An EM Algorithm-Based Approach for Imputation of Pixel Values in Color Image

- Journal title : Korean Journal of Applied Statistics
- Volume 23, Issue 2, 2010, pp.305-315
- Publisher : The Korean Statistical Society
- DOI : 10.5351/KJAS.2010.23.2.305

Title & Authors

An EM Algorithm-Based Approach for Imputation of Pixel Values in Color Image

Kim, Seung-Gu;

Kim, Seung-Gu;

Abstract

In this paper, a frequentistic approach to impute the values of R, G, B-components in random missing pixels of color image is provided. Under assumption that the given image is a realization of Gaussian Markov random field, its model is designed such that each neighbor pixel values for a given pixel follows (independently) the normal distribution with covariance matrix scaled by an evaluates of the similarity between two pixel values, so that the imputation is not to be affected by the neighbors with different color. An approximate EM-based algorithm maximizing the underlying likelihood is implemented to estimate the parameters and to impute the missing pixel values. Some experiments are presented to show its effectiveness through performance comparison with a popular interpolation method.

Keywords

Imputation;random missing pixel;color image;Gaussian Markov random field;EM algorithm;

Language

Korean

References

1.

김승구 (2009). 마코프 랜덤 필드 하에서 정규혼합모형에 의한 다중 결측값 대체기법: 색조영상 결측 화소값 대체에 응용, <한국통계학회논문집>, 16, 914-925.

3.

Blanchet, J. and Vignes, M. (2009). A model-based approach to gene clustering with missing observation reconstruction in a Markov random field framework, Journal of Computational Biology, 16, 475-486.

4.

Dass, S. C. and Nair, V. N. (2003). Edge detection, spatial smoothing, and image reconstruction with partially observed multivariate data, Journal of the American Statistical Association, 98, 77-89.

5.

Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion), Journal of Royal Statistical Society B, 39, 1-38.

6.

Kim, D., Lee, Y. and Oh, H. S. (2006). Hierarchical likelihood-based wavelet method for denoising signals with missing data, IEEE Signal Processing Letters, 13, 361-364.

7.

Ogawa, T., Haseyama, M. and Kitajima, H. (2006). Restoration of missing intensity of still images by using optical flows, System and Computers in Japan, 37, 1786-1795.