Image fusion based on nonlinear structure tensor

2011 ◽  
Author(s):  
Yukun Wang ◽  
Bibo Lu ◽  
Chunli Miao
Keyword(s):  
Image Fusion ◽  
Structure Tensor ◽  
Nonlinear Structure

Image Segmentation Based on GrabCut Framework Integrating Multiscale Nonlinear Structure Tensor

2009 ◽  
Vol 18 (10) ◽  
pp. 2289-2302 ◽  
Author(s):  
Shoudong Han ◽  
Wenbing Tao ◽  
Desheng Wang ◽  
Xue-Cheng Tai ◽  
Xianglin Wu
Keyword(s):  
Image Segmentation ◽  
Structure Tensor ◽  
Nonlinear Structure

MRI and PET image fusion using structure tensor and dual ripplet-II transform

2017 ◽  
Vol 77 (17) ◽  
pp. 22649-22670 ◽  
Author(s):  
Hamid Reza Shahdoosti ◽  
Adel Mehrabi
Keyword(s):  
Image Fusion ◽  
Structure Tensor

Unsupervised Deep Image Fusion With Structure Tensor Representations

2020 ◽  
Vol 29 ◽  
pp. 3845-3858 ◽  
Author(s):  
Hyungjoo Jung ◽  
Youngjung Kim ◽  
Hyunsung Jang ◽  
Namkoo Ha ◽  
Kwanghoon Sohn
Keyword(s):  
Image Fusion ◽  
Structure Tensor ◽  
Tensor Representations ◽  
Deep Image

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

2014 ◽  
Vol 14 (1) ◽  
pp. 112-127 ◽  
Author(s):  
Jie Wu ◽  
Zuren Feng ◽  
Zhigang Ren
Keyword(s):  
Adaptive Filters ◽  
Visual Quality ◽  
Image Features ◽  
Structure Tensor ◽  
Weighted Mean ◽  
Nonlinear Structure ◽  
Anisotropic Filtering ◽  
Analysis Technique ◽  
Filter Kernel ◽  
Mean Filter

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.

scholarly journals Medical image fusion based on variational and nonlinear structure tensor

2018 ◽  
Vol 189 ◽  
pp. 10021
Author(s):  
Xiaobei Wang ◽  
Rencan Nie ◽  
Xiaopeng Guo
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Energy Functional ◽  
Structure Tensor ◽  
Fusion Method ◽  
Edge Information ◽  
Medical Image Fusion ◽  
Fused Image ◽  
New Energy ◽  
Fusion Methods

Medical image fusion plays an important role in detection and treatment of disease. Although numerous medical image fusion methods have been proposed, most of them decrease the contrast and lose the image information. In this paper, a novel MRI and CT image fusion method is proposed combining rolling guidance filter, structure tensor, and nonsubsampled shearlet transform (NSST). First, the rolling guidance filter and the sum-modified laplacian (SML) operator are introduced in the algorithm to construct the weight maps in non-linear domain, then the fused gradient is firstly obtained by a new weighted structure tensor fusion method, and the fused image is firstly acquired in NSST domain, finally, a new energy functional is defined to constrain the gradient and pixel information of the final fused image close to the pre-fused gradient and the pre-fused image, experimental results show that the proposed method can retain the edge information of source images effectively and preserve the reduction of contrast.

scholarly journals Variational multi-source image fusion based on the structure tensor

2013 ◽  
Vol 62 (21) ◽  
pp. 214204
Author(s):  
Zhao Wen-Da ◽  
Zhao Jian ◽  
Xu Zhi-Jun
Keyword(s):  
Image Fusion ◽  
Structure Tensor ◽  
Source Image
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