Research On Text Image Deblurring Based On L 0 Regularization

The Scene text image deblurring includes texture deblurring and non-texture deblurring.However,the image deblurring is a ill-posed inverse problem.Solving this problem directly,cannot get accurate solution.So,the Lp regularization method will be used to deal with this in the text image deblurring.The main works as following:Analysis of TV regularization model shows that the fidelity items is relatively simple,and the regularization items are relatively rich.The Lo norm in the regularization term is an NP combination optimization problem,which is difficult to solve.Although the L0 norm problem can often be solved by the approximate L1 norm,but the L1 norm is usually not the most sparse solution.Secondly,the existing image deblurring method,which applied to text blurred images,and can not achieve good results.Aiming at the above problem,in order to improve the quality of text image deblurring,designed a text image deblurring method based on Lo regularization.For the data fidelity term,the intensity feature and gradient feature based on the text pixel are used as a prior knowledge,which method can effectively use the Lo norm regularization to constrain the intensity characteristics of the text image,and can use the gradient feature of the text image to keep the edge changes of the text image.For the Lo minimum regularization problem,the most efficient half-quadratic splitting method is used to solve this problem,and the auxiliary variables and the alternating iterative method are added to the half-quadratic splitting method,which makes the deblurring results better,at the same time,using the pixel gradient of the intermediate variable to estimate the image blur kernel.Compared with the existing text image deblurring method,the experiment proves that the text deblurring method in this paper can achieve better deblurring effect,and can keep the edge detail of the text image,thus verifying the excellence of article method from subject and object.