Natural Image Enhancement Using a Biogeography Based Optimization Enhanced with Blended Migration Operator

Jasper, J.; Shaheema, S Berlin; Shiny, S Berlin

doi:10.1155/2014/232796

Cited by 7 publications

(4 citation statements)

References 46 publications

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“…There are different methods and criteria for evaluating the quality of enhanced images. In this paper, the evaluation function F(Z) of image enhancement quality in [42] is adopted. F(Z) is a function defined by factors such as the performance measures entropy value, sum of the edge intensities, and edge pixels.…”

Section: B Image Enhancement Evaluation Criteriamentioning

confidence: 99%

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Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

Zhang

Chen

et al. 2019

IEEE Access

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Lévy flight Shuffle Frog Leaping Algorithm (LSFLA) is a SFLA variant and enhances the performance of SFLA largely, however, it still has some defects, such as poor convergence and low efficiency. So an improved LSFLA, namely, LSFLA based on Differential perturbation and Quasi-Newton search (DQLSFLA), is proposed in this paper. Firstly, the way of updating only one solution which is the worst one at every sub-iteration in LSFLA is replaced with an all-solution updating way in the subgroup to improve the probability of obtaining the best solution, to omit one sorting step at every sub-iteration and the subiteration number parameter setting, to reduce the computational complexity and to enhance the optimization efficiency. Secondly, a random differential perturbation approach and an improved Lévy flight updating one are created and used in the subgroup updating to form DLSFLA, that is, the first half frogs in the subgroup use the random differential perturbation updating approach to improve the global search ability, and the last half adopt the improved Lévy flight updating method to keep the advantage of the Lévy flight updating method and overcome its defects. Finally, quasi-Newton local search is integrated into DLSFLA near the end of the algorithm to improve the convergence speed. Experimental results on the complex functions from CEC-2014 show that DQLSFLA's convergence quality and optimization efficiency are much better than LSFLA's and that compared with quite a few other state-of-the-art algorithms, DQLSFLA has better performance. The results on medical image enhancement and Quadratic Assignment Problem (QAP) also show that DQLSFLA can solve the real word optimization problems better than LSFLA can. INDEX TERMS Intelligent optimization algorithm, shuffled frog leaping algorithm (SFLA), Lévy flight, quasi-Newton search, image enhancement, quadratic assignment problem.

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Section: B Image Enhancement Evaluation Criteriamentioning

confidence: 99%

“…The enhanced quality of the image is evaluated by comparing F(Z) values. The larger the value of F(Z) is, the richer the image details are, and the better the image enhancement is [42]. The description is below.…”

Section: B Image Enhancement Evaluation Criteriamentioning

confidence: 99%

Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

Zhang

Chen

et al. 2019

IEEE Access

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“…These advantages make BBO solve more effectively complex optimization problem. It has been applied to image and video processing, such as image classification [24], image matching [25], image segmentation [26], image enhancement [27], and motion estimation for video coding [28]. In this paper, the proposed algorithm utilizes the biogeography-based optimization technique to find the best block size.…”

Section: Introductionmentioning

confidence: 99%

Multifocus Image Fusion Using Biogeography-Based Optimization

Zhang

Fei

Peng

et al. 2015

Mathematical Problems in Engineering

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For multifocus image fusion in spatial domain, sharper blocks from different source images are selected to fuse a new image. Block size significantly affects the fusion results and a fixed block size is not applicable in various multifocus images. In this paper, a novel multifocus image fusion algorithm using biogeography-based optimization is proposed to obtain the optimal block size. The sharper blocks of each source image are first selected by sum modified Laplacian and morphological filter to contain an initial fused image. Then, the proposed algorithm uses the migration and mutation operation of biogeography-based optimization to search the optimal block size according to the fitness function in respect of spatial frequency. The chaotic search is adopted during iteration to improve optimization precision. The final fused image is constructed based on the optimal block size. Experimental results demonstrate that the proposed algorithm has good quantitative and visual evaluations.

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“…The image enhancement algorithm can be applied to improve the adaptability of human visual system [1][2][3][4], especially in the process of object recognition. Due to the fuzzy factors, such as the weather, light, color, shape and surface characteristics and so on, they make the image be fuzzier, the image edge contrast also declines seriously.…”

Section: Introductionmentioning

confidence: 99%

Improved Fast Image Enhancement Algorithm Based on Fuzzy Set Theory

Jia

Liu

Fan

et al. 2014

2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics

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In this paper, we present an improved fast image enhancement algorithm based fuzzy set theory. The gray level image is mapped into fuzzy domain using a novel linear membership function. Then, the generalized fuzzy enhancement operator is applied for the image enhancement operation. The enhancement result is calculated by using the method of linear mapping. The comparative experiments show the superiority in the performance efficiency and visual results, and the operation time of the algorithm is only 0.047887s.

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Natural Image Enhancement Using a Biogeography Based Optimization Enhanced with Blended Migration Operator

Cited by 7 publications

References 46 publications

Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

Multifocus Image Fusion Using Biogeography-Based Optimization

Improved Fast Image Enhancement Algorithm Based on Fuzzy Set Theory

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