Journal ArticleDOI
Multifocus image fusion scheme based on the multiscale curvature in nonsubsampled contourlet transform domain
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TLDR
A selection principle for lowpass frequency coefficients is presented and the connection between a low-frequency image and the defocused image is investigated and the validity and superiority of the proposed scheme in terms of both the visual qualities and the quantitative evaluation are indicated.Abstract:
An efficient multifocus image fusion scheme in nonsubsampled contourlet transform (NSCT) domain is proposed. Based on the property of optical imaging and the theory of defocused image, we present a selection principle for lowpass frequency coefficients and also investigate the connection between a low-frequency image and the defocused image. Generally, the NSCT algorithm decomposes detail image information indwells in different scales and different directions in the bandpass subband coefficient. In order to correctly pick out the prefused bandpass directional coefficients, we introduce multiscale curvature, which not only inherits the advantages of windows with different sizes, but also correctly recognizes the focused pixels from source images, and then develop a new fusion scheme of the bandpass subband coefficients. The fused image can be obtained by inverse NSCT with the different fused coefficients. Several multifocus image fusion methods are compared with the proposed scheme. The experimental results clearly indicate the validity and superiority of the proposed scheme in terms of both the visual qualities and the quantitative evaluation.read more
Citations
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Journal ArticleDOI
Multi-focus image fusion with a deep convolutional neural network
TL;DR: A new multi-focus image fusion method is primarily proposed, aiming to learn a direct mapping between source images and focus map, using a deep convolutional neural network trained by high-quality image patches and their blurred versions to encode the mapping.
Journal ArticleDOI
Multi-focus image fusion: A Survey of the state of the art
TL;DR: A comprehensive overview of existing multi-focus image fusion methods is presented and a new taxonomy is introduced to classify existing methods into four main categories: transformdomain methods, spatial domain methods, methods combining transform domain and spatial domain, and deep learning methods.
Journal ArticleDOI
Image Segmentation-Based Multi-Focus Image Fusion Through Multi-Scale Convolutional Neural Network
Chaoben Du,Shesheng Gao +1 more
TL;DR: A novel image segmentation-based multi-focus image fusion algorithm that achieves segmentation through a multi-scale convolutional neural network and can achieve an optimum fusion performance in light of both qualitative and quantitative evaluations is addressed.
Journal ArticleDOI
A novel dictionary learning approach for multi-modality medical image fusion
TL;DR: The comparative experimental results and analyses reveal that the proposed method achieves better image fusion quality than existing state-of-the-art methods.
Journal ArticleDOI
Multi-Focus Image Fusion With a Natural Enhancement via a Joint Multi-Level Deeply Supervised Convolutional Neural Network
Wenda Zhao,Dong Wang,Huchuan Lu +2 more
TL;DR: A novel end-to-end multi- focus image fusion with a natural enhancement method based on deep convolutional neural network (CNN) that can deliver superior fusion and enhancement performance than the state-of-the-art methods in the presence of multi-focus images with common non-focused areas, anisotropic blur, and misregistration.
References
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TL;DR: Experimental results clearly indicate that this metric reflects the quality of visual information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms.
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