| Summary: | The image data from imaging system are always degraded by the environment and the imaging sensors. The types of degradation are additive random noise, blurring, relative motion between object and the imaging system, atmosphere turbulence, and so forth. The objective of image restoration is to reduce or eleminate the degradation.
In this research, adaptive Wiener filtering has been used to restore the degraded image. An image will be divided into blocks, called local details. From the degraded image and prior knowledge, some measure of the local details, such as the local variance is determined. A space-variant ,filter which is a function of the local image details and of the additional prior knowledge is then determined. The space-variant filter is then applied to the degraded image in the local region from which the space-variant filter is designed. A number of different algorithms can be develop, depending on which specific measure is used to represent local .image details, how the space-variant is determined as a function of the local image details, and what . prior knowledge is available.
This research shows that Adaptive Wiener Filter is effective enough to eleminate additive noise. The size of block should be adjusted according to the noise characteristics and the feature of an image data
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