The Image Restoration Algorithm Based On Nonlocal Means Regularization Method

As the most commonly used information carrier in human social activities,image is an important tool for human to understand the world.However,due to the existing technologies and equipments are imperfect,there exists a certain degree of quality degradation,which may affect the subsequent image processing in the process of image acquisition and transmission.Therefore,how to get the high quality image has become the research direction of many scholars.The image restoration technique aims to restore the high-quality image from its degraded image.Based on the nonlocal means regularization method,two new regularization methods are given in this paper.Experiments show that our methods are of better performances than other methods.We empirically found that the distribution of the residual in the nonlocal means denoising algorithm?differences between the noisy image and the denoised result?is heavy-tailed,which fits well the Laplacian distribution.Based on this observation,a new regularization model is proposed by using the l₁-norm to describe the nonlocal means residual.Then it is solved by utilizing the Bregman operator splitting algorithm,which can be regarded as an extension of Plug-and-Play priors algorithm.Experimental results show that the new model achieves better performance than l2-nonlocal means regularization model and other similar models in terms of both preserving the edges and details of the image.In addition,based on above model,a new regularization model is proposed by using the weighted l₁-norm constraint residual as the regularization term.On one hand,this model can ensure the sparseness of the method-noise of a clean image by using the l₁-norm as the prior constraint.On the other hand,a weight function is introduced to the method-noise so that it can protect the edge structure information.Then the corresponding optimization algorithm is designed by utilizing the global sparse gradient algorithm and the Bregman operator splitting algorithm.Numerical experiments show that the proposed method obtain better results than other similar regularization methods,and our resulting images have better visual effects.