A novel multi-focus image fusion paradigm: A hybrid approach

Jindal, Muskan; Bajal, Eshan; Chakraborty, Alakananda; Singh, Prabhishek; Diwakar, Manoj; Kumar, Neeraj

doi:10.1016/j.matpr.2020.08.704

Cited by 18 publications

(3 citation statements)

References 15 publications

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“…A surgical approach to embedding electrodes of micro-size for deep mind activation has been used in severe cases when medical medicines have failed to treat motor symptoms. Evidence for treatments for nonmovement-associated signs and symptoms of Parkinson`s disease, together with sleep issues and emotional issues, remains being investigated [5] [18].…”

Section: Introductionmentioning

confidence: 99%

CNN –based Spiral drawing classifier to detect Early-Stage Parkinson’s Disease

Khare,

Singh

2023

J. Phys.: Conf. Ser.

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Parkinson’s disease is a neurological condition wherein one experiences a loss in control of motor movements. The disease usually infects gradually but worsens over time. Parkinson’s often starts with a tremor in one hand. Slow movements, stiffness in the body, and loss of balance are some of the major symptoms of the disease. Damaged nerve cells present in the brain cause drops in the levels of dopamine in the body. It is very difficult to detect Early-Stage Parkinson’s Disease. Researchers have now been able to detect Parkinson’s disease at an early stage (Perhaps at Stage 2 or Stage 3), but there have been no significant advancements in the detection of Parkinson’s at the initial stage. Analyzing the handwriting styles can be one of the initial symptoms of the disease. The dataset given by NIATS of the Federal University of Uberlandia was used in our research. In this study, we present a CNN model that can distinguish between spiral drawings made by patients with Parkinson’s disease and those made by people not suffering from Parkinson’s disease with high accuracy.

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Section: Introductionmentioning

confidence: 99%

CNN –based Spiral drawing classifier to detect Early-Stage Parkinson’s Disease

Khare,

Singh

2023

J. Phys.: Conf. Ser.

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“…This method outperforms other methods in terms of difference, entropy, and crossentropy. Jindal et al (2021) [12] combined principal component analysis with DWT and Stationary WT for image fusion, the method has higher objective evaluation indicators and better visualization than other methods. Hsu and Jian (2023) [13] combined neural network with DWT for multi-focus image fusion.…”

Section: Introductionmentioning

confidence: 99%

A novel method of greyscale image fusion based on discrete fractional wavelet transform combined with improved chaotic genetic algorithm

Wang,

Xie,

Yang

2023

Preprint

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Greyscale image fusion plays a crucial role in computer vision and image processing. This paper proposes a novel algorithm for greyscale image fusion that enhances clarity and information retention of the resulting image. Firstly, the source image is decomposed using discrete fractional wavelet transform (DFRWT) to acquire the low and high frequency components. Secondly, considering the different characteristics of low and high frequencies, the spatial frequency technique is chosen to address the coefficients in the low frequency domain. In the high-frequency part, an improved chaotic genetic algorithm (ICGA) is used to optimize the high-frequency fusion coefficients, which can adaptively adjust the weighting to avoid the blurring and distortion problems that are likely to occur when the DFRWT processes the details, as a consequence, the features of the original image are better retained, making the fused image more natural and the details clearer. Finally, the inverse DFRWT is performed to get the fusion results. Simulation experiments prove the effectiveness of the new algorithm.

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“…transform have been employed due to a reduced degradation of the spectral information contained in the original multi-spectral images, also leading to improvement in the spatial resolution [22]- [31]. According to wavelet-based studies, showing promising results for image fusion, [5], [26], [27], [32], [33] the wavelet transform contributes to obtaining better fused images due to the way in which coefficients are computed as part of the transformation process [34], [35].…”

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confidence: 99%

A comparative study for the assessment of Ikonos satellite image-fusion techniques

Daza

Parra

Upegui

2022

IJEECS

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IImage-fusion provide users with detailed information about the urban and rural environment, which is useful for applications such as urban planning and management when higher spatial resolution images are not available. There are different image fusion methods. This paper implements, evaluates, and compares six satellite image-fusion methods, namely wavelet 2D-M transform, gram schmidt, high-frequency modulation, high pass filter (HPF) transform, simple mean value, and PCA. An Ikonos image (Panchromatic-PAN and multispectral-MULTI) showing the northwest of Bogotá (Colombia) is used to generate six fused images: MULTIWavelet 2D-M, MULTIG-S, MULTIMHF, MULTIHPF, MULTISMV, and MULTIPCA. In order to assess the efficiency of the six image-fusion methods, the resulting images were evaluated in terms of both spatial quality and spectral quality. To this end, four metrics were applied, namely the correlation index, erreur relative globale adimensionnelle de synthese (ERGAS), relative average spectral error (RASE) and the Q index. The best results were obtained for the MULTISMV image, which exhibited spectral correlation higher than 0.85, a Q index of 0.84, and the highest scores in spectral assessment according to ERGAS and RASE, 4.36% and 17.39% respectively.

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A novel multi-focus image fusion paradigm: A hybrid approach

Cited by 18 publications

References 15 publications

CNN –based Spiral drawing classifier to detect Early-Stage Parkinson’s Disease

CNN –based Spiral drawing classifier to detect Early-Stage Parkinson’s Disease

A novel method of greyscale image fusion based on discrete fractional wavelet transform combined with improved chaotic genetic algorithm

A comparative study for the assessment of Ikonos satellite image-fusion techniques

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