Enhanced Image Magnification Using Edge Information

에지정보를 이용한 개선된 영상확대기법

  • 제성관 (부산대학교 전자계산학과) ;
  • 조재현 (부산가톨릭대학교 컴퓨터공학과) ;
  • 차의영 (부산대학교 전자계산학과)
  • Published : 2006.12.30


Image magnification is among the basic image processing operations. The most commonly used technique for image magnification are based on interpolation method(such as nearest neighbor, bilinear and cubic interpolation). However, the magnified images produced by the techniques that often appear a variety of undesirable image artifacts such as 'blocking' and 'blurring' or too takes the processing time into the several processing for image magnification. In this paper, we propose image magnification method which uses input image's sub-band information such as edge information to enhance the image magnification method. We use the whole image and not use the one's neighborhood pixels to detect the edge information of the image that isn't occurred the blocking phenomenon. And then we emphasized edge information to remove the blurring phenomenon which incited of edge information. Our method, which improves the performance of the traditional image magnification methods in the processing time, is presented. Experiment results show that the proposed method solves the drawbacks of the image magnification such as blocking and blurring phenomenon, and has a higher PSNR and Correlation than the traditional methods.

영상처리에서 영상확대기법은 기본적인 처리기법으로 일반적으로 사용되는 기법은 보간법(최근접이웃, 양선형, 3차회선 보간법)이다. 그러나 이러한 보간법은 영상확대시 블록화 현상이나 몽롱화현상과 같은 영상의 손실이 발생하거나 계산량이 많아 처리시간이 길게 나타났다. 따라서 본 논문에서는 입력영상의 부대역정보인 에지정보를 이용하여 기존의 확대기법을 개선하고자 한다. 에지정보를 추출하기 위하여 이웃한 화소들을 이용하지 않고 전체영상을 이용하여 블록화현상이 발생되지 않았다. 그리고 에지가 결여되어 나타나는 몽롱화현상을 제거하기 위하여 검출된 에지정보를 강조시켰다. 실험 결과, 제안된 기법은 기존의 확대기법보다 처리시간을 줄일 수 있었으며, PSNR과 상관계수에서도 성능이 뛰어나 블록화나 몽롱화현상과 같은 문제점을 해결하였다.


  1. S. Battiato, and M. Mancuso, 'An introduction to the digital still camera Technology,' ST Journal of System Research, Special Issue on Image Processing for Digital Still Camera, Vol. 2, No.2, 2001
  2. S. Battiato, G. Gallo, and F. Stanco, 'A Locally Adaptive Zooming Algorithm for Digital Images,' Image and Vision Computing. Elsevier Science B.V., Vol. 20, pp.805-812,2002 https://doi.org/10.1016/S0262-8856(02)00089-6
  3. K. Aoyama, and R. Ishii, 'Image magnification by using Spectrum Extrapolation,' IEEE Proceedings of the IECON, Vol. 3, pp.2266 -2271, 1993
  4. F. M. Candocia, and K. C. Principe, 'Superresolution of Images based on Local Correlations,' IEEE Transactions on Neural Networks, Vol. 10, No.2, pp.372-380, 1999 https://doi.org/10.1109/72.750566
  5. A. Biancardi, L. Cinque, and L. Lombardi, 'Improvements to Image Magnification. Pattern Recognition,' Elsevier Science B.V., Vol. 35, Issue 3, pp.677-687, 2002
  6. R. C. Gonzalez, and Richard E. Woods, Digital image processing, Second edition, Prentice Hall, 2001
  7. J. R. Parker, Algorithms for Image Processing and Computer Vision, Bk&Cd-Rom edition John Wiley & Sons, 1996
  8. The HIPR Image Library, http://homepages. inf.ed. ac.uk/rbf/HIPR2/
  9. The USE-SIPI Image Database, http://sipi.usc.edu/services/database