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首页> 外文期刊>IEEE Transactions on Medical Imaging >Vision-Based Surgical Field Defogging
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Vision-Based Surgical Field Defogging

机译:基于视觉的手术除雾

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摘要

Fogged surgical field visualization that is a common and potentially harmful problem can lead to inappropriate device use and incorrectly targeted tissue and increase surgical risks in endoscopic surgery. This paper aims to remove fog or smoke on endoscopic video sequences to augment and maintain a direct and clear visualization of the operating field. A new visibility-driven fusion defogging framework is proposed for surgical endoscopic video processing. This framework first recovers the visibility and enhances the contrast of hazy images. To address the color infidelity problem introduced by the visibility recovery, the luminances of the recovered and enhanced images are fused in the gradient domain, and the fused luminance is reconstructed by solving the Poisson equation in the frequency domain. The proposed method is evaluated on clinical videos that were collected from prostate cancer surgery. The experimental results demonstrate that the proposed framework defogs endoscopic images more robustly than currently available methods. Additionally, our method also provides an effective way to improve the visual quality of medical or high-dynamic range images.
机译:雾化的手术视野可视化是一个常见且潜在的危害问题,可能导致设备使用不当和目标组织不正确,在内窥镜手术中增加手术风险。本文旨在消除内窥镜视频序列上的雾气或烟雾,以增强并保持操作场的直接清晰的可视化。提出了一种新的可视化驱动融合除雾框架,用于外科内窥镜视频处理。该框架首先恢复可见性并增强模糊图像的对比度。为了解决由可见性恢复引入的颜色不保真度问题,在梯度域中融合恢复的图像和增强图像的亮度,并且通过在频域中求解泊松方程来重构融合的亮度。在从前列腺癌手术中收集的临床视频上对提出的方法进行了评估。实验结果表明,与目前可用的方法相比,所提出的框架对内窥镜图像的除雾能力更高。此外,我们的方法还提供了一种有效的方法来改善医学或高动态范围图像的视觉质量。

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