Multi-focus image fusion using pulse coupled neural network

Huang, Wei; Jing, Zhongliang

doi:10.1016/j.patrec.2007.01.013

Cited by 176 publications

(51 citation statements)

References 17 publications

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“…These methods are discrete wavelet transform (DWT), SF (Li's method [19]), PCNN1 (Huang's method [8]), PCNN2 (Miao's method [7]), RPCA (Wan's method [12]). Due to the lack of original source code, this paper uses the Eduardo Fernandez Canga's Matlab image fusion toolbox [20] as a reference for DWT, SF.…”

Section: Resultsmentioning

confidence: 99%

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A Novel Pulse Coupled Neural Network Based Method for Multi-focus Image Fusion

Zhang¹,

Chen²,

Zhao³

et al. 2014

IJSIP

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Multi-focus image fusion means to fuse multiple source images with different focus settings into one image, so that the resulting image appears sharper. In order to extract the focused regions of the fused image efficiently, a novel pulse coupled neural network (PCNN) method for multi-focus image fusion is proposed. The registered source images are decomposed into principal components and sparse components by robust principal component analysis (RPCA) decomposition, and the important features of the sparse components are used to motivate the PCNN neurons, whose outputs detect the focused regions of the source images and integrate them to construct the final fused image. Experimental results show that the proposed scheme works better in extracting the focused regions and improving the fusion quality compared to the other existing fusion methods in terms of mutual information (MI) and

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Section: Resultsmentioning

confidence: 99%

“…It has been observed that PCNN based image fusion methods outperform the conventional methods [6]. So far, many multi-focus image fusion methods based on PCNN have been proposed [7][8][9]. However, most of them suffer from various problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Pulse Coupled Neural Network Based Method for Multi-focus Image Fusion

Zhang¹,

Chen²,

Zhao³

et al. 2014

IJSIP

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“…A greater EOL value results in clearer image. According to Reference [30], EOL is superior when evaluating image quality compared with classical criteria. In EOL, the Laplacian operator is used, and the location distribution of a given pixel and the eight adjacent neighbors of the pixel are fully considered.…”

Section: Fusion Rule For High-frequency Sub-band Coefficientsmentioning

confidence: 99%

Medical Image Fusion Combined with Accelerated Non-negative Matrix Factorization and Expanded Laplacian Energy in Shearlet Domain

Lai¹,

Wang²,

He³

et al. 2017

JESTR

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Shearlet transform has been widely applied in related fields due to its admirable properties in image approximation. Image fusion method based on accelerated non-negative matrix factorization (ANMF) and expanded energy of Laplace (EEOL) rules was proposed in this study to integrate the complementary information of medical images with multiple modalities and improve the accuracy of clinical diagnosis and therapy. First, the registered medical images were decomposed into low-and high-frequency sub-band coefficients in shearlet domain. Then, the ANMF rule was used in merging low-frequency coefficients. Next, the visual-contrast-based EEOL rule was adopted in extracting details of source images from high-frequency coefficients. Finally, the ultimate fused image was reconstructed by applying inverse shearlet transform. Experimental results reveal that aside from visual effect, the proposed method achieves the best in three of five criteria and the run time is reduced by 29.21% compared with a method based on non-subsampled contourlet transform (NSCT) in computed tomography (CT)-magnetic resonance imaging (MRI) fusion. Moreover, the proposed method takes the first place in four of five criteria with run time reduced by 48.32% and 24.55% compared with two shearlet-based methods in a MRI-positron emission tomography (PET) case. This study indicates that the proposed method is superior to the selected approaches in visual and statistical evaluation, which is conducive to clinical practice of medical image fusion.

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“…It works better in preserving edge and texture information, but suffers from contrast reduction. Huang et al [9] have proposed a fast fusion method based on energy of the image laplacian (EOL) motivated PCNN in spatial domain. It produces the blocking artifacts in fused image while improves the fusion speed.…”

Section: Introductionmentioning

confidence: 99%