Abstract
In this paper, an adaptive and computationally efficient filter is proposed for suppressing the unknown and non-stationary noises which exist in monochrome or colour images. This new methodology is based on estimating the unknown noise characteristics by using the generalized Gaussian probability density function (gGf). This function is used as a generic noise distribution model for various types of noise. Depending upon the shape parameter of the gGf, a specific model for the noise is assumed. It has been shown that the shape parameter, which characterizes the gGf, can be found easily by extracting the sample points from the corrupted image. Once the unknown noise distribution is modeled by the gGf, a suitable filter is utilized to smooth out the noise from the input image. Simulation results had shown that the proposed methodology is able to suppress effectively the unknown types of noise in the image.
| Original language | English |
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| Pages (from-to) | 696-699 |
| Number of pages | 4 |
| Journal | Canadian Conference on Electrical and Computer Engineering |
| Volume | 2 |
| Publication status | Published - 1997 |
| Externally published | Yes |
| Event | Proceedings of the 1997 Canadian Conference on Electrical and Computer Engineering. CCECE'97. Part 2 (of 2) - St.John, Can Duration: 25 May 1997 → 28 May 1997 |