<abstract xmlns="http://www.w3.org/1999/xhtml"><p> A variety of structure-adaptive filters are proposed to overcome the blurred effects of image structures caused by the classical Gaussian weighted mean filter. However, two major issues are needed to be dealt with carefully for structure-adaptive anisotropic filters. One is to properly construct the filter kernel and the other is to accurately estimate the orientation of the image structures. In this paper we propose to improve the structure-adaptive anisotropic filtering approach based on the nonlinear structure tensor (NLST) analysis technique. According to the anisotropism measurements of image structures, a new kernel construction method is designed to make the filter shape fine adapted to image features. Through the accurately estimated orientation of the image structures, the filter kernels are then properly aligned to perform the filtering process. Experimental results show that the proposed filter denoises the noisy images carefully and image features, such as corners and junctions are well preserved. Compared with some other known filters, the proposed filter obtains great improvements both in Mean Square Error (MSE) and visual quality.</p></abstract>

A variety of structure-adaptive filters are proposed to overcome the blurred effects of image structures caused by the classical Gaussian weighted mean filter. However, two major issues are needed to be dealt with carefully for structure-adaptive anisotropic filters. One is to properly construct the filter kernel and the other is to accurately estimate the orientation of the image structures. In this paper we propose to improve the structure-adaptive anisotropic filtering approach based on the nonlinear structure tensor (NLST) analysis technique. According to the anisotropism measurements of image structures, a new kernel construction method is designed to make the filter shape fine adapted to image features. Through the accurately estimated orientation of the image structures, the filter kernels are then properly aligned to perform the filtering process. Experimental results show that the proposed filter denoises the noisy images carefully and image features, such as corners and junctions are well preserved. Compared with some other known filters, the proposed filter obtains great improvements both in Mean Square Error (MSE) and visual quality.

Improved structure-adaptive anisotropic filter based on a nonlinear structure tensor

Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China

School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710032, China

Autocontrol Research Institute, Xi'an Jiaotong University, Xi'an, China

Cybernetics and Information Technologies

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A variety of structure-adaptive filters are proposed to overcome the blurred effects of image structures caused by the classical Gaussian weighted mean filter....

Improved structure-adaptive anisotropic filter based on a nonlinear structure tensor

Published Online: Apr 09, 2014

Page range: 112 - 127

DOI: https://doi.org/10.2478/cait-2014-0009

Keywords
Structure-adaptive anisotropic filter, non-linear structure tensor, image denoising, orientation estimation

© by Jie Wu

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Improved structure-adaptive anisotropic filter based on a nonlinear structure tensor

Published Online: Apr 09, 2014

Page range: 112 - 127

DOI: https://doi.org/10.2478/cait-2014-0009

KeywordsStructure-adaptive anisotropic filter, non-linear structure tensor, image denoising, orientation estimation

© by Jie Wu

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords
Structure-adaptive anisotropic filter, non-linear structure tensor, image denoising, orientation estimation