25-05-2012, 04:20 PM
Image Motion Deblurring Using Different Methods
Image_deblurring2.ppt (Size: 2.55 MB / Downloads: 107)
Motivation
In many applications, such as astronomy, remote sensing, medical imaging, military detection, public security, and video technology, images are the main sources of information.
The ultimate goal of restoration techniques is to reconstruct or recover an image that has been degraded by using a priori knowledge of the degradation phenomenon.
Blurring
It is one of the famous degradation phenomena ,which is a form of bandwidth reduction of an ideal image owing to the imperfect image formation process.
It can be caused by:
Relative motion between the camera and the original scene
Or by an optical system that is out of focus.
By atmospheric turbulence.
Deblurring Methods
The most famous deblurring methods are:
Inverse filters which assume that there is no noise in the blurred image. so it’s noise sensitive.
Least square filters, e.g. Wiener filter.
Iterative filters, e.g. Lucy filter.
Wiener Algorithm:
This algorithm can be used effectively when the frequency characteristics of the image (PSF parameters) and additive noise are known, at least to some degree.
Lucy-Richardson algorithm :
This algorithm performs multiple iterations, using optimization techniques and Poisson statistics with no need to provide information about the additive noise in the degraded image. But it still needs a good knowledge of the image PSF which can be unavailable.