Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Nonlinear Extrapolation Based Image Restoration Using Region Classification

지역 분할을 통한 비선형 외삽법 기반 영상 복원 기법

  • Published : 2009.05.25
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Abstract

In this paper, we propose a locally adaptive image restoration method based on nonlinear extrapolation in frequency domain. In general, the conventional method causes ringing artifacts on the object boundary. To solve this problem, we introduce an improved restoration method which considers textures of an image block. In the proposed method, a blurred image is divided into several blocks, and each block is classified into three groups; simple, one edge, and complex blocks according to the contained texture. Depending on the classification result, adaptive nonlinear extrapolation is applied to each block in a blurred image. Experimental results show that the proposed algorithm can achieve higher quality image in both subjective and objective views as compared with the conventional method.

본 논문에서는 지역 특성에 기반한 주파수 영역에서의 비선형 외삽법을 통한 영상 복원 기법을 제안한다. 기존의 비선형 외삽법은 객체의 경계 부분에서 링잉(ringing) 현상이 발생한다는 단점이 있다. 제안된 영상 복원 기법은 기존의 단점을 보완하기 위해 영상의 지역적 특징을 고려하는 적응적 복원 기법을 도입하였다. 즉, 열화된 영상을 일정 크기의 블록으로 나누고, 각 블록의 특성을 파악하여 단순한 영상을 포함한 블록, 한 개의 경계를 포함한 블록, 그리고 복잡한 영상을 포함한 블록으로 구분한다. 각 블록의 특징에 따라 적응적인 비선형 외삽법을 적용하여 영상을 복원한다. 실험 결과에서 제안하는 알고리즘이 기존의 방법보다 주관적, 객관적 관점에서 향상된 화질을 갖는 것을 알 수 있다.

Keywords

References

  1. M. Den-Ezra and K. Nayar, "Moion-based motion deblurring," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 26, no. 6, pp.689-698, June 2004 https://doi.org/10.1109/TPAMI.2004.1
  2. G. Demoment, "Image reconstruction and restoration: Overview of common estimation structures and problems," IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol.37, no.12, pp.2024-2036, Dec. 1989 https://doi.org/10.1109/29.45551
  3. P. A. Jansson, Deconvolution of Image and Spectra, Acamedic Press, second edition, 1997
  4. Y. L. You and M. Kaveh, "A regularized approach to joint blur identification and image restoration," IEEE Trans. on Image Processing, Vol.5, no.5, pp.416-428, Mar. 1996 https://doi.org/10.1109/83.491316
  5. A Polesel, G. Ramponi, and V. J. Mathews, "Adaptive unsharp masking for contrast enhancement," Proc. ICIP, Oct. 1997, Vol. 1, pp. 267-270 https://doi.org/10.1109/ICIP.1997.647756
  6. H. Greenspan, C. H. Anderson, and S. Akber, "Image enhancement by nonlinear extrapolation in frequency space," IEEE Trans. on Image Processing, Vol. 9, no. 6, pp.1035-1048, June 2000 https://doi.org/10.1109/83.846246
  7. P. J. Burt and E. A. Adelson, "The Laplacian pyramid as a compact image code," IEEE Trans. on Commun., Vol. COM-31, no. 4, pp. 532-540, Apr. 1983
  8. J. Canny, "A Computational Approach to edge detection," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.8, no.6, pp.679-698, Nov. 1986 https://doi.org/10.1109/TPAMI.1986.4767851