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Research On Image Denoising Algorithm Based On Total Variation And Bidimensional Empirical Mode Decomposition

Posted on:2021-05-08Degree:MasterType:Thesis
Country:ChinaCandidate:P LiuFull Text:PDF
GTID:2428330611457096Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
The presence of noise in the image not only seriously reduces the visual quality and effect,but also brings serious obstacles to subsequent work.Image denoising is the most basic and indispensable research in image processing technology.In recent years,a large number of researchers have carried out research work on how to remove noise in images,and have achieved certain research results,but how to remove noise while retaining more detailed information of original image remains to be further explored.This paper will study the image denoising algorithm based on bidimensional empirical mode decomposition and total variation.The main research contents are as follows:1.Being image denoising algorithm based on high-order TV model,there is a problem that the edges are too smooth after denoising,a gray image denoising algorithm based on boosting high-order non-convex total variational model is proposed.By averaging each denoised image and the original image as the input of the next cycle of the boosting high-order non-convex total variational model and updating the parameters,the Lagrangian multiplier method and the alternating direction multiplier method are used to cycle solve.The simulation experiment results show that the detailed information of the image can be better preserved through multiple iterations of the solution,thereby improving the denoising effect.2.In order to adaptively decompose the image and accurately describe the distribution state of the decomposition coefficients,a new image denoising algorithm based on fast and adaptive bidimensional empirical mode decomposition is proposed.Firstly,the algorithm performs fast and adaptive bidimensional empirical mode decomposition on the image.By determining the number of noise-dominated subband after decomposition,the noise-dominated subband coefficient distribution is further modeled by the normal inverse Gaussian model.Then the Bayesian maximum a posteriori probability estimation theory is used to derive the corresponding threshold from the model.Finally,the optimal linear interpolation threshold function algorithm is used to complete the denoising.Simulation results show that the algorithm effectively retains more image details.3.For correlation between channel of RGB color space,the chromatic aberration exist in color image after denoising.And the TV model is used to cause staircase effect.A color image denoising algorithm based on high-order total variational model in HSV space is proposed.This algorithm transforms the color image from RGB space to HSV space,and uses boosting high-order non-convex total variational model for denoising in each of the three channels.Simulation results show that this method can better retain the details of the denoised image and improve the denoising effect.
Keywords/Search Tags:Image denoising, High order non-convex TV model, Fast and adaptive bidimensional empirical mode decomposition, HSV color space
PDF Full Text Request
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