Self-guided filter for image denoising

被引:21
|
作者
Zhu, Shujin [1 ]
Yu, Zekuan [2 ]
机构
[1] Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Dept Biomed Engn, Nanjing 210023, Peoples R China
[2] Fudan Univ, Acad Engn & Technol, Shanghai 200433, Peoples R China
关键词
image denoising; filtering theory; image texture; exceptional edge-preserving filter; traditional guided filter; desired result; performing image denoising; clear guidance image; effective guided filter variant; single image noise; denoising strategy; weak textured patches based image noise estimation; clear intermediate image; local noise level; state-of-the-art local denoising methods;
D O I
10.1049/iet-ipr.2019.1471
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The guided filter has been acknowledged as an exceptional edge-preserving filter whose output is a locally linear transform of the guidance image. However, the traditional guided filter heavily relies on the guidance image and fails to achieve the desired result when performing image denoising without a clear guidance image. In this study, to address this limitation, the authors propose a simple yet effective guided filter variant for the single image noise removing. They further show that the proposed denoising strategy can be easily realised by using the iterative framework. Moreover, the weak textured patches based image noise estimation is utilised to generate a clear intermediate image which makes the proposed method highly adaptable to the local noise level. Experimental results demonstrate that their proposed algorithm can compete with the state-of-the-art local denoising methods in edge-preserving.
引用
收藏
页码:2561 / 2566
页数:6
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