A Novel Image Fusion Method Using Non-Subsampled Shearlet Transform

Xing, Xiao; Cao, Fu Cheng; Shang, Wei Wei; Liu, Fu

doi:10.4028/www.scientific.net/amm.668-669.1033

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Image fusion mingles absolutely registered images from numerous sources to fabricate a high quality fused image with spatial and spectral information. So many image fusion methods have been developed from the past to now such as: the IHS [3], DCT [7], DWT [4], DT CWT [6] and PCA [3] methods etc. These methods functions under spatial domain & have proved to be flourishing in computer vision, robotics, satellite and medical image fusion applications.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

Kumari¹,

Rawat²

2018

IJCA

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Nowadays lots of work is going to be done on the field of image fusion and also used in various application such as medical imaging and multi spectra sensor image fusing etc. For fusing the image various techniques has been proposed by different previous works such as wavelet transform, IHS (Intensity, Hue and Saturation) and Principal Component Analysis (PCA), based methods etc. In this paper literature of the image fusion is discussed with implementation using wavelet transform used for the specific application as in the image restoration field. Using Image fusion may improve the perceptual quality of the restored images. Usually image deblurring methods are used at the front end for restoration and then image fusion is used for improving the visual quality. Paper uses three de-blurring technique to blindly restoring the image then use statistical parameters for adopting the best fused images out of various hybrid fusion results. Performance is tested on images with distinct features.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

Kumari¹,

Rawat²

2018

IJCA

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Multimodal Medical Image Fusion based on the VGG19 Model in the NSCT Domain

Liu,

Wang,

Cheng

et al. 2024

RACSC

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Aim: To deal with the drawbacks of the traditional medical image fusion methods, such as the low preservation ability of the details, the loss of edge information, and the image distortion, as well as the huge need for the training data for deep learning, a new multi-modal medical image fusion method based on the VGG19 model and the non-subsampled contourlet transform (NSCT) is proposed, whose overall objective is to simultaneously make the full use of the advantages of the NSCT and the VGG19 model. Methodology: Firstly, the source images are decomposed into the high-pass and low-pass subbands by NSCT, respectively. Then, the weighted average fusion rule is implemented to produce the fused low-pass sub-band coefficients, while an extractor based on the pre-trained VGG19 model is constructed to obtain the fused high-pass subband coefficients. Result and Discussion: Finally, the fusion results are reconstructed by the inversion transform of the NSCT on the fused coefficients. To prove the effectiveness and the accuracy, experiments on three types of medical datasets are implemented. Conclusion: By comparing seven famous fusion methods, both of the subjective and objective evaluations demonstrate that the proposed method can effectively avoid the loss of detailed feature information, capture more medical information from the source images, and integrate them into the fused images.

show abstract

A Novel Image Fusion Method Using Non-Subsampled Shearlet Transform

Cited by 2 publications

References 6 publications

An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

Multimodal Medical Image Fusion based on the VGG19 Model in the NSCT Domain

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