  Endoscopy is the most widely used medical technique for cancer and polyp
detection inside hollow organs. However, images acquired by an endoscope are
frequently affected by illumination artefacts due to the enlightenment source
orientation. There exist two major issues when the endoscope's light source
pose suddenly changes: overexposed and underexposed tissue areas are produced.
These two scenarios can result in misdiagnosis due to the lack of information
in the affected zones or hamper the performance of various computer vision
methods (e.g., SLAM, structure from motion, optical flow) used during the non
invasive examination. The aim of this work is two-fold: i) to introduce a new
synthetically generated data-set generated by a generative adversarial
techniques and ii) and to explore both shallow based and deep learning-based
image-enhancement methods in overexposed and underexposed lighting conditions.
Best quantitative results (i.e., metric based results), were obtained by the
deep-learnnig-based LMSPEC method,besides a running time around 7.6 fps)
