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A fusion approach based on black hole algorithm and particle swarm optimization for image enhancement

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Abstract

The main objective of this paper is to present a new 2-stage hybrid optimization algorithm based scheme named PSO-BHA for image enhancement. A parameterized mapping function and a novel objective function are utilized in this paper to achieve the best-enhanced images. The suggested scheme combines the merits of particle swarm optimization (PSO) with the black hole algorithm (BHA) in two sequential stages to find the best parameters for the mapping function with the aid of the proposed objective function. The objective function uses contrast, edge, entropy, and universal quality index (UQI) for measuring contrast, and different improved information in the enhanced image. In the proposed scheme, PSO is applied first to adjust the tunable parameters of the mapping function and as a result, new pixel intensities are produced. Then, in the second stage, the obtained pixel intensities are again passed through the mapping function whose parameters are tuned by the use of the BHA. The suggested framework overcomes the limitations of the traditional histogram equalization (HE) based enhancement techniques in which excessive contrast enhancement and image information loss can occur. The suggested method is evaluated on several test images and compared with different state-of-the-art methods. The results indicate that the proposed framework provides superior performance to all existing methods in terms of various metrics. The proposed scheme also contributes to substantial feature enhancement and contrast boosting in the enhanced image, while retaining the natural feel of the original image.

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  1. Department of Software Engineering, Istanbul Aydin University, Istanbul, Turkey

    Elnaz Pashaei

  2. Department of Computer Engineering, Istanbul Gelisim University, Istanbul, Turkey

    Elham Pashaei

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  1. Elnaz Pashaei
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Pashaei, E., Pashaei, E. A fusion approach based on black hole algorithm and particle swarm optimization for image enhancement. Multimed Tools Appl 82, 297–325 (2023). https://doi.org/10.1007/s11042-022-13275-3

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