{"title":"双阱势作为扩散函数用于基于偏微分方程的标量图像恢复","authors":"A. Histace, M. Ménard","doi":"10.5220/0002191304010404","DOIUrl":null,"url":null,"abstract":"Anisotropic regularization PDE’s (Partial Differential Equation) raised a strong interest in the field of image processing. The benefit of PDE-based regularization methods lies in the ability to smooth data in a nonlinear way, allowing the preservation of important image features (contours, corners or other discontinuities). In this article, we propose a PDE-based method restoration approach integrating a double-well potential as diffusive function. It is shown that this particular potential leads to a particular regularization PDE which makes it possible integration of prior knowledge about the gradients intensity level to restore. As a proof a feasibility, results of restoration are presented both on ad hoc and natural images to show potentialities of the proposed method.","PeriodicalId":302311,"journal":{"name":"ICINCO-RA","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Double Well Potential as Diffusive Function for PDE-based Scalar Image Restoration Method\",\"authors\":\"A. Histace, M. Ménard\",\"doi\":\"10.5220/0002191304010404\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Anisotropic regularization PDE’s (Partial Differential Equation) raised a strong interest in the field of image processing. The benefit of PDE-based regularization methods lies in the ability to smooth data in a nonlinear way, allowing the preservation of important image features (contours, corners or other discontinuities). In this article, we propose a PDE-based method restoration approach integrating a double-well potential as diffusive function. It is shown that this particular potential leads to a particular regularization PDE which makes it possible integration of prior knowledge about the gradients intensity level to restore. As a proof a feasibility, results of restoration are presented both on ad hoc and natural images to show potentialities of the proposed method.\",\"PeriodicalId\":302311,\"journal\":{\"name\":\"ICINCO-RA\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ICINCO-RA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0002191304010404\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICINCO-RA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0002191304010404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Double Well Potential as Diffusive Function for PDE-based Scalar Image Restoration Method
Anisotropic regularization PDE’s (Partial Differential Equation) raised a strong interest in the field of image processing. The benefit of PDE-based regularization methods lies in the ability to smooth data in a nonlinear way, allowing the preservation of important image features (contours, corners or other discontinuities). In this article, we propose a PDE-based method restoration approach integrating a double-well potential as diffusive function. It is shown that this particular potential leads to a particular regularization PDE which makes it possible integration of prior knowledge about the gradients intensity level to restore. As a proof a feasibility, results of restoration are presented both on ad hoc and natural images to show potentialities of the proposed method.