A hybrid genetic algorithm for image denoising
Jônatas Lopes de Paiva, Claudio Fabiano Motta Toledo, Hélio Pedrini · 2015
This paper presents a novel Hybrid Genetic Algorithm (HGA) for image denoising, whose main purpose is to restore images while preserving relevant information, for instance, texture and edges. The proposed method combines operators available in existing evolutionary methods, such as crossover, mutation and population reinitialization with some state-of-the-art image denoising methods. Experiments are conducted on a set of noise contaminated images commonly used by the scientific community as benchmark, where different levels of noise are applied to the images. The results achieved by the proposed method are compared against image denoising methods. The HGA performance demonstrated to be very effective and competitive, outperforming other approaches in several levels of noise.