Blind Restoration Of Blurred And Noisy Images And Ringing Reducing

Traditional image restoration methods are always on the assumptions that the PSF(Point Spread Function) of the system and noise distribution are known, they reconstruct images using different deconvolution approaches, for example, inverse filtering and so on. However, in real image processing, much priori knowledge (including the priori knowledge of image and the system) is not available. So it is required to determine degraded information from the degenerated image itself under the condition of unknowing the systemic PSF, and restore the real image according to the degraded image only, this constitutes the problem blind image deconvolution solving. It is a very practical and challenging subject. Our goal is to realize blind image deconvolution of blurred images whose blur information is ignorant.There are two main reasons in image degradation, one is PSF degenerating, the other is noise. And due to the poor match between the PSF abtained by blind image deconvolution and the real PSF in size and value, there is ringing effect in deblurred image. To aim at the three problems raised above, we solve blind image deconvolution problem with three parts. Part one is denoising. To eliminate noise and enhance edge, we use the method of combining wavelet threshold denoising and canny edge detection. Part two is blind image deconvolution, the means of IBD(Iterative Blind Deconvolution)is used in this part. During this period the initial value of image is estimated by zero sheet separation approach, and the energy redistribution of original method is improved. Part three is ringing effect postprocessing. The Fuzzy filter based on the fuzzy transformation theory is employed to reduce ringing effect. Its computational complexity is low and it gains remarkable results. It is a very effective way to settle ringing effect problem.Blind image deconvolution is a very complex and difficult topic, we should improve the existing theories constantly and propose new algorithm, in the mean while, it will be a new strategy integrating diverse technique used for image restoration. Before blind image deconvolution we make denoising, and after that we process the ringing effect. The combination can get perfect impact and increase the recovery quality remarkably, and decrease the computational complexity in the meantime. Further research to explore combining different digital image restoration technique will be a promising job.