Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

Suresh, Shilpa; Lal, Shyam

doi:10.1016/j.asoc.2017.08.019

Cited by 61 publications

(33 citation statements)

References 37 publications

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“…This algorithm is used to find the optimal values for a, b, c and k. Retain the current best value and find the new solutions by moving the bats around the search space. After each iteration, fitness value is computed and the new frequency, position and velocity are updated using Equations (12)- (14) as:…”

Section: Bat Optimized Contrast Stretchingmentioning

confidence: 99%

“…Combining optimization algorithm with traditional contrast enhancement approaches is an upcoming area of research and is effective in improving the accuracy of the enhancement technique. A differential evolution based contrast and brightness enhancement of satellite images is presented in [14]. Another image enhancement technique involving DWT (Discrete Wavelet Transform) and SVD (Singular Value Decomposition) optimized using Artificial Bee Colony optimization is proposed in [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

et al. 2020

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The remote sensing images acquired from the satellites are low contrast images. The availability of low contrast images and failure of the traditional methods such as Histogram Equalization and Gamma correction in preserving the brightness levels in the image are the main issues in satellite image processing. This paper proposes an optimized contrast stretching using non-linear transformation for image enhancement. The non-linear transformation is influenced by the appropriate choice of control parameters for the sample images since manual tuning for individual images is tedious. A Bat algorithm based tuning is employed for the automated selection of control parameters in the transformation. The performance of the optimization algorithm is compared against other metaheuristic algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). It is noted that the bat algorithm based contrast enhancement outperforms the other optimization techniques in terms of metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy and CPU time (Central Processing Unit).

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Section: Bat Optimized Contrast Stretchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

et al. 2020

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“…Likewise, an entity in M CR is replaced by the weighted Lehmer mean of the successful CR values in the generation. The calculation of the weighted Lehmer mean is given in Equations (7)(8)(9).…”

Section: L-shadementioning

confidence: 99%

“…One reason for this is that unlike traditional optimization methods that utilize gradient information (that is not always available), DE only requires fitness values, thereby being applicable to a wider range of problems [6]. DE has shown its good performance in different real world scenarios like designing optimal harmonic filters [6], process modeling for greenhouses [7], as well as enhancing the contrast and brightness of satellite images [8]. However, DE also encounters two problems in some practical applications: slow convergence speed and stagnation in local optima [9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search

León¹,

Xiong²,

Molina³

et al. 2019

IJCIS

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Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process. A study of the important components of MFDEALS shows that there is a synergy between them. MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE CEC'2014 Conference. The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS) not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADE.

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“…Thus, this paper proposes another alternative optimization approach for selecting the optimized mode number and the balancing parameter values for the VMD method by using a differential evolution algorithm (DEA), or VMDEA. DEA is a simple, effective design and coding optimization method that has been used in many fields of study, such as satellite images, facial recognition, and induction motors . In addition, DEA has been proved more effective compared with GA and PSO in solving most optimization problems because of its simple and compact construction, rapid convergence, and fewer control parameters .…”

Section: Introductionmentioning

confidence: 99%

Intelligent wind turbine gearbox diagnosis using VMDEA and ELM

et al. 2019

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Wind turbine gearbox diagnosis is a vital tool for maintaining wind turbine operation and safety. The gearbox vibration signal is invariably complex and variable, and useful information and features are difficulty of extraction. Recently, a new and adaptive signal decomposition method, known as variational mode decomposition (VMD), has been proposed, which helps to improve the efficiency and effectiveness of extracting features from gearbox vibration signals. However, the performance of the VMD method mainly depends on its input parameters, especially the mode number and balancing parameter (also called the quadratic penalty term). Hence, this paper proposes a selection method for an optimized VMD parameter using differential evolution algorithm (DEA), also called VMDEA. Firstly, the VMDEA is used to select optimized VMD input parameters for each of the vibration signals. Following this, VMD decomposes each vibration signal into sets of subsignals using the selected optimized parameter. Multidomain features are extracted from VMD reconstructed signals and are passed on to the extreme learning machine (ELM) for fault classification. This study can thus provide a good solution for determining an optimized VMD parameter for decomposing vibration signals and can also provide a more efficient and effective diagnostic approach to wind turbine gearbox maintenance.

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Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

Cited by 61 publications

References 37 publications

Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search

Intelligent wind turbine gearbox diagnosis using VMDEA and ELM

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