The super-resolution technology can be applied in many aspects. It can be used to solve image’s blurring and visual artifacts. We proposed a super-resolution method combine feature matching with self-example method. By using feature matching, it extends the search space’s information in multi-scale and multi-orientation, and predicts the high-frequency part to reconstruction by use the self-similarity. Our method has reasonable complexity and higher quality compared to self-example method. It does not need external database and parametric. All of the data is come from the input image.

影像的超解析度技術可以應用在許多方面上，為了解決在使用上遇到解析度不足的情況，藉由各式各樣的演算法來獲得更高解析度的影像。一般來說，影像的超解析度放大通常是用內差演算的方式來快速獲得一個當作基準參考的範例條件，然後再用演算法來處理內差演算所造成的模糊效應或者是視覺上明顯的錯誤。然而使用者只輸入單張影像的情況時，可以應用來判斷重建的資訊太少，使得處理後結果不合預期。因此在這裡我們提出了利用特徵比對去尋找單張影像中的自我相似性的影像的超解析度方法。藉由特徵比對的方法，找出自然影像當中具有相似性的結構，擴充找尋的範圍到不同尺度跟方向上。我們可以從單張影像中獲得更多可供利用的資訊，然後分析此資訊去預估得到高解析度的部分來重建影像。而且比起為了獲得相似結構的資訊，從影像當中尋找所有可能的尺度跟方向。特徵比對能提供較為合理的估測數據，因而我們的方法擁有合理的運算複雜度跟計算時間，比起一般的超解析度技術也擁有較好的重建品質。這個方法使用了類似範例法的運算結構方式，用原本的輸入影像來取得高低解析度範例，卻不需要提供額外包含高低解析度的影像資料庫或是學習另外的設定參數來幫助重建。所有的一切資料都是來自於原本的輸入影像。

References
[1] D. Glasner, S. Bagon, and M. Irani, “Super-resolution from a single image,” in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2009.
[2] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 60, pp. 91-110, Feb. 2004.
[3] G. Freedman and R. Fattal, “Image and video upscaling from local self-examples,” ACM Trans. Graphics, April 2011.
[4] J. Yang, J. Wright, T. Huang, and Y. Ma, “Image super-resolution as sparse representation of raw image patches,” in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., pp. 1-8, 2008.
[5] M. Protter, M. Elad, H. Takeda, and P. Milanfar, “Generalizing the nonlocal-means to super-resolution reconstruction,” IEEE Trans. Image Process., vol.18, no.1, pp.36-51, Jane 2009.
[6] W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super-resolution,” IEEE Comput. Graphics & App., vol. 22, no. 2, pp. 56–65, Mar. 2002.
[7] Y. HaCohen, R. Fattal, and D. Lischinski, “Image upsampling via texture hallucination,” in Proc. IEEE Int. Conf. Comput. Photography, Cambridge MA USA, pp. 20-30, Mar. 2010.
[8] Y. Zhi, Y. Peimin, and L. Sheng, “Super resolution based on scale invariant feature transform,” in Proc. IEEE Int. Conf. Audi., Langu. and Image Process., pp.1550-1554, 7-9 July 2008.
[9] Y. Lou, P. Favaro, S. Soatto, and A. Bertozzi, “Nonlocal similarity image filtering,” Proceedings of the 15th Int. Conf. Image Analysis and Processing, 2009.
[10] M. Irani and S. Peleg, “Motion analysis for image enhancement: resolution, occlusion and transparency,” Journal of Visual Communication and Image Representation, vol. 4, no. 4, pp. 324-335, 1993.
[11] H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robust features,” 9th European Conference on Computer Vision, 2006
[12] L. Juan, and O. Gwun, “A Comparison of SIFT, PCA-SIFT and SURF,” International Journal of Image Processing (IJIP) Vol. 3(4) pp 143-152, 2009.