摘要
全变分(TV)模型采用了梯度的1范数作为正则化约束,它能够沿着梯度方向较好地保护图像的边缘信息,但在图像较均匀区域,容易产生"阶梯"效应。利用梯度的可变指数函数作为正则化项,提出TV模型的改进模型,该模型既保持TV模型保护图像边缘信息的优点,又可以明显地减少非边界区域"阶梯"效应的产生,同时把u-f的1范数作为数据保真项增强了模型修复图像破损部分的能力。
The L1 norm of gradient is used as the regularization term in the Total Variation （TV） model which can preserve the edges of the image well. However, it has the staircasing effect in the relatively smooth regions. Using the variable exponent function as the regularization term, the modified model can not only preserve the edges of image as well as the TV model but also decrease the staircasing effect obviously. Simultaneously, the L1 norm ofu -fwas regarded as the fidelity term of the model, which can enhance the ability of image denoising.
出处
《计算机应用》
CSCD
北大核心
2013年第10期2931-2934,共4页
journal of Computer Applications
关键词
图像去噪
全变分模型
可变指数函数
正则化
L1范数
image denoising
Total Variation （TV） model
variable exponent function
regularization
L1 norm
