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Renyi entropy and atom search sine cosine algorithm for multi focus image fusion

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Abstract

This paper proposes a novel strategy, atom search sine cosine algorithm (ASSCA) for multi-focus image fusion. Here, the discrete wavelet transform (DWT) is adapted for transforming images into sub-bands. The fusion is carried out using a fusion rule based on weighting criteria that uses two attributes, Renyi entropy and the proposed ASSCA. Entropy discovers the entropy fusion factor considering the assessed entropy from the source image. Further, an optimization strategy, ASSCA is developed by integrating atom search optimization and sine cosine algorithm for precise selection of fusion factor. The output obtained from the fusion undergoes inverse discrete wavelet transform to obtain the resultant fused image. The proposed DWT + ASSCA + Renyi entropy outperformed other methods with maximal mutual information of 1.492, maximal peak signal-to-noise ratio of 40.625 dB, minimal root mean-squared error of 7.651, maximum correlation coefficient of 0.988, and minimum deviation index of 1.146.

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Abbreviations

JHE:

Joint holo-entropy

HWFusion:

Holoentropy whale fusion

UDWT:

Undecimated discrete wavelet transform

GA:

Genetic algorithm

SP-Whale:

Whale optimization algorithm + particle swarm optimization

coif1:

Coiflets 1 wavelet

db2:

Daubechies 2 wavelet

sym2:

Symlets 2 wavelet transform

PCNN:

Pulse coupled neural network

DCNN:

Deep convolutional neural network

CA:

Approximation coefficients array

CD:

Diagonal detailed coefficients array

CH:

Horizontal detail coefficients array

CV:

Vertical detail coefficients array

i.e.:

That is

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  1. CSE Department, Harcourt Butler Technical University, Kanpur, India

    Vineeta Singh & Vandana Dixit Kaushik

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  1. Vineeta Singh
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  2. Vandana Dixit Kaushik
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Correspondence to Vineeta Singh.

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Singh, V., Kaushik, V.D. Renyi entropy and atom search sine cosine algorithm for multi focus image fusion. SIViP 15, 903–912 (2021). https://doi.org/10.1007/s11760-020-01814-0

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