Application of fractional-order differentiation in multispectral image fusion

Azarang, Arian; Ghassemian, Hassan

doi:10.1080/2150704x.2017.1395963

Cited by 37 publications

(16 citation statements)

References 25 publications

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“…A fractional-order differential [38] is a generalization of the traditional integer order differential. When the order of the fractional differential is between [0, 1], the high-frequency signal portion corresponds to the edge and noise component of the image.…”

Section: Fractional Differentialmentioning

confidence: 99%

Multispectral Image Fusion Using Fractional-Order Differential and Guided Filtering

Yuan

Fan

2019

IEEE Photonics J.

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Remote sensing satellites can provide a large number of multispectral images. However, due to the limitations of optical sensors embedded in satellites, the spatial resolution of multispectral images is relatively low. Pansharpening aims to combine high-resolution panchromatic and multi-spectral images to generate high-resolution multi-spectral images. In this paper, we propose a pansharpening method based on a component substitution framework. We use fractional-order differential operators and guided filter to balance the spectral distortion and spatial information loss that occur when remote sensing image fusion. Fractional-order differentiation can better define the detailed map, and the guided filter can enhance the spectral information of the detailed map. Experiments show that the proposed method in this paper can better combine the spectral information and spatial information, as well as obtain satisfactory results in both subjective visual perception and objective object evaluation.

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Section: Fractional Differentialmentioning

confidence: 99%

Multispectral Image Fusion Using Fractional-Order Differential and Guided Filtering

Yuan

Fan

2019

IEEE Photonics J.

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“…Recently, among image fusion methods, the emergence of algorithms based on fractional differential calculus can be observed [ 15 , 16 , 17 , 18 , 19 ]. They are applied in such problems as fingerprint detection [ 20 ], exposing of essential elements in medical images [ 21 ], brain photo analysis [ 22 ], elimination of noise in images (improved image quality) [ 23 ], or contrast enhancement [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present an analysis of the method that uses a fractional order differential to multispectral images fusion [ 15 ]. We analyze the correct basic definitions of the fractional order derivative.…”

Section: Introductionmentioning

confidence: 99%

Fractional Derivatives Application to Image Fusion Problems

Motłoch

Sarwas

Dzieliński

2022

Sensors

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In this paper, an analysis of the method that uses a fractional order calculus to multispectral images fusion is presented. We analyze some correct basic definitions of the fractional order derivatives that are used in the image processing context. Several methods of determining fractional derivatives of digital images are tested, and the influence of fractional order change on the quality of fusion is presented. Results achieved are compared with the results obtained for methods where the integer order derivatives were used.

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“…An adaptive image enhancement operator based on fractionaldifferential and image gradient feature was proposed by Lan [15]. Arian Azarang inferred different structure mask to image fusion [16]. An adaptive fractional-order integral filter was presented for echocardiographic image denoising [17].…”

Section: Introductionmentioning

confidence: 99%

Image Denoising of Adaptive Fractional Operator Based on Atangana–Baleanu Derivatives

Lin

Wang

et al. 2021

Journal of Mathematics

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A fractional integral operator can preserve an image edge and texture details as a denoising filter. Recently, a newly defined fractional-order integral, Atangana–Baleanu derivatives (ABC), has been used successfully in image denoising. However, determining the appropriate order requires numerous experiments, and different image regions using the same order may cause too much smoothing or insufficient denoising. Thus, we propose an adaptive fractional integral operator based on the Atangana–Baleanu derivatives. Edge intensity, global entropy, local entropy, and local variance weights are used to construct an adaptive order function that can adapt to changes in different regions of an image. Then, we use the adaptive order function to improve the masks based on the Grumwald–Letnikov scheme (GL_ABC) and Toufik–Atangana scheme (TA_ABC), namely, Ada_GL_ABC and Ada_TA_ABC, respectively. Finally, multiple evaluation indicators are used to assess the proposed masks. The experimental results demonstrate that the proposed adaptive operator can better preserve texture details when denoising than other similar operators. Furthermore, the image processed by the Ada_TA_ABC operator has less noise and more detail, which means the proposed adaptive function has universality.

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Application of fractional-order differentiation in multispectral image fusion

Cited by 37 publications

References 25 publications

Multispectral Image Fusion Using Fractional-Order Differential and Guided Filtering

Multispectral Image Fusion Using Fractional-Order Differential and Guided Filtering

Fractional Derivatives Application to Image Fusion Problems

Image Denoising of Adaptive Fractional Operator Based on Atangana–Baleanu Derivatives

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