A context sensitive multilevel thresholding using swarm based algorithms

Pare, S.; Kumar, Arun; Bajaj, Varun; Singh, Gagandeep

doi:10.1109/jas.2017.7510697

Cited by 30 publications

(9 citation statements)

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“…First, the sky and nonsky regions are segmented by OTSU image segmentation method [31][32][33][34], and the average intensity value of the sky region of the original image after segmentation is taken as the estimated value of A. Since the depth of field of the sky region can be regarded as infinite, that is d(x) ⟶ + ∞, we can know t(x) ≈ 0, taking it into equation (3), It can be seen that:…”

Section: Improvement Of Atmospheric Lightmentioning

confidence: 99%

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

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According to the characteristics of foggy images, such as high noise, low resolution, and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed. First, the new algorithm refines the transmittance and optimizes the atmospheric light value and converts the restored image to HSV space. Second, the brightness V component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The new method solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion in JPEG, BMP, GIF, PNG, PSD, and TIFF formats. By comparing with other image enhancement algorithms, the improved algorithm performs better than DCP, SSR, MSR, MSRCR, and CLAHE algorithm in PSNR, SSIM, and IE evaluation indexes. It has a good effect on preserving the edge information and has good adaptability and stability for heavily polluted haze image enhancement.

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Section: Improvement Of Atmospheric Lightmentioning

confidence: 99%

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

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“…The PSO algorithm is one of the most particularly popular, which shows good performance in terms of global search ability and convergence speed [21]. It has been successfully used to solve problems in various areas such as healthcare [22], finance [23]- [24], telecommunications [25]- [26], energy [27]- [28], image thresholding [29] and others [30]- [31]. Despite its good results, the PSO method encounter a premature convergence when solving a complex optimization problem, this is due to the improper balance between the local and global searches.…”

Section: Research and Application Of Transient Electromagnetic Methods Inversion Technique Based On Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Research and Application of the Transient Electromagnetic Method Inversion Technique Based on Particle Swarm Optimization Algorithm

Zhang³

2020

IEEE Access

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The transient electromagnetic (TEM) method is widely used in shallow surface engineering geological surveys due to its advantages such as light weight, high efficiency, and strong resolution. However, interpretation and inversion of TEM data is a complicated process. The traditional algorithm of TEM inversion employs the "smoke ring" fast imaging method, which can only reflect the approximate morphology of the stratigraphic model, and the inversion accuracy is low. Therefore, this method cannot meet the requirements of high-precision inversion. In this paper, we present the particle swarm optimization (PSO) algorithm for TEM inversion. First of all, the response of the rectangular loop source TEM based on electric dipole integration was calculated and compared with the analytical solution results of the rectangular loop source and the accuracy of the algorithm was verified. Then, we introduced the solution process of the particle swarm optimization algorithm and analyzed the influence of particle swarm optimization algorithm parameter selection on the accuracy of the inversion result and the convergence speed. Next, a layered medium model was established. The particle swarm optimization algorithm and "smoke ring" fast imaging method were used to perform inversion calculation. The results show that the PSO algorithm has the advantages of high efficiency and accuracy. Finally, we examined the effectiveness of the particle swarm optimization algorithm for TEM data processing by inverting survey data from an Air-raid shelter on the campus of Chongqing University in China and comparing the results with those from the "smoke ring" fast imaging. The research works in this paper provide new methods and techniques for TEM data processing.INDEX TERMS Transient electromagnetic method; particle swarm optimization algorithm; fast imaging; inversion;

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“…The hybrid approach performs better than ABC, SCA, SSO, SSA algorithms in a multilevel thresholding problem using Kapur's entropy as the objective function. Multilevel thresholding of Satellite images proposed by Pare et al [10] using optimization techniques WDO, BFO, FA, ABC, DE and PSO by taking energy curves of image as an input. The objective functions based on Kapur's entropy, Tsallis entropy and Otsu's method are used.…”

Section: Related Workmentioning

confidence: 99%

A New Harris Hawks-Cuckoo Search Optimizer for Multilevel Thresholding of Thermogram Images

Samantaray¹,

Hembram²,

Panda³

2020

RIA

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The exploitation capability of the Harris Hawks optimization (HHO) is limited. This problem is solved here by incorporating features of Cuckoo search (CS). This paper proposes a new algorithm called Harris hawks-cuckoo search (HHO-CS) algorithm. The algorithm is validated using 23 Benchmark functions. A statistical analysis is carried out. Convergence of the proposed algorithm is studied. Nonetheless, converting color breast thermogram images into grayscale for segmentation is not effective. To overcome the problem, we suggest an RGB colour component based multilevel thresholding method for breast cancer thermogram image analysis. Here, 8 different images from the Database for Research Mastology with Infrared images are considered for the experiments. Both 1D Otsu’s between-class variance and Kapur's entropy are considered for a fair comparison. Our proposal is evaluated using the performance metrics – Peak Signal to Noise Ratio (PSNR), Feature Similarity Index (FSIM), Structure Similarity Index (SSIM). The suggested method outperforms the grayscale based multilevel thresholding method proposed earlier. Moreover, our method using 1D Otsu’s fitness functions performs better than Kapur’s entropy based approach. The proposal would be useful for analysis of infrared images. Finally, the proposed HHO-CS algorithm may be useful for function optimization to solve real world engineering problems.

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A context sensitive multilevel thresholding using swarm based algorithms

Cited by 30 publications

References 0 publications

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Research and Application of the Transient Electromagnetic Method Inversion Technique Based on Particle Swarm Optimization Algorithm

A New Harris Hawks-Cuckoo Search Optimizer for Multilevel Thresholding of Thermogram Images

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