Image denoising based on genetic algorithm
Digital images play an essential role in analysis tasks with applications in various knowledge domains, such as medicine, meteorology, geology, biology, among others. Such images can be degraded by noise during the process of acquisition, transmission, storage or compression. Although several image denoising methods have been proposed in the literature, noise suppression in images still remains a challenging problem for researchers since the process can cause the removal of relevant image features, such as edges and corners. This papers describes a novel image denoising method based on a genetic algorithm. A population of noisy images is evolved for several epochs applying tailor-made crossover and mutation operators. The population is reinitialized every time a convergence occurs, when only the best individual (image) is kept for the next epoch. Experimental results demonstrate that the proposed method is competitive in comparison with state-of-the-art approaches.