2026-05-31T10:40:07Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/449022026-01-27T00:46:38Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Rodríguez Rodríguez, José Antonio
author
Molina-Cabello, Miguel Ángel
author
Benítez-Rochel, Rafaela
author
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
Redes neuronales artificiales
Visión por ordenador
The effect of image enhancement algorithms on convolutional neural networks
conference output
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
URL
https://riuma.uma.es/bitstreams/d8a37a83-5585-4ebd-8c2c-e1775aa2ff4c/download
File
MD5
578a67a1ba77b353453a53e9f418336a
5138157
application/pdf
ICPR____The_effect_of_image_enhancement_algorithms_on_convolutional_neural_networks.pdf