Edge Information Based Image Fusion Metrics Using Fractional Order Differentiation and Sigmoidal Functions

Sengupta, Animesh; Seal, Ayan; Panigrahy, Chinmaya; Krejcar, Ondřej; Yazidi, Anis

doi:10.1109/access.2020.2993607

Cited by 44 publications

(22 citation statements)

References 62 publications

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“…Eleven quantitative parameters such as EN, standard deviation (SD), mutual information (MI) [39], SF [13], edge index

false(Q_{normalAB / normalF} false)

[40], non‐linear correlation information EN

false(Q_{NICE} false)

[41], Peilla metric ( Q ) [42], Cvejic metric

false(Q_{C} false)

[43], Yang metric

false(Q_{Y} false)

[44], and Chen and Blum metric

false(Q_{CB} false)

[45] and

R_{Q}^{F / AB}

(with arctangent sigmoid function) [46] are used to evaluate the performance of the proposed CT–MR image fusion method. Higher EN indicates more information, but sometimes noise also influences EN values.…”

Section: Resultsmentioning

confidence: 99%

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

et al. 2020

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Diagnostic medical imaging plays an imperative role in clinical assessment and treatment of medical abnormalities. The fusion of multimodal medical images merges complementary information present in the multi-source images and provides a better interpretation with improved diagnostic accuracy. This paper presents a CT-MR neurological image fusion method using an optimised biologically inspired neural network in nonsubsampled shearlet (NSST) domain. NSST decomposed coefficients are utilised to activate the optimised neural model using particle swarm optimisation method and to generate the firing maps. Low and high-frequency NSST subbands get fused using max-rule based on firing maps. In the optimisation process, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image. To analyse the fusion performance, extensive experiments are conducted on the different CT-MR neurological image dataset. Objective performance is evaluated based on different metrics to highlight the clarity, contrast, correlation, visual quality, complementary information, salient information, and edge information present in the fused images. Experimental results show that the proposed method is able to provide better-fused images and outperforms other existing methods in both visual and quantitative assessments.

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“…Eleven quantitative parameters such as EN, standard deviation (SD), mutual information (MI) [39], SF [13], edge index

false(Q_{normalAB / normalF} false)

[40], non‐linear correlation information EN

false(Q_{NICE} false)

[41], Peilla metric ( Q ) [42], Cvejic metric

false(Q_{C} false)

[43], Yang metric

false(Q_{Y} false)

[44], and Chen and Blum metric

false(Q_{CB} false)

[45] and

R_{Q}^{F / AB}

Section: Resultsmentioning

confidence: 99%

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

et al. 2020

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“…An edge quality metric is often used in image fusion [41]. In this study, the ratio of the increased number of visible edges in the dehazed image to visible edges of the original image ve R , the mean visibility level vl M [42], and the noise variance v N are used to evaluate the dehazed images of hazy images without or with low-density noise.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

2021

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In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.

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“…Besides of the P-R curve metric, the three edge value assessment metrics [30,31] was also adopted to assess the edge similarity between the detection results of different detectors and ground truth. Detection error rate (DCR), detection common rate (DCR), and detection correct similarity (DCS) are separately defined in Eqs.…”

Section: Tp Recmentioning

confidence: 99%

“…Detection error rate (DCR), detection common rate (DCR), and detection correct similarity (DCS) are separately defined in Eqs. (30), (31), and(32).…”

Section: Tp Recmentioning

confidence: 99%

Color Edge Detection Using the Normalization Anisotropic Gaussian Kernel and Multichannel Fusion

et al. 2020

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Edge Information Based Image Fusion Metrics Using Fractional Order Differentiation and Sigmoidal Functions

Abstract: This work was supported in part by the project (Prediction of diseases through computer assisted diagnosis system using images captured by minimally-invasive and non-invasive modalities),

Cited by 44 publications

References 62 publications

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

NSST domain CT–MR neurological image fusion using optimised biologically inspired neural network

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

Color Edge Detection Using the Normalization Anisotropic Gaussian Kernel and Multichannel Fusion

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