Journal ArticleDOI
A novel multi-modality image fusion method based on image decomposition and sparse representation
TLDR
A novel image fusion scheme based on image cartoon-texture decomposition and sparse representation is proposed, which outperforms the state-of-art methods, in terms of visual and quantitative evaluations.About:Â
This article is published in Information Sciences.The article was published on 2017-09-01. It has received 287 citations till now. The article focuses on the topics: Image texture & Image fusion.read more
Citations
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Journal ArticleDOI
Infrared and visible image fusion methods and applications: A survey
TL;DR: This survey comprehensively survey the existing methods and applications for the fusion of infrared and visible images, which can serve as a reference for researchers inrared and visible image fusion and related fields.
Journal ArticleDOI
Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain
TL;DR: Experimental results demonstrate that the proposed method can obtain more competitive performance in comparison to nine representative medical image fusion methods, leading to state-of-the-art results on both visual quality and objective assessment.
Journal ArticleDOI
Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis
TL;DR: Experimental results demonstrate that the proposed CS-MCA model can outperform some benchmarking and state-of-the-art SR-based fusion methods in terms of both visual perception and objective assessment.
Journal ArticleDOI
Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning
TL;DR: This paper develops a novel medical image fusion, denoising, and enhancement method based on low-rank sparse component decomposition and dictionary learning that consistently outperforms existing state-of-the-art methods in terms of both visual and quantitative evaluations.
Journal ArticleDOI
Infrared and visible image fusion based on target-enhanced multiscale transform decomposition
TL;DR: Qualitative and quantitative experimental results on publicly available datasets demonstrate that the proposed target-enhanced multiscale transform (MST) decomposition model for infrared and visible image fusion can generate fused images with clearly highlighted targets and abundant details.
References
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Journal ArticleDOI
Clustering by fast search and find of density peaks
Alex Rodriguez,Alessandro Laio +1 more
TL;DR: A method in which the cluster centers are recognized as local density maxima that are far away from any points of higher density, and the algorithm depends only on the relative densities rather than their absolute values.
Journal ArticleDOI
Image information and visual quality
Hamid R. Sheikh,Alan C. Bovik +1 more
TL;DR: An image information measure is proposed that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image and combined these two quantities form a visual information fidelity measure for image QA.
Journal ArticleDOI
Kernel Regression for Image Processing and Reconstruction
TL;DR: This paper adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more and establishes key relationships with some popular existing methods and shows how several of these algorithms are special cases of the proposed framework.
Journal ArticleDOI
A wavelet-based image fusion tutorial
TL;DR: This tutorial performs a synthesis between the multiscale-decomposition-based image approach, the ARSIS concept, and a multisensor scheme based on wavelet decomposition, i.e. a multiresolution image fusion approach.
Journal ArticleDOI
A Statistical Approach to Texture Classification from Single Images
Manik Varma,Andrew Zisserman +1 more
TL;DR: A method of reliably measuring relative orientation co-occurrence statistics in a rotationally invariant manner is presented, and whether incorporating such information can enhance the classifier’s performance is discussed.
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