  Endoscopy is the most widely used imaging technique for the diagnosis of
cancerous lesions in hollow organs. However, endoscopic images are often
affected by illumination artefacts: image parts may be over- or underexposed
according to the light source pose and the tissue orientation. These artifacts
have a strong negative impact on the performance of computer vision or AI-based
diagnosis tools. Although endoscopic image enhancement methods are greatly
required, little effort has been devoted to over- and under-exposition
enhancement in real-time. This contribution presents an extension to the
objective function of LMSPEC, a method originally introduced to enhance images
from natural scenes. It is used here for the exposure correction in endoscopic
imaging and the preservation of structural information. To the best of our
knowledge, this contribution is the first one that addresses the enhancement of
endoscopic images using deep learning (DL) methods. Tested on the Endo4IE
dataset, the proposed implementation has yielded a significant improvement over
LMSPEC reaching a SSIM increase of 4.40% and 4.21% for over- and underexposed
images, respectively.
