Image fusion by pulse couple neural network with shearlet

Geng, Peng; Wang, Zhengyou; Zhang, Zhigang; Xiao, Zhong

doi:10.1117/1.oe.51.6.067005

Cited by 55 publications

(41 citation statements)

References 15 publications

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“…Fusion method based on MGA tools, such as multifocus image fusion method of ripplet transform based on cycle spinning [9], the fusion is also performed using the fusion method of choosing larger SML based on contourlet domain proposed in [11], the fusion method based on the combination of NSCT and PCNN proposed in [12], the fusion method based on the combination of ST and PCNN proposed in [14] are the comparison methods.…”

Section: Resultsmentioning

confidence: 99%

“…When the PCNN is used in digital image processing, each pixel corresponds to a PCNN neuron. This paper applies a simplified PCNN model and discrete form [14]: …”

Section: Pulse Coupled Neural Networkmentioning

confidence: 99%

“…MGA tools mainly contain discrete wavelet transform (DWT), contourlet transform, non-subsampled contourlet transform (NSCT) [12], and shearlet transform (ST) [14]. They are all successfully applied to image fusion.…”

Section: Introductionmentioning

confidence: 99%

“…The spatial domain algorithms mainly include principal component analysis [4,5], guided filtering based method [6] and so on. The transform domain algorithms are mainly based on multiresolution geometric analysis (MGA) tool domain and they can be divided into local energy function (such as image fusion algorithm based on wavelet [7,8], ripplet [9], contourlet [10,11], shearlet [13], surfacelet [15], trained dictionaries [16,17]) and artificial neural network (ANN) (such as contourlet with PCNN [12], shearlet with PCNN [14]) by different fused rules.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Focus Image Fusion Based on Nonsubsampled Shearlet Transform and Pulse Coupled Neural Network With Self-Similarity and Depth Information

2015

Int. J. Appl. Math. Mach. Learning

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Combined with the shared similarity among multiple source images and depth of field in a camera, a new image fusion algorithm based on nonsubsampled shearlet transform (NSST) domain and pulse coupled neural network (PCNN) is proposed. First, NSST is utilized for decomposition of the source images, the low frequency coefficients are fused by weight votes in the structure-driven regions by using shared similarity and depth of field (SSSID), and the high frequency coefficients are fused by fired map of PCNN which motivated by larger Liu Shuaiqi et al. / IJAMML 2:1 (2015) 47-65 48 sum-modified-Laplacian (SML) based on SSSID, finally, the fusion image is gained by inverse NSST. The algorithm can both preserve the information of the source images well and suppress pixel distortion due to nonlinear operations in transform domain.Experimental results demonstrate that the proposed method outperforms the state-of-theart transform domain and PCNN fusion methods.

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Section: Resultsmentioning

confidence: 99%

“…When the PCNN is used in digital image processing, each pixel corresponds to a PCNN neuron. This paper applies a simplified PCNN model and discrete form [14]: …”

Section: Pulse Coupled Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Focus Image Fusion Based on Nonsubsampled Shearlet Transform and Pulse Coupled Neural Network With Self-Similarity and Depth Information

2015

Int. J. Appl. Math. Mach. Learning

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“…However, the curvelet, bandelet, contourlet lack of shift-invariance and results in artifacts along the edges to some extend. To represent the edges more efficiently, Labate et al introduced a new multiscale analysis tool called shearlet that has all properties like other MGA tools as multiscale, localization, anisotropy and directionality [2]. The nonsubsampled shearlet transform (NSST) is realized by nonsubsampled Laplacian pyramid (NSLP) and several shearing filters.…”

Section: Introductionmentioning

confidence: 99%

Denoising Method Based on the Nonsubsampled Shearlet Transform

Ren¹,

Wei²,

He³

et al. 2015

Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference

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Abstract. In this paper, a new bivariate shrinkage denoising method is proposed to model statistics of shearlet coefficients of images. Using Bayesian estimation theory we derive from this model a simple non-linear shrinkage function for shearlet denoising, which generalizes the soft threshold approach. Experimental results show that the proposed method can remove Gaussian white noise while effectively preserving edges and texture information. At the same time, it can achieve a higher PSNR and mean structural similarity than other denoising method.

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Medical image fusion based on improved sum-modified-Laplacian

Liu

Zhao

Shi

2015

Int. J. Imaging Syst. Technol.

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Sum‐modified‐Laplacian (SML) plays an important role in medical image fusion. However, fused rules based on larger SML always lead to fusion image distortion in transform domain image fusion or image information loss in spatial domain image fusion. Combined with average filter and median filter, a new medical image fusion method based on improved SML (ISML) is proposed. First, a basic fused image is gained by ISML, which is used for evaluation of the selection map of medical images. Second, difference images can be obtained by subtracting average image of all sources of medical images. Finally, basic fused image can be refined by difference images. The algorithm can both preserve the information of the source images well and suppress pixel distortion. Experimental results demonstrate that the proposed method outperforms the state‐of‐the‐art medical image fusion methods. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 206–212, 2015

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Image fusion by pulse couple neural network with shearlet

Cited by 55 publications

References 15 publications

Multi-Focus Image Fusion Based on Nonsubsampled Shearlet Transform and Pulse Coupled Neural Network With Self-Similarity and Depth Information

Multi-Focus Image Fusion Based on Nonsubsampled Shearlet Transform and Pulse Coupled Neural Network With Self-Similarity and Depth Information

Denoising Method Based on the Nonsubsampled Shearlet Transform

Medical image fusion based on improved sum-modified-Laplacian

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