PT Unknown
AU Patricia Suarez
   Dario Carpio
   Angel Sappa
TI Non-homogeneous Haze Removal Through a Multiple Attention Module Architecture
BT 16th International Symposium on Visual Computing
PY 2021
BP 178–190
VL 13018
AB This paper presents a novel attention based architecture to remove non-homogeneous haze. The proposed model is focused on obtaining the most representative characteristics of the image, at each learning cycle, by means of adaptive attention modules coupled with a residual learning convolutional network. The latter is based on the Res2Net model. The proposed architecture is trained with just a few set of images. Its performance is evaluated on a public benchmark—images from the non-homogeneous haze NTIRE 2021 challenge—and compared with state of the art approaches reaching the best result.
ER