An Adaptive Gradient-Projection Image Restoration using Spatial Local Constraints and Estimated Noise 


Vol. 32,  No. 10, pp. 975-981, Oct.  2007


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  Abstract

In this paper, we propose a spatially adaptive image restoration algorithm using local and statistics and estimated noise. The ratio of local mean, variance, and maximum values with different window size is used to constrain the solution space, and these parameters are computed at each iteration step using partially restored image. In addition, the additive noise estimated from partially restored image and the local constraints are used to determine a parameter for controlling the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared to the non-adaptive algorithm. In addition, a smooth solution with a controlled degree of smoothness is obtained without a prior knowledge about the noise. Experimental results demonstrate that the proposed algorithm requires the similar iteration number to converge, but there is the improvement of SNR more than 0.2 ㏈ comparing to the previous approach.

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  Cite this article

[IEEE Style]

M. Hong, "An Adaptive Gradient-Projection Image Restoration using Spatial Local Constraints and Estimated Noise," The Journal of Korean Institute of Communications and Information Sciences, vol. 32, no. 10, pp. 975-981, 2007. DOI: .

[ACM Style]

Min-Cheol Hong. 2007. An Adaptive Gradient-Projection Image Restoration using Spatial Local Constraints and Estimated Noise. The Journal of Korean Institute of Communications and Information Sciences, 32, 10, (2007), 975-981. DOI: .

[KICS Style]

Min-Cheol Hong, "An Adaptive Gradient-Projection Image Restoration using Spatial Local Constraints and Estimated Noise," The Journal of Korean Institute of Communications and Information Sciences, vol. 32, no. 10, pp. 975-981, 10. 2007.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880