Video Denoising [Neurosurgery Wiki]

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====== Video Denoising ======

Video denoising refers to the process of **removing noise from sequences of image frames (videos)** to enhance visual clarity and data quality. In medical imaging, especially in fields like **light scattering imaging (LSI)** or **intraoperative video monitoring**, denoising is critical for **accurate interpretation and analysis**.

===== 1. Purpose =====

  * **Improve image quality**: Reduce random noise while preserving structural details and motion consistency.
  * **Enhance signal detection**: Boost signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for better visualization of tissues, tumors, or instruments.
  * **Facilitate automated analysis**: Clean data improves the performance of AI and machine learning algorithms.

===== 2. Techniques =====

  * **Traditional Filtering**:
    * Gaussian blur, median filters, and temporal averaging
    * Fast but often results in **loss of detail** and **motion blur**

  * **Model-Based Methods**:
    * Total variation minimization, block-matching (e.g., BM3D)
    * Require manual tuning and are **computationally intensive**

  * **Deep Learning-Based Methods**:
    * **FastDVDNet**, **DNNs**, **autoencoders**, or **GANs**
    * Exploit **temporal information** and **spatial correlations**
    * Can be trained in a **self-supervised (unsupervised)** manner, avoiding the need for clean ground-truth videos
    * More robust to complex, dynamic noise

===== 3. Applications in Neurosurgery =====

  * **Intraoperative Light Scattering Imaging (LSI)**:
    * Enhances visibility of brain structures, tumor margins, or blood flow dynamics
    * Reduces visual interference caused by tissue movement, blood, or lighting fluctuations

  * **Neuroendoscopy and Microscope Recordings**:
    * Improves clarity in endoscopic video streams
    * Enables high-quality recordings for surgical planning, teaching, or AI training

  * **Post-processing of Surgical Videos**:
    * Denoised videos can be used for case documentation, outcome analysis, or dataset generation

===== 4. Key Challenges =====

  * **Preserving fine details**: Especially important for small vessels or tumor boundaries
  * **Real-time processing**: Denoising must be fast enough for intraoperative use
  * **Generalizability**: Algorithms must adapt to different lighting conditions, tissue types, and imaging setups

===== 5. Recent Advances =====

  * Lin et al. (2025) 
((Lin M, Zheng Y, Yang L, Yan J, Ma X, Guo Y. [[Unsupervised Adaptive Deep Learning]] Framework for Video Denoising in Light Scattering Imaging. Anal Chem. 2025 May 22. doi: 10.1021/acs.analchem.4c06905. Epub ahead of print. PMID: 40405330.)) proposed an **unsupervised adaptive denoising framework** using FastDVDNet and noise distribution maps to enhance LSI videos in applications such as nanoparticle analysis and single-cell imaging:
    * Significant improvements in **SNR** and **CNR**
    * Enhanced reliability in particle sizing and cell classification

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Video denoising is a vital step in modern neurosurgical imaging workflows. By improving visual and analytical quality, it supports safer surgeries, better diagnostics, and the integration of advanced AI tools.