometric Invariant Semi-fragile Watermarking Using Real Symmetric Matrix 26
3.1. Introduction 26
3.2 Multi-rings Zernike Transform 29
3.2.1 Zernike Transform 29
3.2.2 Candidate Selection Process 32
3.3. Eigenvectors and Eigenvalues of Real Symmetric Matrix 33
3.4 The Proposed Semi-fragile Watermarking Scheme 35
3.4.1 Embedding Algorithm 35
3.4.2 Extraction Algorithm 37
3.5. Experimental Results 39
3.5.1 Rotation and Scaling Invariance 39
3.5.2 Image Quality 40
3.5.3 Dimension Decision of the Real Symmetric Matrix 42
3.5.4 Comparison 43
3.5.5 Image Authentication 45
3.6 Summary 49
Chapter 4 Proposed Image Inpainting Scheme Based on Watermarking and Halftoning 50
4.1 Introduction 50
4.2 Related Works 52
4.2.1 Least Significant Bit Substitution 52
4.2.2 Error Diffusion Algorithm for Halftoning 53
4.2.3 Look-Up-Table Inverse Halftoning 55
4.3 The Proposed Image Inpainting Scheme 58
4.4 Experimental Results 60
4.5 Summary 69
Chapter 5 Proposed Automatic Image Authentication and Recovery Using Multiple Watermarks 70
5.1 Introduction 71
5.2 Fragile Watermark for Image Authentication 73
5.3.1 Embedding Algorithm 73
5.3.2 Extracting Algorithm 74
5.4 The Proposed Automatic Image Authentication and Recovery Scheme 75
5.4.1 Fragile Watermark Embedded process 75
5.4.2 Damaged Image Recovery Process 76
5.5 Experimental Results 78
5.5.1 The Results of Watermark Embedding phase 78
5.5.2 The Results of Image Inpainting phase 78
5.6 Summary 82
CHAPTER 6 Conclusions and Future Works 83
6.1 Conclusions 83
6.2 Future Works 84
References 85

List of Figures
Fig. 2.1 Frequency Distribution in a DCT block (left), Structural decomposition in a DCT block(right) 13
Fig. 2.2 Neighbourhood of DCT blocks used for AC prediction 13
Fig. 2.3 the average PSNR of the ten natural images 16
Fig. 2.4 the procedure of embedding the watermark 16
Fig. 2.5 the procedure of extracting the watermark 19
Fig. 2.6 Nine 512x512 host images 21
Fig. 2.7 watermark (64x64) 21
Fig. 2.8 The correct decoding Rates of extracting watermarks 24
Fig. 2.9 The correct decoding rates under attacks of different JPEG quality compression 24
Fig. 3.1 Block diagram of proposed digital watermark embedding system. 37
Fig. 3.2 Block diagram of proposed digital watermark extraction system. 37
Fig. 3.3 (a) Original Lena image. (b) Watermarked Lena image. 42
Fig. 3.4 (a) Modified Pepper image. (b) Modified areas detected by proposed method. (c) Modified areas detected by Lin [33] method. 47
Fig. 3.5 (a) Modified Lena image. (b) Modified areas are detected by proposed method. (c) Modified areas are detected by Lin [33] method. 47
Fig. 3.6 (a) Modified Baboon image. (b) Modified areas are detected by proposed method. (c) Modified areas are detected by Lin [33] method. 47
Fig. 3.7 (a) Rotated Baboon image. (b) Simulated result of (a). Modified areas are detected by proposed method. 48
Fig. 3.8 (a) Watermarked natural image. (b) Modified natural image. (c) Modified areas are detected by proposed method. (d) Modified areas are detected by Lin [33] method. 48
Fig. 4.1 The error diffusion algorithm. 53
Fig. 4.2 Error filters of different error diffusion methods. 55
Fig. 4.3 LUT template 57
Fig. 4.4 the process of watermark embedded phase. 58
Fig. 4.5 the process of inpainting phase 59
Fig. 4.6 watermark embedded phase (a)original image Lena(512x512) (b) the halftone image converted from Fig. 4.6(a)with error diffusion method. (c) the permuted binary random image converted form Fig. 4.6(b). (d) the protected image which is embedded Fig. 4.6(c) into Fig. 4.6(a),PSNR=54.14. 62
Fig. 4.7 An example of inpainting phase (a)The damaged Lena image attacked in both eyes from the protected image(Fig. 4.6(d)) (b) The random binary damaged image from Fig. 4.7(a) with LSB method. (c) The damaged halftone image converted from Fig. 4.7(b). (d) The reference image converted from Fig. 4.7(c) with LUT inverse halftoning algorithm. (e) The enhanced reference image form Fig. 4.7(d) with median filtering. (F) The restored image. 63
Fig. 4.8 (a) Damaged rate is 1.76% for Lena; (b) Restored image, PSNR=47.80 64
Fig. 4.9 (a) Damaged rate is 1.35% for Lena; (b) Restored image, PSNR=48.82 64
Fig. 4.10 (a) Damaged rate is 2.09% for Lena; (b) Restored image, PSNR=47.25 65
Fig. 4.11 (a) Damaged rate is 5.46% for Lena; (b) Restored image, PSNR=41.12 65
Fig. 4.12(a) Damaged rate is 10.74% for Lena; (b) Restored image, PSNR=34.17 66
Fig. 4.13 (a) Damaged rate is 41.07% for Lena; (b) Restored image, PSNR=16.69 66
Fig. 4.14 (a) Damaged rate is 64.85% for Lena; (b) Restored image, PSNR=10.69 67
Fig. 4.15 Experimental results from utilizing different methods: (a)The damaged image; (b) The PDE method[47].; (c) The PII method[52]. ;(d) Our proposed method. 68
Fig. 4.16 Zoom-in inpainting result of Fig. 4.15 (a), 4.15 (c) and 4.15(d): (a) The damaged region; (b) The PII method [52]; (c) The proposed method. 68
Fig. 5.1 Block diagram of proposed digital fragile watermark embedding system. 74
Fig. 5.2 Block diagram of proposed fragile watermark extracting system. 75
Fig. 5.3 watermark embedded phase. 76
Fig. 5.4 Inpainting phase. 77
Fig. 5.5 (a) The original image for Lena; (b)The watermarked image after embedding signature bits into Fig.5.5 (a), PSNR=46.2043; (c) The protected image after embedding the halftone image into Fig. 5.5(b), PSNR=44.82. 79
Fig. 5.6 (a)The original image for Pepper; (b)The watermarked image after embedding signature bits into Fig.5.6 (a), PSNR=46.2043; (c) The protected image after embedding the halftone image into Fig. 5.6(b), PSNR=44.97. 79
Fig. 5.7 (a) The damaged image for Lena; (b) Auto localize the tampered areas from Fig 5.7(a); (c) The result of inpainting from Fig.5.7 (a), PSNR=43.67. 80
Fig. 5.8 (a)The original image for Pepper; (b) Tampered image from Fig. 5.8(a); (c) Auto localize the tampered areas from Fig 5.8(b); (d) The result of inpainting, PSNR=30.74. 80
Fig. 5.9 (a)The original image for Pepper; (b)The watermarked image after embedding signature bits into Fig.5.9 (a),PSNR=46.17; (c)The protected image after embedding the halftone image into Fig. 5.9(b),PSNR=44.80. 81
Fig. 5.10 (a) The altered image for Door; (b) Auto localize the tampered areas from Fig 5.10(a); (c)The recovered image obtained by our scheme, PSNR=42.95; (d) The recovered image obtained by Wang’s scheme[13], PSNR=38.54. 81


List of Tables
Table 2.1 Imperceptibility using PSNR 22
Table 2.2 Watermark capacity(bit) Image Size 512*512 23
Table 2.3 Quality of watermarked image, PSNR (db). 23
Table 3.1 Signature bits and direction of dominant eigenvector. (The dimension of the real symmetric matrix is 2×2) 34
Table 3.2 The MSE of the estimating angle under malicious attacks 40
Table 3.3 Estimating angle by Zernike transform and proposed multi-ring Zernike transform 40
Table 3.4 PSNR value with different dimension of the real symmetric matrix 43
Table 3.5 Bit error rate of images under different quality of JPEG compression 44
Table 3.6 Bit error rate of proposed extracting method under different quality of JPEG compression 44
Table 3.7 PSNR value of watermarked image 44
Table 3.8 Bit error rate for various embedding algorithms 45

參考文獻 [1] Podilchuk, C. I. and Zeng, W.. "Image-Adaptive Watermarking Using Visual Models," IEEE Journal on Selected Areas in Comm., vol 16, no. 4, pp. 525-539, May 1998.
[2] Fridrich, J., Goljan, M. and Baldoza, “New fragile authentication watermark for images,” International Conference on Image Processing (ICIP 2000), Sep 10-13 2000 Vancouver, BC, IEEE Computer Society pp. 446- 449.
[3] J. Cao, and F. Li, “DCT Information Recovery of Erroneous Image for MPEG-2,” Int. Conf. Neural Networks and Signal Processing, vol. 2, pp. 1160_1162, 2003.
[4] L.W. Kang and J.J. Leou, “An Error Resilient Coding Scheme for JPEG Image Transmission Based on Data Embedding and Side-match Vector Quantization,” Journal of Visual Communication and Image Representation, Vol. 17, No. 4, pp. 876_891, 2006.
[5] T. Kim, “Side Match and Overlap Match Vector Quantizers for Images,” IEEE Trans. Image Processing, Vol. 1, pp.170_185, April 1992.
[6] P. Yin, B. Liu, and H. H. Yu, “Error Concealment Using Data Hiding,” Acoustics, Speech, and Signal Processing, vol. 3, pp. 1453_1456, 2001.
[7] C. S. Lu, “Wireless Multimedia Error Resilience via A data Hiding Technique,” IEEE Workshop on Multimedia Signal Processing, pp. 316_319, 2002.
[8] Piva A, Bartolini F, Caldelli R. “Self-recovery authentication of images in the DWT domain,” Int J Image Graphics 2005;5(1):149–65.
[9] Liu H, Steinebach M. “Semi-fragile watermarking for image authentication with high tampering localization capability,” Proceeding of the second international conference on automated production of cross media content for multi-channel distribution, IEEE; 2006. p. 143–52.
[10] Chamlawi R, Khan A, Idris A. “Wavelet based image authentication and recovery. ” J Comput Sci Technol 2007;22(6):795–804.
[11] J. Fridrich, M. Goljan, “Images with self-correcting capabilities, ” International Conference on Image Processing, vol. 3, 1999, pp. 792–796.
[12] P.L. Lin, P.W. Huang, A.W. Peng, “A fragile watermarking for image authentication with localization and recovery,” International Symposium on Multimedia Software Engineering, 2004, pp. 146–153.
[13] Shuenn-Shyang Wang, Sung-Lin Tasi, “Automatic image authentication and recovery using fractal code embedding and image inpainting,” Pattern Recognition (41), No. 2, February 2008, pp. 701-712
[14] R G. van Scbyndel, A. 2. Tirkel, and C. F. Shamoon,“Secure spread specbum watermarking for multimedia,”IEEE Trans. Image Processing, vol. 6, pp. 1673-1687, Dec.1997.
[15] C. Langelaar, J. C. A. Vea der Lubbe, and R L. Lagendijk, “Robust labeling method for copy protection of images,” Proceedings of SPWIS&T Electronic lmaging, San Jose, CA, vol. 3022, pp. 298-309, Feb. 1997.
[16] M. Bami, F. Bartolini, V. Cappelhi, and A. F’iva, “A DCT domian system for robust image watermarking,” Signal Processing (Special Issue on Watermarking), vol. 66, no. 3, pp. 357-372, May 1998.
[17] C.-W. Tang and EL-M. Hang “A feature-based robust digital image watermarking scheme,” IEFE Transactions on Signal Processing, vol. 51, no. 4, pp. 950-959, Apr.2003.
[18] M. Bami, F. Bartolini, V. Cappellini, and A. Piva, “A DCT-domian system for robust image watermarking,” Signal Processing (Special Issue on Watermarking), vol. 66, no. 3, pp. 357-372, May 1998.
[19] Tay,R.; Havlicek, J.P. “Image Watermarking Using Wavelets”, Proc. IEEE. Vol. 3, pp.III-258 - III-261, 2002.
[20] W. Bender, D. Gruhl, N. Morimoto, and A. Lu, “Techniques for Data Hiding,” IBM Systems Journal, vol. 35, no. 3-4, 1996, IBM,USA, pp. 313-336.
[21] I.J. Cox, J. Killian, T. Leghton, and T. Shamoon, “Secure Spread Spectrum for Multimedia,” IEEE Trans. on Image Processing, vol. 6, no. 12, 1997, pp. 1673-1687.
[22] Gonzales, C.A., Allman, L., Mccarthy, T., Wendt, P., “DCT coding for motion video storage using adaptive arithmetic coding”, Signal Processing: Image Comm. 2 (2), 1990.
[23] Wang Yulin, Pearmain, Alan, “Blind image data hiding based on self reference”, Pattern Recognition Letters Volume: 25, Issue: 15, November, pp. 1681-1689, 2004
[24] “H.264/MPEG-4 Part 10: Transform & quantization,” ITU-T Rec. H264/ISO/IEC 11496-10, Final Committee Draft, Document JTV-F100, Dec. 2002. (Latest version modified on 19.03.2003) [Online] Available:
http://www.vcodex.fsnet.co.uk/h264.html
[25] J. Zhang and Anthony T. S. Ho,” An efficient digital image-in-image watermarking algorithm using the integer discrete cosine transform (IntDCT)”, ICICS-PCM 2003,15-18 December 2003,Singapore.
[26] Kim, H. S. and Lee, H. K., “Invariant Image Watermark Using Zernike Moments,” IEEE Transactions on Circuits and Systems for Video Technology, pp. 766-775 (2003).
[27] Xin, Y., Liao, S. and Pawlak, M., “A Multibit Geometrically Robust Image
Watermark Based on Zernike Moments,” in Proceedings of Pattern Recognition,
pp. 861-864 (2004).
[28] Liu, H., Lin, J. and Huang, J., “Image Authentication Using Content Based
Watermark,” in Proceedings of IEEE Circuits and Systems, pp. 4014-4017
(2005).
[29] Farzam, M. and Shirani, S., “A Robust Multimedia Watermarking Technique
Using Zernike Transform,” in Proceedings of IEEE Multimedia Signal
Processing, pp. 529-534 (2004).
[30] Chen, J., Yao, H., Gao, W. and Liu, S., “A Robust Watermarking Method Based on Wavelet and Zernike Transform,” in Proceedings of Circuits and Systems, pp.173-176 (2004).
[31] Kundur, D. and Hatzinakos, D., “Digital Watermarking for Telltale Tamper Proofing and Authentication,” in Proceedings of IEEE Digital Object Identifier,Vol. 87, pp. 1167-1180 (1999).
[32] Lin, E. T. and Delp, E. J., “A Review of Fragile Image Watermarks,” in Proceedings of the Multimedia and Security Workshop, pp. 25-29 (1999).
[33] Lin, C. Y. and Chang, S. F., “Semi-Fragile Watermarking for Authenticating JPEG Visual Content,” in Proceeding of the SPIE, Security and Watermarking of
Multimedia Contents, pp. 140-151 (2000).
[34] Maeno, K., Sun, Q., Chang, S. F. and Suto, M., “New Semi-Fragile Image
Authentication Watermarking Techniques Using Random Bias and Non-Uniform Quantization,” in Proceedong of the SPIE, Security and Watermarking of Multimedia Contents, pp. 659-670 (2002).
[35] Nakai, Y., “Multivalued Semi-Fragile Watermarking,” in Proceeding of the SPIE,Security and Watermarking of Multimedia Contents, pp. 671-678 (2002).
[36] Lu, C. S. and Liao, L. Y. M., “Multipurpose Watermarking for Image Authentication and Protection,” IEEE Transactions on Image Processing, Vol.10, pp. 1579-1592 (2001).
[37] Sun, Q., Chang, S. F., Maeno, K. and Suto, M., “A New Semi-Fragile Image Authentication Framework Combining ECC and PKI Infrastructures,” in Proceedings of the IEEE Circuits and Systems, pp. 440-443 (2002).
[38] Fridrich, J., “A Hybrid Watermark for Tamper Detection in Digital Images,” in Proceedings of the Signal Processing and Its Applications, pp. 301-304 (1999).
[39] Wong, P.W., “A Public KeyWatermark for Image Verification and Authentication,” in Proceedings of the ICIP, Vol. 2, pp. 427-431 (1998).
[40] Coppersmith, D., Mintzer, F., Tresser, C., Wu, C. W. and Yeung, M. M., “Fragile Imperceptible Digital Watermark with Privacy Control,” in Proceedings SPIE,
Security and Watermarking of Multimedia Contents, pp. 79-84 (1999).
[41] Dittmann, J., Steinmetz, A. and Steinmetz, R., “Content-Based Digital Signature for Motion Pictures Authentication and Content-fragile Watermarking,” in
Proceedings of IEEE Multimedia Computing and Systems, Vol. 2, pp. 209-213(1999).
[42] Wolfgang, R. B. and Delp, E. J., “Fragile Watermarking Using the VW2DWatermark,” in Proceedings of SPIE, Security and Watermarking of Multimedia Contents, pp. 204-213 (1999).
[43] Yin, P. and Yu, H. H., “A Semi-Fragile Watermarking System for MPEG Video Authentication,” in Proceedings of IEEE ICASSP, pp. 3461-3464 (2002).
[44] Chen, T., Wang, J. and Zhou, Y., “Combined Digital Signature and Digital Watermark Scheme for Image Authentication,” in Proceedings of Info-tech and
Infonet, pp. 78-82 (2001).
[45] Yu, G. J., Lu, C. S., Liao, H. Y. M. and Sheu, J. P., “Mean Quantization Blind Watermarking for Image Authentication,” in Proceedings of Image Processing,pp. 706-709 (2002).
[46] T. K. Shih and R. C. Chang, “Digital inpainting—Survey and multilayer image
inpainting algorithms,” in Proc. IEEE Int. Conf. Inform. Tech. Appl., 2005, vol. 1,pp. 15–24.
[47] M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester,”Image inpainting”,SIGGRAPH, pp.417–424, July 2000.
[48] A. Telea, “An image inpainting technique based on the fast marching method”,Journal of Graphics Tools, vol. 9, no. 1, ACM Press, pp.25-36, 2004.
[49] T. Zhou, F. Tang, J. Wang, Z. Wang and Q. Peng, “Digital Image Inpainting with Radial Basis Functions,” J. Image Graphics, September 2004, pp. 1190-1196.
[50] A. W. C. Liew, N. F. Law and D. T. Nguyen, “Multiple Resolution Image Restoration”, IEE Proceedings - Vision, Image and Signal Processing, Vol.144,pp.199-206, 1997.
[51] C.S. Burrus, R. A. Gopinath, and H.Guo, Introduction to Wavelets and Wavelet Transform, Prentice-Hall, 1998.
[52] Y. L. Chen, C. T. Hsieh, and C. H. Hsu, “Progressive Image Inpainting Based on Wavelet Transform,” IEICE, Trans. Fund., Vol. E88-A, No. 10, Oct., pp.2826-2834 ,2005.
[53] E. Le Pennec and S. Mallat, “Sparse geometrical image representation with bandelets,” IEEETrans. Image Process., 14 (2005), pp. 423–438.
[54] E. Le Pennec and S. Mallat “Bandelet image approximation and compression,”SIAM J. Multiscale Simul., vol. 4, no. 3, pp. 992–1039, 2005.
[55] Kuo-Ming HUNG, Yen-Liang CHEN and Ching-Tang HSIEH“ A Novel Bandelet-Based Image Inpainting” IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, Volume E92-A, No.10,October 2009, pp.2471-2478.(SCI)
[56] R. Ulichney, “Digital Halftoning,” The MIT Press, 1987.
[57] R.W. Floyd, and L. Steinberg, “Adaptive algorithm for spatial grey scale,” SID Int. Sym. Digest of Tech. Papers, pp. 36-37, 1975.
[58] J.F. Javis, C.N. Judice, and W.H. Ninke, “A survey of techniques for the display of continuous-tone pictures on bilevel displays,” Computer Graphics and Image
Processing, vol.5, pp.13-40, 1976.
[59] P. Stucki, “MECCA –a multiple error correcting computation algorithm for bilevel image hardcopy reproduction,” Research Report RZ1060, IBM Research Laboratory, 1981.
[60] S. Hein and A. Zakhor, “Halftone to Continuous-Tone Conversion of Error-Diffusion Coded Image,” IEEE Trans. Image Processing, vol. 4, pp.208–216, Feb. 1995.
[61] M. Y. Shen and C.-C. J. Kuo, “A Robust Nonlinear Filtering Approach to Inverse Halftoning,” J. Vis. Commun. Image Represen., vol. 12, pp. 84–95, Mar. 2001.
[62] R. Neelamani, R. Nowak, and R. Baraniuk, “WinHD: Wavelet-Based Inverse Halftoning via Deconvolution,” IEEE Trans. on Image Process., October 2002.Submitted.
[63] Z. Xiong, M. T. Orchard, and K. Ramchandran, “Inverse Halftoning Using Wavelets,” IEEE Trans. Image Process., vol. 8, no. 10, pp. 1479–1482, Oct.1999.
[64] Dabov, K., A. Foi, V. Katkovnik, and K. Egiazarian, “Inverse halftoning by pointwise shape-adaptive DCT regularized deconvolution,” Proc. 2006 Int. TICSP Workshop Spectral Meth. Multirate Signal Process., SMMSP 2006, Florence, Sep. 2006.
[65] M. Mese and P. P. Vaidyanathan, “Look Up Table (LUT) Method for Inverse Halftoning,” IEEE Trans. Image Process., vol. 10, no. 10, pp. 1566-1578, Oct. 2001.
[66] K. –L. Chung and S. –T. Wu, “Inverse Halftoning Algorithm Using Edge-Based Lookup Table Approach,” IEEE Trans. on Image Process., vol. 14, no. 10, pp. 1583-1589, 2005.
[67] D.W. Redmill, and N.G. Kingsbury, “The EREC: An Error-Resilient Technique for Coding Variable-length Blocks of Data,” IEEE Trans. Image Processing, Vol. 5, No. 4, pp. 565_574, 1996.
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