Research on Threshold Segmentation Method of Two-Dimensional Otsu Image Based on Improved Sparrow Search Algorithm

Du, Yun; Yuan, Hua; Jia, Kejin; Li, F.

doi:10.1109/access.2023.3293191

Cited by 11 publications

(8 citation statements)

References 35 publications

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“…Otsu's thresholding algorithm is a widely used threshold segmentation method in image processing that can automatically determine the optimal threshold of the image. Based on the maximum between-class variance criterion, this method determines the optimal threshold by minimizing the between-class variance between background and foreground [10][11][12]. This method is robust, adaptive, and can effectively segment the image, so it is widely used in many fields.…”

Section: The Otsu's Thresholding Methodsmentioning

confidence: 99%

“…Where div is the divergence operator, r is the gradient operator, t represents the iteration time, and ρ is the diffusion coefficient function of the image at time t, which is used to control the anisotropic characteristics of the diffusion process. Therefore, the finite difference method [19,20]] is used as the discrete differential operator to solve Eq (10), and the discrete expression of image enhancement is obtained as shown in Eq (11):…”

Section: Plos Onementioning

confidence: 99%

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Low illumination fog noise image denoising method based on ACE-GPM

Li,

Zhou,

Wang

2024

PLoS ONE

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The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image’s foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality.

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Section: The Otsu's Thresholding Methodsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Low illumination fog noise image denoising method based on ACE-GPM

Li,

Zhou,

Wang

2024

PLoS ONE

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“…Geng et al [19] introduced chaotic back-propagation learning and dynamic weights to prevent the SSA from being entrapped in local optima. Wu et al [20] combined Levy flights and nonlinear inertia weight to present an improved SSA. Xiong et al [21] introduced a fractional-order chaotic improved SSA, demonstrating higher convergence accuracy than the SSA.…”

Section: Introductionmentioning

confidence: 99%

An Improved Sparrow Search Algorithm for the Optimization of Variational Modal Decomposition Parameters

Du,

Wang,

Qian

et al. 2024

Applied Sciences

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Variational modal decomposition (VMD) is frequently employed for both signal decomposition and extracting features; however, the decomposition outcome is influenced by the quantity of intrinsic modal functions (IMFs) and the specific parameter values of penalty factors. To tackle this issue, we propose an algorithm based on the Halton sequence and the Laplace crossover operator for the sparrow search algorithm–VMD (HLSSA-VMD) to fine-tune the parameters of VMD. First, the population initialization by the Halton sequence yields higher-quality initial solutions, which effectively addresses the issue of the algorithm’s sluggish convergence due to overlapping and the lack of diversity of the initial solutions. Second, the introduction of the Laplace crossover operator (LX) to perturb the position of the best individual in each iteration helps to prevent the algorithm from becoming ensnared in a local optimum and improves the convergence speed of the algorithm. Finally, from the simulation of 17 benchmark test functions, we found that the HLSSA exhibited superior convergence accuracy and accelerated convergence pace, as well as better robustness than the particle swarm optimization (PSO) algorithm, the whale optimization algorithm (WOA), the multiverse optimization (MVO) algorithm, and the traditional sparrow search algorithm (SSA). In addition, we verified the effectiveness of the HLSSA-VMD algorithm on two simulated signals and compared it with PSO-VMD, WOA-VMD, MVO-VMD, and SSA-VMD. The experimental findings indicate that the HLSSA-VMD obtains better parameters, confirming the superiority of the algorithm.

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“…The most commonly used method is still the maximum entropy method. Although the segmentation effect of dark areas is slightly better, it still cannot segment the defect area and the background area accurately [8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Segmentation Algorithm for Subtle Defect Images on the Surface of Magnetic Ring Using 2D-Gabor Filter Bank

Li,

Ge,

Zhang

et al. 2024

Sensors

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In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and the background. Secondly, this study analyzed the grayscale statistical characteristics of the processed image; the segmentation threshold was constructed according to the gray statistical law of the image; and the adaptive segmentation of subtle defect images on the surface of small magnetic rings was realized. Finally, a classifier based on a BP neural network is designed to classify the scar images and crack images determined by different threshold segmentation methods. The classification accuracies of the iterative method, the OTSU method, the maximum entropy method, and the adaptive threshold segmentation method are, respectively, 85%, 87.5%, 95%, and 97.5%. The adaptive threshold segmentation method proposed in this paper has the highest classification accuracy. Through verification and comparison, the proposed algorithm can segment defects quickly and accurately and suppress noise interference effectively. It is better than other traditional image threshold segmentation methods, validated by both segmentation accuracy and computational efficiency. At the same time, the real-time performance of our algorithm was performed on the advanced SEED-DVS8168 platform.

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Research on Threshold Segmentation Method of Two-Dimensional Otsu Image Based on Improved Sparrow Search Algorithm

Cited by 11 publications

References 35 publications

Low illumination fog noise image denoising method based on ACE-GPM

Low illumination fog noise image denoising method based on ACE-GPM

An Improved Sparrow Search Algorithm for the Optimization of Variational Modal Decomposition Parameters

Adaptive Segmentation Algorithm for Subtle Defect Images on the Surface of Magnetic Ring Using 2D-Gabor Filter Bank

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