Image Smoothing Prior Based Blind Deblurring Algorithm And Its Application On Binary Images

With the development of science and technology,digital image processing is be-coming more and more popular during our daily lives.Blind image deblurring is one of the most important issues in digital image processing.The main goal of blind image deblurring is to estimate both the blur kernel and the latent image with a blurry image as input.In this paper,we propose a new algorithm for blind image deblurring based on the image smoothing prior.Besides,we propose two functions as regularization terms for deblurring binary images and extend one of them to handle with multiple-value images.Firstly,we notice that the salient edges play an important role in estimating blur kernel.If we can find a strategy that can preserve the salient edges of images and smooth out unnecessary details,the blur kernel estimated will be more accurate.According to it,we draw on the experience of image smoothing and propose a new model based on the smoothing prior.We use half-quadratic splitting method to solve the optimization problem.In the experiment part,we test our model on various datasets to show the effectiveness.Compared with existing methods,our model can estimate more accurate blur kernel and generate less artifacts.Next,we concentrate on binary images and propose a new model combining the bi-nary priors and the smoothing priors.For relevant sub-problems,we analyse the mathe-matical properties of the solution and provide solving strategies.In the experiment part,we create a new binary dataset and test our model on it.Compared with other methods,our model can restore clearer latent images.