A novel Approach of Retinal Image Enhancement using PSO System and Measure of Fuzziness

Ghosh, Swarup Kr; Biswas, Biswajit; Ghosh, Anupam

doi:10.1016/j.procs.2020.03.446

Cited by 17 publications

(9 citation statements)

References 22 publications

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“…PSO has been successfully applied to solve many image processing optimization problems in diverse areas such as image segmentation [230][231][232], image enhancement [233], image compression [234] and image watermarking [235].…”

Section: Image Processingmentioning

confidence: 99%

“…In the area of image enhancement, PSO is utilized in [233] to address the inaccurate nature of retinal images. The effectiveness of the proposed approach is validated on two well-known image datasets and results have shown that PSO can significantly enhance the quality of retinal images compared with GA, ACO, and ABC.…”

Section: Image Processingmentioning

confidence: 99%

“…Exponential center symmetric inertia weight based PSO [233], Multi-objective PSO [254] PSO with blind deconvolution technique [255] Modified PSO [256], Hybridized PSO with mutation operator [257], adaptive PSO with fuzzy logic [258], PSO-GA [259] Enhanced PSO [260], improved PSO [230] PSO with Haar wavelet transform [234] Multi-dimensional PSO [235], guided dynamic PSO [261] Electrical Power Systems Economic dispatch Improved quantum PSO [237], evolutionary PSO [238], improved random drift PSO [239], hybrid PSO and gravitational search algorithm [130], improved PSO with a biogeography learning strategy [264], cultural quantum-behaved PSO [265], modified PSO with time varying acceleration coefficients (MPSO-TVAC) [134], hybrid double-weighted PSO [266] Optimal Power flow Modified PSO [240], hybrid PSO-dragonfly algorithm [241], hybrid PSO-artificial physics optimization [242], hybrid PSO-pattern search algorithm [267], enhanced PSO with stochastic weight and chaotic mutation [268].…”

Section: Image Watermarkingmentioning

confidence: 99%

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Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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Section: Image Processingmentioning

confidence: 99%

Section: Image Processingmentioning

confidence: 99%

Section: Image Watermarkingmentioning

confidence: 99%

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Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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“…A novel approach, PSO System and Measure of Fuzziness [14], enhanced retinal fundus images by fuzzy framework applying particle swarm optimization (PSO) to define the fitness function of the fuzzy system. The system divided an input image into two fuzzy sub-regions determined by a type-2 fuzzy system, then applied the S-shape function to the sub-regions.…”

Section: Introductionmentioning

confidence: 99%

Enhance Contrast and Balance Color of Retinal Image

2021

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This paper proposes a simple and effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance for symmetric information in biomedicine. The aim of the study is for reliable diagnosis of AMD (age-related macular degeneration) screening. The method consists of a few simple steps. Firstly, local image contrast is refined with the CLAHE (Contrast Limited Adaptive Histogram Equalization) technique by operating CIE L*a*b* color space. Then, the contrast-enhanced image is stretched and rescaled by a histogram scaling equation to adjust the overall brightness offsets of the image and standardize it to Hubbard’s retinal image brightness range. The proposed method was assessed with retinal images from the DiaretDB0 and STARE datasets. The findings in the experimentation section indicate that the proposed method results in delightful color naturalness along with a standard color of retinal lesions.

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“…These methods are applicable to single image target and histogram curve fluctuation, they can artificially control the degree of image enhancement. Ghosh et al [17] enhanced the images combining with optimization theory and functional analysis. These methods do not rely on the original histogram distribution and just solve the target value according to the set conditions, but their mathematical operation are complex and require a clear understanding of the target histogram distribution [18].…”

Section: Introductionmentioning

confidence: 99%

A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement

Shen¹,

Jiang²,

Teng³

2018

ICST Transactions on Scalable Information Systems

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Traditional image enhancement methods have the problems of low contrast and fuzzy details. Therefore, we propose a novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. Firstly, multi-scale convolution is used to preprocess the image. Then, we improve the traditional Laplace edge detection operator and combine it with Gauss filter. The Gaussian filter is used to smooth the image and suppress the noise, and the edge detection is processed based on the Laplace gradient edge detector. The detail image extracted by Gauss-Laplace operator and the image with brightness enhancement are linearly weighted fused to reconstruct the image with clear detail edge and strong contrast. Experiments are carried out with detailed images in different scenes. It is compared with traditional methods to verify the effectiveness of the proposed method.

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A novel Approach of Retinal Image Enhancement using PSO System and Measure of Fuzziness

Cited by 17 publications

References 22 publications

Particle Swarm Optimization: A Comprehensive Survey

Particle Swarm Optimization: A Comprehensive Survey

Enhance Contrast and Balance Color of Retinal Image

A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement

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