臺灣博碩士論文加值系統

影像放大(Image Up-scaling)為數位影像處理領域中的一個重要技術，當今已有許多各樣不同的影像放大演算法被提出。然而，這些演算法為了追求較高的影像放大品質，往往因運算量較高而限制了其即時處理的能力；而運算量較低的演算法，其影像放大的品質又經常不盡理想。Kim等人所提出的Winscale影像放大演算法，使用能直接在二維的影像座標上改變影像解析度的區域像素模型(Area Pixel Model)，可以用簡單的運算方法達到不錯的影像縮放效果，然而此種創新之演算法其得到的影像品質仍然不如現今最常被使用的Bicubic演算法。
本文參考並改良Winscale的方法，以一種新的概念，縱向考慮原始影像和放大影像間的關係，提出了一種像素標記(Pixel-tagging)的機制，並且透過階層式放大(Step-scaling)的方式，可以使得放大後的影像具有高平滑度同時保留了原有影像邊緣分布的對比性。在某些影像的處理上，我們甚至可以用較低的運算量得到比用Bicubic演算法更令人滿意的結果。

Image up-scaling is a significant technique in the area of image processing and a lot of image up-scaling algorithms have been proposed. However, in order to obtain a high quality scaled image, these algorithms often result in high operation cost and reduce the ability in real time process. On the other hand, algorithms with low operation cost are unable to make a high quality scaled image. The Winscale algorithm which proposed by Kim can adjust image resolution directly in its 2D pixel coordinates and obtain a not bad scaled image by a so-called “area pixel model”. But the quality of scaled image created by the new method is not better than that by the most commonly used Bicubic interpolation.
To improve the Winscale algorithm, we introduced a new method, called “pixel- tagging”, to create the relationship between source image and its scaled image. Applying the method, we proposed an algorithm which can make the scaled image smooth and preserve the edge distribution through the “step-scaling” process. By using the algorithm, we can obtain higher quality and less operation cost than that by Bicubic interpolation with some images.

摘要 2
英文摘要 3
目錄 4
圖目錄 6
表目錄 10
第一章 緒論 11
1.1 研究動機 11
1.2 研究目標 11
1.3 論文大綱 13
第二章 相關研究 14
2.1 函式內插法(Function Interpolation) 15
2.2 幾何內插法(Geometric Interpolation) 16
2.3 邊緣偵測內插法(Edge Detection Interpolation) 18
2.3.1 Sobel Filter 18
2.3.2 Laplacian Filter 18
2.3.3 型態學法 19
2.4 頻域內插法(Frequency Domain Interpolation) 21
2.5 次像素內插法(Subpixel Interpolation) 22
2.6 其他類型演算法 24
2.7 Winscale演算法 25
第三章 邊緣保留之影像放大演算法 31
3.1 基本概念 31
3.2 建立像素座標 33
3.3 像素標記 34
3.4 像素值計算 36
3.5 完整的影像放大流程 36
第四章 實驗結果 38
4.1 實驗方法 38
4.2 以本文方法用不同參數的比較 39
4.3 以Winscale與本文方法用相同參數的比較 42
4.4 以本文方法與其他演算法的比較 47
4.4.1 自然影像 48
4.4.2 人工影像 50
4.5 討論 53
第五章 結論 56
參考文獻 57

[1]S. G. Mallat, “A Wavelet Tour of Signal Processing,” New York: Academic, 1998.

[2]R. G. Keys, "Cubic Convolution Interpolation for Digital Images," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-29, pp. 1153-1160, Dec. 1981.

[3]J. Parker, R. Kenyon and D. Troxel, “Comparison of Interpolating Methods for Image Resampling, “ IEEE Transaction on Medical Imaging, vol. MI-2, pp. 31-39, Mar. 1983.

[4]H. Rabtanen, “Color Video Signal Processing with Median Filters,” IEEE Transaction on Consumed Electrons, vol. 38, No. 3, pp. 157-161, Aug. 1992.

[5]K. Oistamo and Y. Neuvo, “A Motion Intensive Method for Scan Rate Converter and Cross Error Cancellation,” IEEE Transaction on Consumer Electronics, vol. 37, no. 3, pp. 296-301, Aug. 1991.

[6]H. B. Kwon et al. “Pseudomedian Character for De-Interlacing Scan Conversion,” IEEE Journal of Korea Telecommunication Society, vol. 21, no. 1, pp. 1151-1171, 1996.

[7]H. Kim, B. Kwon and M. R. Choi, “An Image Interpolator with Image Improvement for LCD Controller,” IEEE Transactions on Consumer Electronics, vol. 47, pp.263-271, no. 2, May 2001.

[8]C. Gonzalez and R. E. Woods, Digital Image Processing(2nd Edition). Reading, MA: Addsion-Wesley Publishing, Jan. 2002.

[9]W. T. Kuo and S. H. Yen, “A Hybrid Method for Image Enlargement Improvement,” Mater Thesis of TKU, Jul. 2004.

[10]A. M. Darwish, M. S. Bedair and S. I. Shaheen, “Adaptive Resampling Algorithm for Image Zooming Vision,” IEEE Image and Signal Processing, vol 144, Issue 4, pp.207-212 Aug. 1997.

[11]W. P. Lin, C. M. Chen, Y. C. Chen, “Image Compression with Interpolation in Wavelet-transform Domain,” IEEE International Symposium on Circuits and Systems, vol. 3, pp. 2084-2087, May 2005.

[12]K. Kinebuchi, D. D. Muresan and T. W. Parks, “Image Interpolation Using Wavelet -based Hidden Markov Trees,” IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1957-1960, May 2001.

[13]H. F. Ates and M. T. Orchard, “Image Interpolation Using Wavelet-based Contour Estimation,” IEEE International Conference on Multimedia and Expo, vol. 1, pp. I - 101-104, Jul. 2003.

[14]S. C. Park, M. K. Park and M. G. Kang, “Super-resolution Image Reconstruction: A Technical Overview,” IEEE Signal Processing Magazine 20, pp. 21-36, May 2003.

[15]G. Canel, A. M. Tekalp and W. Heinzelman, “Super Resolution Recovery for Multi-camera Surveillance,” IEEE International Conference on Multimedia & Expo, pp. I - 109-I - 112, July 2003.

[16]S. Moshe, B. Ezra, A. Zomet and S. K. Nayar, “Video Super-resolution Using Controlled Subpixel Detector,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 977-987, Jun. 2005.

[17]I. M. Huang and Y. N. Sun, “Super-resolution Techniques for Image Sequence Enlargement,” Mater Dissertation of NCKU, Jul. 2003.

[18]S. Yuan, A. Taguchi and M. Kawamata, “Arbitrary Scale Image Enlargement with the Prediction of High Frequency Components,” IEEE International Symposium on Circuits and Systems, pp. 6264-6267, May 2005.

[19]T. Aso, N. Suetake and T. Yamakawa, “A Codebook-based Digital Image Enlargement Employing a Fuzzy Similarity Measure,” IEEE Conferences on info-tech and Info-net, 2001, vol. 4, pp.38-43, Nov. 2001.

[20]C. K Chow and H. T. Tsui, “Image Enlargement as an Edge Estimation,” IEEE International Joint Conference on Neural Networks, vol. 4, pp.2577-2581, Jul. 2004.

[21]C. B. Atkins, C. A. Bouman, and J.P. Allebach, “Optimal Image Scaling Using Pixel Classification,” IEEE International Conference on Image Processing, vol 3, pp. 864-867, Sep. 2001.

[22]K. H. Chung, Y. H. Fung and Y. H. Chan, “Image Enlargement Using Fractal,” IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.6, pp. 273-276, Apr. 2003.

[23]N. Kato, H. Yasukawa and A. Taguchi, “Expansion of the Image Using the B-spline Surface,” IEEE International Symposium on Intelligent Signal Processing and Communication Systems, pp.559-563, Nov. 2004.

[24]H. Lim, Y. H. Lee and S. Yang, “Image Scaling Using Vector Planes and Directional Information,” IEEE Transactions on Image Processing, vol. 6, pp. 79-80, Jan. 2005.

[25]F. Tao, X. W. Lu and Y. L. Xing, “An Architecture and Implementation of Image Scaling Conversion,” IEEE 4th International Conference on ASIC, pp.409-410 23-25, Oct. 2001.

[26]N. Maganda, M.A. and A. Estrada, “Real-Time FPGA-Based Architecture for Bicubic Interpolation: An Application for Digital Image Scaling,” IEEE International Conference on Reconfigurable Computing and FPGAs, pp. 1-1, Sept. 2005.

[27]C.H. Kim, S.M. Seong, J.A. Lee and L.S. Kim, “ Winscale : An Image-scaling Algorithm Using an Area Pixel Model,” IEEE Transactions Circuits Syst. Video Technol., vol. 13, no. 6, pp. 549-553, Jun. 2003.

[28]E. Aho, J. Vanne, K. Kuusilinna and T. D. Hamalainen, “Comments on “Winscale: an Image-scaling Algorithm Using an Area Pixel Model,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 15, no. 3, pp.454-455, Mar. 2005.

J. Andreadis and A. Amanatiadis, “Digital Image Scaling” IEEE Conference on Inistrumentationi and Measurement Technology, pp.2028-2032, May 2005.