Image Inpainting Method Based On L0 Norm Constraint

Vision is one of the most important ways for humans to extract information.Meanwhile,images are one of the most intuitive ways for human visual perception.Due to the influence of the transmission process,storage methods and human factors,real-life images usually have problems such as blurring or missing pixels,which brings certain difficulties to the further processing and analysis of the image.Therefore,the research of image inpainting technology has become extremely important.The image inpainting technology is based on the known pixel information to fill in missing area in a specific way,so that the repaired traces are not easy to be noticed.This paper analyzes the classic image inpainting model,and uses the sparsity of the L0 norm to establish the inpainting model based on variational and sparse representation.The L0 norm contained is a combinatorial optimization problem with a large amount of calculation in the model.Therefore,this article will introduce an intermediate variable to transform the L0 norm into a minimum problem with constraints,design an optimization algorithm according to the characteristics of the model,and carry out a numerical experiment simulation.Firstly,two new methods of non-blind and blind image inpainting based on L0 norm are proposed.Assuming that the image has a sparsity prior,the L0 norm is applied to the noise-free image inpainting based on variation.Using the sparsity of the L0 norm to constrain the data items,and the regular term adopts the TV prior.A new non-blind image inpainting method based on L0 norm is proposed.Under normal circumstances,the missing area is also unknown,and it is necessary to obtain the decision of confrontation between the original image and the missing area.Under such circumstances,it is very appropriate to use game theory to solve this problem.Therefore,on the basis of non-blind image inpainting,not only the L0 norm is applied to the data item,but also the missing area is constrained by it.A new blind image inpainting method based on game theory is proposed.In the numerical solution,the L0 norm problem is transformed into an optimization problem with constraints using its variational form in the model,and the PADMM algorithm and an alternate algorithm based on game theory are designed.The experimental results show that the two algorithms proposed have relatively high PSNR values,and can effectively process the image information of the damaged area in this paper.Secondly,the L0 norm is replaced with the L1 norm constrained by the original regular term,and a semi-smooth Newton sparse estimation method based on the L0 norm is proposed.The L0 norm is applied to the image inpainting model based on the sparse estimation method,and the L0 norm problem in the original model is further rewritten as a mathematical programming problem with constraints through its equivalent form.In the process of solving,the semi-smooth Newton method uses the second derivative information and has the characteristic of linear convergence.An alternating solution algorithm based on the semi-smooth Newton method is designed.Experimental results show that whether it is a one-dimensional signal or a two-dimensional image,the algorithm proposed can recover the original data more effectively,which shows the robustness and effectiveness of the algorithm in this paper.