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Hybrid Noise Reduction Filter Using the Gaining–Sharing Knowledge-Based Optimization and the Whale Optimization Algorithms

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Abstract

Noise reduction is one of the main challenges for researchers. Classical image de-noising methods reduce the image noise but sometimes lose image quality and information, such as blurring the edges of the image. To solve this challenge, this work proposes two optimal filters based on a generalized Cauchy (GC) distribution and two different nature-inspired algorithms that preserve image information while decreasing the noise. The generalized Cauchy filter and the bilateral filter are two parameter-based filters that significantly remove image noise. Parameter-based filters require proper parameter selection to remove the noise and maintain the edge details. To this end, two filters are considered. In the previous works, the parameters of the mask that was made with the GC function were optimized and the mask size was considered fixed. By studying different noisy images, we find that the selected mask size significantly impacts the designed filter performance. Therefore in this paper, a mask is designed using the GC function to formulate the first filter, and despite the optimization of the filter parameters, the selected mask size is also optimized using the peak signal-to-noise ratio (PSNR) as a fitness function. In most metaheuristic-based bilateral filters, only the domain and range parameters, which are based on Gaussian distribution, are optimized and the neighboring radius is a constant value. Filter results on different noisy images show that the neighboring radius has a major effect on the filter performance. Since the filter designed with the GC function causes significant noise removal, this function is effective, and on the other hand, it’s almost similar behavior with the Gaussian function has caused it to be combined with the bilateral filter to design the second filter in this paper. The kernel of the domain and range is considered to be the GC function instead of the Gaussian function. The domain and range parameters and the neighboring radius are optimized using the PSNR as a fitness function. With the help of optimization algorithms such as the whale optimization algorithm and the Gaining sharing knowledge-based optimization algorithm, bilateral filter; and GC filter parameters are optimized. Finally, the performance of the proposed filters is investigated on images corrupted by Gaussian and impulse noise. It is compared with other classical filters, the particle swarm optimization (PSO) based GC filter, and two PSO-based bilateral filters on various images. The experimental findings demonstrate that the suggested filters outperform the others.

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Data Availability

Data sets are in the form of images that are available at: https://www.kaggle.com/datasets/saeedehkamjoo/standard-test-images and the result data sets are generated during the current study and are available from the corresponding author.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Mathematics, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Mehrdad Nabahat & Farzin Modarres Khiyabani

  2. Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Nima Jafari Navmipour

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  1. Mehrdad Nabahat
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  2. Farzin Modarres Khiyabani
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Contributions

The authors confirm contribution to the paper as follows: study conception and design: MN, FMK, NJN; data collection: MN; analysis and interpretation of results: MN; draft manuscript preparation: MN. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Farzin Modarres Khiyabani.

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Nabahat, M., Khiyabani, F.M. & Navmipour, N.J. Hybrid Noise Reduction Filter Using the Gaining–Sharing Knowledge-Based Optimization and the Whale Optimization Algorithms. SN COMPUT. SCI. 5, 417 (2024). https://doi.org/10.1007/s42979-024-02674-y

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