Mejora eficiente de la luminosidad en imágenes del cerebro humano utilizando redes neuronales pulso-acopladas

Digital images are widely used in the medicine area but these could be degraded by several factors. The images affected in its luminosity generate a problem for its correct analysis, since they have a short dynamic range and low contrast. The need to obtain good quality images and the tendency to increase the resolution of images, require new techniques to solve this problem in less time, that's why there is a need to looking for paradigms that would can take advantage of parallel computing such as Pulsed-Coupled Artificial Neural Networks. In this work, two methods based on the Intersection Cortical Model are proposed and implemented to enhance the luminosity in medical human brain image. Experiments shown that the proposed models are highly competitive.