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

A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models