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Desmoking Laparoscopy Surgery Images Using an Image-to-Image Translation Guided by an Embedded Dark Channel
In this paper, a method to remove the smoke effects in laparoscopic images is presented. The proposed method is based on an image-to-image conditional generative adversarial network endowed with a dark channel's embedded guide mask. The obtained experimental results were evaluated and quantitat...
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Published in: | IEEE access 2020, Vol.8, p.208898-208909 |
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creator | Salazar-Colores, Sebastian Jimenez, Hugo Moreno Ortiz-Echeverri, Cesar Javier Flores, Gerardo |
description | In this paper, a method to remove the smoke effects in laparoscopic images is presented. The proposed method is based on an image-to-image conditional generative adversarial network endowed with a dark channel's embedded guide mask. The obtained experimental results were evaluated and quantitatively compared with desmoking state-of-art methods using the Peak Signal-to-Noise Ratio (PSNR) metrics and Structural Similarity (SSIM) index. Those results throw an improved performance compared with relevant works. Also, the processing time required by our method is 92 frames per second; a processing time that sets the foundation for a possible real-time implementation in a more modest embedded system. |
doi_str_mv | 10.1109/ACCESS.2020.3038437 |
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subjects | Channel estimation conditional generative adversarial network dark channel Embedded systems Frames per second Generative adversarial networks Generators Image color analysis image smoke removal Laparoscopes Laparoscopy Minimally invasive surgery Neural networks Signal to noise ratio |
title | Desmoking Laparoscopy Surgery Images Using an Image-to-Image Translation Guided by an Embedded Dark Channel |
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