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

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

URL: http://arxiv.org/abs/2207.02396v1
Date: Wed, 6 Jul 2022 01:47:17 GMT
Title: A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models
Authors: Axel Garcia-Vega, Ricardo Espinosa, Gilberto Ochoa-Ruiz, Thomas Bazin, Luis Eduardo Falcon-Morales, Dominique Lamarque, Christian Daul
Abstract summary: This work introduces a new synthetically generated data-set generated by a generative adversarial techniques. It also explores both shallow based and deep learning-based image-enhancement methods in overexposed and underexposed lighting conditions.
Score: 0.9236074230806579
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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)

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