基于双向耦合扩散的保持特征的边缘锐化和图像增强

在过去的十几年中,偏微分方程在图像增强中得到了越来越多的研究和应用.该文提出了一个保持特征的双向耦合扩散框架.这个框架沿着等照度线(边缘)的梯度方向,利用柔和的边缘判定实施反向扩散以锐化边缘;而相反地沿切线方向实施正向扩散以去除噪声和锯齿伪像.为了消除这两个相反的扩散力彼此之间的冲突,将算法分裂为一种耦合的格式,而且为了保持图像特征,利用图像的局部微分几何特征调整非线性扩散系数.实验结果显示,文中算法可以显著地提高被增强图像的视觉质量.

Feature Preserving Coupled Bidirectional Flow for Edge Sharpening and Image Enhancement

In the past decade there has been a growing amount of research concerning partial differential equations in image enhancement.In this paper,a feature preserving coupled bidirectional flow is presented,where an inverse diffusion with a soft edge decision is performed to sharpen edges along the normal directions to the isophote lines(edges),while a normal diffusion is done to remove noise and artifacts(jaggies)along the tangent directions on the contrary.The two converse diffusion forces are split into a coupled form to stop the cancellation between each other.To preserve image features,the nonlinear diffusion coefficients are adjusted according to the local differential geometry of image.Experimental results demonstrate that the algorithm substantially improves the subjective quality of the enhanced images.