RT Conference Proceedings
T1 The effect of image enhancement algorithms on convolutional neural networks
A1 Rodríguez Rodríguez, José Antonio
A1 Molina-Cabello, Miguel Ángel
A1 Benítez-Rochel, Rafaela
A1 López-Rubio, Ezequiel
K1 Redes neuronales artificiales
K1 Visión por ordenador
AB 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
PB IEEE
SN 9781728188089
SN 1051-4651
YR 2021
FD 2021
LK https://hdl.handle.net/10630/44902
UL https://hdl.handle.net/10630/44902
LA eng
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 28 feb 2026