Video Denoising in Fluorescence Guided Surgery

TL;DR

This paper introduces a novel noise simulation pipeline and three deep learning algorithms to improve real-time video denoising in fluorescence guided surgery, addressing unique noise challenges like laser leakage light.

Contribution

It presents the first accurate noise simulation including laser leakage light and three baseline deep learning methods tailored for fluorescence guided surgery video denoising.

Findings

Proposed a realistic noise simulation pipeline for FGS.

Developed three baseline deep learning denoising algorithms.

Enhanced video quality in low-fluorescence conditions.

Abstract

Fluorescence guided surgery (FGS) is a promising surgical technique that gives surgeons a unique view of tissue that is used to guide their practice by delineating tissue types and diseased areas. As new fluorescent contrast agents are developed that have low fluorescent photon yields, it becomes increasingly important to develop computational models to allow FGS systems to maintain good video quality in real time environments. To further complicate this task, FGS has a difficult bias noise term from laser leakage light (LLL) that represents unfiltered excitation light that can be on the order of the fluorescent signal. Most conventional video denoising methods focus on zero mean noise, and non-causal processing, both of which are violated in FGS. Luckily in FGS, often a co-located reference video is also captured which we use to simulate the LLL and assist in the denoising processes. In this work, we propose an accurate noise simulation pipeline that includes LLL and propose three baseline deep learning based algorithms for FGS video denoising.