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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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.208898-208909
Main Authors: Salazar-Colores, Sebastian, Jimenez, Hugo Moreno, Ortiz-Echeverri, Cesar Javier, Flores, Gerardo
Format: Article
Language:English
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
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3038437