Comparative analysis of evolutionary algorithms for image enhancement
This work addresses image enhancement for applications like medical imaging or photography, but it is incremental as it applies existing evolutionary methods to a known problem without introducing new techniques.
The paper tackled automatic image enhancement as an optimization problem by comparing three evolutionary algorithms (Genetic Algorithm, Differential Evolution, Self Organizing Migration Algorithm) to find optimal parameters for an image enhancement transfer function, aiming to maximize contrast and detail visibility, with results compared among themselves and against histogram equalization.
Evolutionary algorithms are metaheuristic techniques that derive inspiration from the natural process of evolution. They can efficiently solve (generate acceptable quality of solution in reasonable time) complex optimization (NP-Hard) problems. In this paper, automatic image enhancement is considered as an optimization problem and three evolutionary algorithms (Genetic Algorithm, Differential Evolution and Self Organizing Migration Algorithm) are employed to search for an optimum solution. They are used to find an optimum parameter set for an image enhancement transfer function. The aim is to maximize a fitness criterion which is a measure of image contrast and the visibility of details in the enhanced image. The enhancement results obtained using all three evolutionary algorithms are compared amongst themselves and also with the output of histogram equalization method.