Blind Restoration Of Gauss Blurred Images

The purpose of image restoration is to rebuild original image from observation of its degraded image. It is studied widely as is the basis of image processing, model identification, machine vision, and so on. It has been applied on such fields as astronomical, remote sensing and medical image. As an important aspect of image restoration, blind restoration has got more and more attentions in recent years. In case of unknowing point spread functions (PSF), blind-restoration can gain optimum clear effect, thus be more fitted for engineering real situation. Many systems can be simulated by Gauss PSF because that it is very common in many optical image systems and measuring systems. Based on these reasons above, this thesis studies the blind restoration methods of Gauss blurred image so as to reach a better restoration effect.1. An simple introduction to image noise and de-noise methods is given in this thesis, and six different denoise methods are compared experimentally.2. The most similar PSF of degraded image is searched by means of maximum likelihood estimation, i.e., the most fitted PSF to maximize likelihood function is estimated iteratively.3. The constrained least square image restoration by using estimated PSF is mainly described, and the method for choosing regularized parameter is also analyzed.4. At last, the methods for restoration of Gauss blurred images according to simulations in this thesis are summed up, and the work of future is pointed out...