Restoration is the main process in many applications. Restoring an original image from a damaged image is the foundation of the restoring operation, either blind or non-blind. One of the main challenges in the restoration process is to estimate the degradation parameters. The degradation parameters include Blurring Function (Point Spread Function, PSF) and Noise Function. The most common causes of image degradation are errors in transmission channels, defects in the optical system, inhomogeneous medium, relative motion between object and camera, etc. In our research, a novel algorithm was adopted based on Circular Hough Transform used to estimate the width (radius, sigma) of the Point Spread Function. This algorithm is based on the PSF, which represents the redistribution of energy in the image plane of a point source located in the object plane. A second novel algorithm was adopted to estimate the variance of the added noise, based on dividing the degraded image into sub homogeneous images. The result shows that these two algorithms give excellent results for estimating the PSF and Noise Variance, and for different values of PSF widths and Noise variances, compared to real PSF widths and Noise Variances values.
