摘要
经典的TV(Total Variation)模型在对图像扩散的同时能有效保持图像边缘,但该类模型所得到的结果具有明显的阶梯效应,其改进的方案之一是在能量泛函中增加高阶项。但其对应的偏微分方程计算效率非常低。基于中值公式开展了如下研究:给出了TV-L2,TV-L1变分图像扩散模型中值公式的四邻域、八邻域计算过程实现;提出了基于散度的高阶图像扩散的中值公式。实验证明高阶TV模型能很好地消除阶梯效应,将中值公式应用于图像扩散模型,提高了计算效率。
The classic TV（Total Variation）model can be used to diffuse images with edge preserving,but it also can result in staircase effects beyond edges.One of the means to reduce staircase effects is to add high order item.But the computation efficiency is low which results in considerable attention in image processing area.Some improvements and applications are presented,which include：Implementations of TV-L2,TV-L1 models are given using four points neighborhood and eight points neighborhood.A new median formula is proposed for higher order image diffusion based on divergence to reduce staircase effects.The median formula is used to complex computation of multiscale denoising model to improve computing efficiency.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第9期159-162,共4页
Computer Engineering and Applications
基金
教育部新世纪优秀人才支持计划(No.NCET-05-0601)
关键词
变分方法
图像扩散
中值公式
TV模型
variational method
image diffusion
median formulas
TV model
