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
Multi-focus image fusion method based on two stage of convolutional neural network
TL;DR: Experimental results demonstrate that the proposed convolutional neural network fusion method is superior to other fusion algorithms in subjective vision and objective assessment.
Journal ArticleDOI
Constrained LSTM and Residual Attention for Image Captioning
TL;DR: A model is proposed that aligns the language model to certain visual structure and also constrains it with a specific part-of-speech template and develops a residual attention mechanism to simultaneously focus on the pre-extracted visual objects and unextracted regions in an image.
Journal ArticleDOI
Multi-scale counting and difference representation for texture classification
TL;DR: Experimental results on Brodatz, VisTex, and Outex databases demonstrate that the proposed multi-scale CDR-based texture classification method outperforms five representative texture classification methods.
Journal ArticleDOI
Multi-focus Image Fusion Based on Multi-scale Gradients and Image Matting
TL;DR: The results show that compared with several state-of-the-art algorithms, the proposed fusion method can obtain accurate decision maps and achieve better performance in visual perception and quantitative analysis.
Journal ArticleDOI
Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images
TL;DR: In this paper, a novel SRC-based method for face recognition with low quality images named sparse low-rank component-based representation (SLCR) is proposed, and the minimum class-wise reconstruction residual is used as the recognition rule, leading to a substantial improvement on the proposed SLCR’s performance.
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