Genetic algorithms for optimal image enhancement
Pattern Recognition Letters
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Digital Image Processing
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Gray-scale image enhancement as an automatic process driven by evolution
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The principal objective of enhancement is to improve the contrast and detail an image so, that the result is more suitable than the original image for a specific application. The enhancement process is a non-linear optimization problem with several constraints. In this paper, an adaptive local enhancement algorithm based on Firefly Algorithm (FA) is proposed. FA represents a new approach for optimization. The FA is used to search the optimal parameters for the best enhancement. In the proposed method, the evaluation criterion is defined by edge numbers, edge intensity and the entropy. The proposed method is demonstrated and compared with Linear Contrast Stretching (LCS), Histogram Equalization (HE), Genetic Algorithm based image Enhancement (GAIE), and the Particle Swarm Optimization based image enhancement (PSOIE) methods. Experimental results presented that proposed technique offers better performance.