Journal ArticleDOI
Multi-focus image fusion based on multi-scale sparse representation
Xiaole Ma,Zhihai Wang,Shaohai Hu +2 more
TLDR
A fusion method based on multi-scale sparse representation for registered multi-focus images (MIF-MsSR), which not only reserves the integrity of the information in source images, but also has better fusion performance on subjective and objective indicators than other state-of-the-art methods.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2021-11-01. It has received 4 citations till now. The article focuses on the topics: Sparse approximation & Image fusion.read more
Citations
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Subject evaluation criteria for image fusion used in paper Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain
TL;DR: In this article, the authors proposed a method for navigation system with the assistance of the Navigation Science Foundation of P. R. China (05F07001) and National Natural Science Foundation (NNSF) of China (60472081).
Journal ArticleDOI
A new multi-focus image fusion method based on multi-classification focus learning and multi-scale decomposition
Journal ArticleDOI
Critical reflection on quantitative assessment of image fusion quality
TL;DR: Zhang et al. as mentioned in this paper found that image dissimilarities are unavoidable due to the spectral coverage of different image sensors and that image fusion should integrate these disimilarities when they are representing spatial improvement.
Journal ArticleDOI
GIPC-GAN: an end-to-end gradient and intensity joint proportional constraint generative adversarial network for multi-focus image fusion
Junwu Li,Binhua Li,Yaoxi Jiang +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed a new gradient-intensity joint proportional constraint generative adversarial network for multi-focus image fusion, with the name of GIPC-GAN.
References
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Journal ArticleDOI
Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain
TL;DR: This research presents a probabilistic method to estimate the intensity of the response of the immune system to earthquake-triggered landslides in the Northern Hemisphere.
Journal ArticleDOI
Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters
TL;DR: The proposed hybrid-MSD transform enables to better capture important multi-scale IR spectral features and separate fine-scale texture details from large-scale edge features and proves the superiority of the proposed method compared with conventional MSD-based fusion methods.
Journal ArticleDOI
Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity
TL;DR: This paper unify the image fusion problem into the texture and intensity proportional maintenance problem of the source images, and defines a uniform form of loss function based on these two kinds of information, which can adapt to different fusion tasks.
Journal ArticleDOI
Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure
Yu Zhang,Xiangzhi Bai,Tao Wang +2 more
TL;DR: This paper proposes a novel boundary finding based multi-focus image fusion algorithm, in which the task of detecting the focused regions is treated as finding the boundaries between the focused and defocused regions from the source images.
Journal ArticleDOI
Advances in Multimodal Data Fusion in Neuroimaging: Overview, Challenges, and Novel Orientation
Yudong Zhang,Yudong Zhang,Zhengchao Dong,Shuihua Wang,Shuihua Wang,Shuihua Wang,Xiang Yu,Xujing Yao,Qinghua Zhou,Hua Hu,Hua Hu,Min Li,Min Li,Carmen Jimenez-Mesa,Javier Ramírez,F. J. Martínez,Juan Manuel Górriz,Juan Manuel Górriz +17 more
TL;DR: Overall, multi-modal fusion shows significant benefits in clinical diagnosis and neuroscience research and widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
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