Image Restoration Using Two Statistical Modeling Methods

The process of image restoration is to restore the degraded image to its original form. The quality of image degraded because of blur and noise added in an image. The quality of image may degrade due to motion of camera, miss-focus between original scene and camera etc. . . The objective of image restoration is to recover the quality of an image nearly equal to its original form So that it is necessary to find out the type of blur is present in an image to recover it or to perform inverse operation of it. The degradation process first identifies the type of blur and noise is present and after that inverse process takes place to improve the corrupted or degraded image. Restoration of degraded quality image is very essential in various application areas. The priori knowledge about an image is very important to perform image restoration task. There are many types of blur models such as motion blur, gaussian blur, uniform blur etc. This paper proposes image deblurring by using local statistical modeling. In this paper various image deblurring methods are discussed which are used to remove blur from the images.