Double Well Potential as Diffusive Function for PDE-based Scalar Image Restoration Method

ICINCO-RA Pub Date : 2009-07-02 DOI:10.5220/0002191304010404
A. Histace, M. Ménard
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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.
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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
各向异性正则化偏微分方程在图像处理领域引起了极大的兴趣。基于pde的正则化方法的好处在于能够以非线性方式平滑数据,允许保留重要的图像特征(轮廓、角点或其他不连续点)。在本文中,我们提出了一种基于偏微分方程的方法,该方法将双井势作为扩散函数进行积分。结果表明,这一特定的势导致了一个特定的正则化PDE,这使得关于梯度强度水平的先验知识的整合成为可能。为了证明该方法的可行性,给出了在自然图像和临时图像上的恢复结果,以显示该方法的潜力。
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