Blind Deconvolution Algorithm based on Filter and PSF Estimation for Image Restoration

Image restoration is the process of reconstruction of the original image from the observed degraded image. Blind image restoration, that restores a clear ideal image from a single blur image, is the ill-posed problem of finding two unknowns, the point spread function (PSF) and the ideal image. Different methods of blind image restoration, which iteratively approximate the PSF, their performance is sufficiently dependent on the precision of that estimate. When we restore a degraded image, if the blur function is unknown, it is necessary to estimate the PSF and the ideal image by using an input image. A method of alternately repeating PSF and ideal image estimations produces good results. In order to improve the quality of the restored image, a blind deconvolution method is proposed by estimating the blur function of the imaging model. This method is used for PSF estimation. In addition, we propose to use a filter for removing noise.