2026-05-27T16:21:32Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/449022026-01-27T00:46:38Zcom_10630_2254col_10630_37959

Rodríguez Rodríguez, José Antonio Molina-Cabello, Miguel Ángel Benítez-Rochel, Rafaela López-Rubio, Ezequiel 2026-01-26T13:46:07Z 2021 9781728188089 1051-4651 https://hdl.handle.net/10630/44902 10.1109/ICPR48806.2021.9412110 Convolutional Neural Networks (CNNs) are widely used due to their high performance in many tasks related to computer vision. In particular, image classification is one of the fields where CNNs are employed with success. However, images can be heavily affected by several inconveniences such as noise or illumination. Therefore, image enhancement algorithms have been developed to improve the quality of the images. In this work, the impact that brightness and image contrast enhancement techniques have on the performance achieved by CNNs in classification tasks is analyzed. More specifically, several well known CNNs architectures such as Alexnet or Googlenet, and image contrast enhancement techniques such as Gamma Correction or Logarithm Transformation are studied. Different experiments have been carried out, and the obtained qualitative and quantitative results are reported eng open access Redes neuronales artificiales Visión por ordenador The effect of image enhancement algorithms on convolutional neural networks conference output