Summary of Video Denoising in Fluorescence Guided Surgery, by Trevor Seets and Andreas Velten
Video Denoising in Fluorescence Guided Surgery
by Trevor Seets, Andreas Velten
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel computational model is proposed to improve fluorescence-guided surgery (FGS) systems, which rely on high-quality video in real-time environments. The challenge lies in the presence of bias noise from laser leakage light (LLL), which can be orders of magnitude larger than the fluorescent signal. Conventional video denoising methods are ineffective due to their zero-mean assumption and non-causal processing. However, FGS often captures a co-located reference video, allowing for simulation of LLL and improved denoising processes. This work presents an accurate noise simulation pipeline and three baseline deep learning-based algorithms for FGS video denoising. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FGS is a new way to help surgeons during operations by showing them what’s inside the body. Right now, it can be tricky to get good pictures because of extra light that gets in the way. Scientists want to make sure the pictures are clear and easy to understand. They’ve found a way to make a special simulation that helps get rid of this extra light. This will help make FGS better and more useful for surgeons. |
Keywords
* Artificial intelligence * Deep learning
