Surgery Scene Restoration for Robot Assisted Minimally Invasive Surgery
Shahnewaz Ali, Yaqub Jonmohamadi, Ross Crawford, Davide Fontanarosa,, Ajay K. Pandey

TL;DR
This paper introduces a novel online preprocessing framework that restores clean, sharp images from noisy, blurred, and raw observations in robot-assisted minimally invasive surgery, improving visualization and aiding surgical tasks.
Contribution
It proposes a new method for real-time image restoration in MIS, addressing noise, blur, and color correction challenges caused by hardware and environmental factors.
Findings
Effective noise and blur removal in surgical images
Improved visualization for surgical scene understanding
Compatible with real-time processing constraints
Abstract
Minimally invasive surgery (MIS) offers several advantages including minimum tissue injury and blood loss, and quick recovery time, however, it imposes some limitations on surgeons ability. Among others such as lack of tactile or haptic feedback, poor visualization of the surgical site is one of the most acknowledged factors that exhibits several surgical drawbacks including unintentional tissue damage. To the context of robot assisted surgery, lack of frame contextual details makes vision task challenging when it comes to tracking tissue and tools, segmenting scene, and estimating pose and depth. In MIS the acquired frames are compromised by different noises and get blurred caused by motions from different sources. Moreover, when underwater environment is considered for instance knee arthroscopy, mostly visible noises and blur effects are originated from the environment, poor control…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Vision and Imaging · Photoacoustic and Ultrasonic Imaging
