Implementation of Neural Filter Optimal Algorithms for Image Restoration 


Vol. 6,  No. 7, pp. 1980-1987, Jul.  1999
10.3745/KIPSTE.1999.6.7.1980


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  Abstract

Restored image is always lower quality than original one due to distortion and noise. The purpose of image restoration is to improve the image quality by fixing the noise or distortion information. One category of spatial filters for image restoration is linear filter. This filter algorithm is easily implemented and can be suppressed the Gaussian noise effectively, but not so good performance for spot of impulse noise. In this paper, we propose the nonlinear spatial filter algorithm for image restoration called the optimal adaptive multistage filter(OAMF). The OAMF is used to reduce the filtering time, increases the noise suppression ratio and preserves the edge information. The OAMF optimizes the adaptive multistage filter(AMF) by using weight learning algorithm of back-propagation learning algorithm. Simulation results of this filter algorithm are presented and discussed.

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[IEEE Style]

L. B. Ho and M. B. Jin, "Implementation of Neural Filter Optimal Algorithms for Image Restoration," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 6, no. 7, pp. 1980-1987, 1999. DOI: 10.3745/KIPSTE.1999.6.7.1980.

[ACM Style]

Lee Bae Ho and Moon Byoung Jin. 1999. Implementation of Neural Filter Optimal Algorithms for Image Restoration. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 6, 7, (1999), 1980-1987. DOI: 10.3745/KIPSTE.1999.6.7.1980.

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