High Dynamic Range Imaging via Visual Attention Modules
Ali Reza Omrani Davide Moroni
Thanks to High Dynamic Range (HDR) imaging methods, the scope of photography
has seen profound changes recently. To be more specific, such methods try to
reconstruct the lost luminosity of the real world caused by the limitation of
regular cameras from the Low Dynamic Range (LDR) images. Additionally, although
the State-Of-The-Art methods in this topic perform well, they mainly
concentrate on combining different exposures and have less attention to
extracting the informative parts of the images. Thus, this paper aims to
introduce a new model capable of incorporating information from the most
visible areas of each image extracted by a visual attention module (VAM), which
is a result of a segmentation strategy. In particular, the model, based on a
deep learning architecture, utilizes the extracted areas to produce the final
HDR image. The results demonstrate that our method outperformed most of the
State-Of-The-Art algorithms.