Image De-noising Based On Multi-scale And Intersecting Cortical Mode

Image acquisition system has been widely used in various areas,receives more and more attention.Because of the system and environmental factors,images acquired have a lot of different types noise which affects the image visual quality,so that people or equipment to obtain the information is not comprehensive enough,a direct impact on image follow-up operations,such as image segmentation,image fusion,and other operations.The image de-noising is particularly important in the whole image process system,it's purpose is to maximize the removal of noise in the presence of image information to retain useful information in the image and improve image quality.In recent years,with the application of wavelet technology,artificial neural network model and partial differential in image de-noising,image de-noising has become a very active research topic in image processing,pattern recognition and other fields.In this paper,the theory of multi-scale geometric analysis and artificial neural network model is studied,and an improved image de-noising algorithm is proposed.The main contents of this paper are as follows:(1)The application of multi-scale decomposition and reconstruction tools in image de-no is ing is studied.Wavelet transform is widely used in the transform domain because it can sufficiently highlight the noise.Contourlet transform not only has all the advantages of wavelet transform and has the advantages of multi-directional.NSCT transform has all the advantages of Contourlet transform and has the same invariance,but there is no fast algorithm and its application to image de-noising will produce the shortcoming of the phenomenon of pseudo-Gibbs.(2)Analysis two key factors of threshold de-noising effect:Threshold and Threshold Functions,we proposed an adaptive threshold based on the energy of decomposition coefficients.In order to preserve the merits of the soft and hard threshold functions and to remove their shortcomings,We propose a dual threshold function based on adaptive threshold.According to the threshold,this threshold function divides the high frequency decomposition coefficient into high energy coefficient,medium energy coefficient,low energy coefficient.So that the Contourlet coefficients of different energies can be treated differently.(3)Analysis the advantages and disadvantages of the two kinds of artificial neural network model:the pulse coupled neural network model and intersecting cortical model.Because of the pulse coupled neural network model is complex,many parameters are to be determined,while the intersecting cortical model is relatively simple and has less parameters to be determined.By modifying the intersecting cortical model,this model only rely on the independent information of each node in the processing time,and the threshold function is changed into the concept of the timing matrix in conjunction with the number of iterations.The timing matrix contains information about the space and determines the processing period and the number of iterations.(4)Different algorithms are proposed for two different types of noise based on the timing matrix.A variable step size gray scale adjustment function is proposed for Gaussian noise.The size of the adjustment step size is determined according to the information in the image itself.In this paper,a large number of simulation experiments are carried out.The comparison between the de-noised images obtained by other algorithms and the proposed algorithm shows the effectiveness and superiority of the proposed method.The experimental results show that the proposed algorithm has better visual effect,and the objective evaluation standard is superior to other algorithms.